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Jones JR, Gottlieb D, McMurry AJ, Atreja A, Desai PM, Dixon BE, Payne PRO, Saldanha AJ, Shankar P, Solad Y, Wilcox AB, Ali MS, Kang E, Martin AM, Sprouse E, Taylor DE, Terry M, Ignatov V, Mandl KD. Real world performance of the 21st Century Cures Act population-level application programming interface. J Am Med Inform Assoc 2024; 31:1144-1150. [PMID: 38447593 PMCID: PMC11031206 DOI: 10.1093/jamia/ocae040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/02/2024] [Accepted: 02/19/2024] [Indexed: 03/08/2024] Open
Abstract
OBJECTIVE To evaluate the real-world performance of the SMART/HL7 Bulk Fast Health Interoperability Resources (FHIR) Access Application Programming Interface (API), developed to enable push button access to electronic health record data on large populations, and required under the 21st Century Cures Act Rule. MATERIALS AND METHODS We used an open-source Bulk FHIR Testing Suite at 5 healthcare sites from April to September 2023, including 4 hospitals using electronic health records (EHRs) certified for interoperability, and 1 Health Information Exchange (HIE) using a custom, standards-compliant API build. We measured export speeds, data sizes, and completeness across 6 types of FHIR. RESULTS Among the certified platforms, Oracle Cerner led in speed, managing 5-16 million resources at over 8000 resources/min. Three Epic sites exported a FHIR data subset, achieving 1-12 million resources at 1555-2500 resources/min. Notably, the HIE's custom API outperformed, generating over 141 million resources at 12 000 resources/min. DISCUSSION The HIE's custom API showcased superior performance, endorsing the effectiveness of SMART/HL7 Bulk FHIR in enabling large-scale data exchange while underlining the need for optimization in existing EHR platforms. Agility and scalability are essential for diverse health, research, and public health use cases. CONCLUSION To fully realize the interoperability goals of the 21st Century Cures Act, addressing the performance limitations of Bulk FHIR API is critical. It would be beneficial to include performance metrics in both certification and reporting processes.
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Affiliation(s)
- James R Jones
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States
| | - Daniel Gottlieb
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Andrew J McMurry
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, United States
| | - Ashish Atreja
- Department of Health Innovation Technology, UC Davis Health, Rancho Cardova, CA 95670, United States
| | - Pankaja M Desai
- Department of Internal Medicine, Rush University Medical Center, Chicago, IL 60612, United States
| | - Brian E Dixon
- Department of Health Policy and Management, Fairbanks School of Public Health, Indiana University, Indianapolis, IN 46202, United States
- Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN 46202, United States
| | - Philip R O Payne
- Department of Medicine, Washington University in St Louis, St Louis, MO 63110, United States
| | - Anil J Saldanha
- Department of Health Innovation, Rush University Medical Center, Chicago, IL 60612, United States
| | - Prabhu Shankar
- Department of Health Innovation Technology, UC Davis Health, Rancho Cardova, CA 95670, United States
- Department of Public Health Sciences, UC Davis Health, Davis, CA 95817, United States
| | - Yauheni Solad
- Department of Health Innovation Technology, UC Davis Health, Rancho Cardova, CA 95670, United States
| | - Adam B Wilcox
- Department of Medicine, Washington University in St Louis, St Louis, MO 63110, United States
| | - Momeena S Ali
- Department of Health Innovation Technology, UC Davis Health, Rancho Cardova, CA 95670, United States
| | - Eugene Kang
- Department of Health Innovation Technology, UC Davis Health, Rancho Cardova, CA 95670, United States
| | - Andrew M Martin
- Department of Technical Services, Regenstrief Institute, Indianapolis, IN 46202, United States
| | | | - David E Taylor
- Department of Technical Services, Regenstrief Institute, Indianapolis, IN 46202, United States
| | - Michael Terry
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States
| | - Vladimir Ignatov
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
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McMurry AJ, Zipursky AR, Geva A, Olson KL, Jones JR, Ignatov V, Miller TA, Mandl KD. Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort Study. J Med Internet Res 2024; 26:e53367. [PMID: 38573752 PMCID: PMC11027052 DOI: 10.2196/53367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/30/2023] [Accepted: 02/27/2024] [Indexed: 04/05/2024] Open
Abstract
BACKGROUND Real-time surveillance of emerging infectious diseases necessitates a dynamically evolving, computable case definition, which frequently incorporates symptom-related criteria. For symptom detection, both population health monitoring platforms and research initiatives primarily depend on structured data extracted from electronic health records. OBJECTIVE This study sought to validate and test an artificial intelligence (AI)-based natural language processing (NLP) pipeline for detecting COVID-19 symptoms from physician notes in pediatric patients. We specifically study patients presenting to the emergency department (ED) who can be sentinel cases in an outbreak. METHODS Subjects in this retrospective cohort study are patients who are 21 years of age and younger, who presented to a pediatric ED at a large academic children's hospital between March 1, 2020, and May 31, 2022. The ED notes for all patients were processed with an NLP pipeline tuned to detect the mention of 11 COVID-19 symptoms based on Centers for Disease Control and Prevention (CDC) criteria. For a gold standard, 3 subject matter experts labeled 226 ED notes and had strong agreement (F1-score=0.986; positive predictive value [PPV]=0.972; and sensitivity=1.0). F1-score, PPV, and sensitivity were used to compare the performance of both NLP and the International Classification of Diseases, 10th Revision (ICD-10) coding to the gold standard chart review. As a formative use case, variations in symptom patterns were measured across SARS-CoV-2 variant eras. RESULTS There were 85,678 ED encounters during the study period, including 4% (n=3420) with patients with COVID-19. NLP was more accurate at identifying encounters with patients that had any of the COVID-19 symptoms (F1-score=0.796) than ICD-10 codes (F1-score =0.451). NLP accuracy was higher for positive symptoms (sensitivity=0.930) than ICD-10 (sensitivity=0.300). However, ICD-10 accuracy was higher for negative symptoms (specificity=0.994) than NLP (specificity=0.917). Congestion or runny nose showed the highest accuracy difference (NLP: F1-score=0.828 and ICD-10: F1-score=0.042). For encounters with patients with COVID-19, prevalence estimates of each NLP symptom differed across variant eras. Patients with COVID-19 were more likely to have each NLP symptom detected than patients without this disease. Effect sizes (odds ratios) varied across pandemic eras. CONCLUSIONS This study establishes the value of AI-based NLP as a highly effective tool for real-time COVID-19 symptom detection in pediatric patients, outperforming traditional ICD-10 methods. It also reveals the evolving nature of symptom prevalence across different virus variants, underscoring the need for dynamic, technology-driven approaches in infectious disease surveillance.
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Affiliation(s)
- Andrew J McMurry
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States
| | - Amy R Zipursky
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States
- Division of Pediatric Emergency Medicine, Department of Pediatrics, The Hospital for Sick Children, Toronto, ON, Canada
| | - Alon Geva
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States
- Division of Critical Care Medicine, Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, MA, United States
- Department of Anaesthesia, Harvard Medical School, Boston, MA, United States
| | - Karen L Olson
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States
| | - James R Jones
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States
| | - Vladimir Ignatov
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States
| | - Timothy A Miller
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
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Phelan D, Gottlieb D, Mandel JC, Ignatov V, Jones J, Marquard B, Ellis A, Mandl KD. Beyond compliance with the 21st Century Cures Act Rule: a patient controlled electronic health information export application programming interface. J Am Med Inform Assoc 2024; 31:901-909. [PMID: 38287642 PMCID: PMC10990503 DOI: 10.1093/jamia/ocae013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 12/22/2023] [Accepted: 01/20/2024] [Indexed: 01/31/2024] Open
Abstract
OBJECTIVE The 21st Century Cures Act Final Rule requires that certified electronic health records (EHRs) be able to export a patient's full set of electronic health information (EHI). This requirement becomes more powerful if EHI exports use interoperable application programming interfaces (APIs). We sought to advance the ecosystem, instantiating policy desiderata in a working reference implementation based on a consensus design. MATERIALS AND METHODS We formulate a model for interoperable, patient-controlled, app-driven access to EHI exports in an open source reference implementation following the Argonaut FHIR Accelerator consensus implementation guide for EHI Export. RESULTS The reference implementation, which asynchronously provides EHI across an API, has three central components: a web application for patients to request EHI exports, an EHI server to respond to requests, and an administrative export management web application to manage requests. It leverages mandated SMART on FHIR/Bulk FHIR APIs. DISCUSSION A patient-controlled app enabling full EHI export from any EHR across an API could facilitate national-scale patient-directed information exchange. We hope releasing these tools sparks engagement from the health IT community to evolve the design, implement and test in real-world settings, and develop patient-facing apps. CONCLUSION To advance regulatory innovation, we formulate a model that builds on existing requirements under the Cures Act Rule and takes a step toward an interoperable, scalable approach, simplifying patient access to their own health data; supporting the sharing of clinical data for both improved patient care and medical research; and encouraging the growth of an ecosystem of third-party applications.
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Affiliation(s)
- Dylan Phelan
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States
| | - Daniel Gottlieb
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Joshua C Mandel
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Vladimir Ignatov
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States
| | - James Jones
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States
| | | | - Alyssa Ellis
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
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Voorhies K, Mohammed A, Chinthala L, Kong SW, Lee IH, Kho AT, McGeachie M, Mandl KD, Raby B, Hayes M, Davis RL, Wu AC, Lutz SM. GSDMB/ORMDL3 Rare/Common Variants Are Associated with Inhaled Corticosteroid Response among Children with Asthma. Genes (Basel) 2024; 15:420. [PMID: 38674355 DOI: 10.3390/genes15040420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 03/12/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
Inhaled corticosteroids (ICS) are efficacious in the treatment of asthma, which affects more than 300 million people in the world. While genome-wide association studies have identified genes involved in differential treatment responses to ICS in asthma, few studies have evaluated the effects of combined rare and common variants on ICS response among children with asthma. Among children with asthma treated with ICS with whole exome sequencing (WES) data in the PrecisionLink Biobank (91 White and 20 Black children), we examined the effect and contribution of rare and common variants with hospitalizations or emergency department visits. For 12 regions previously associated with asthma and ICS response (DPP10, FBXL7, NDFIP1, TBXT, GLCCI1, HDAC9, TBXAS1, STAT6, GSDMB/ORMDL3, CRHR1, GNGT2, FCER2), we used the combined sum test for the sequence kernel association test (SKAT) adjusting for age, sex, and BMI and stratified by race. Validation was conducted in the Biorepository and Integrative Genomics (BIG) Initiative (83 White and 134 Black children). Using a Bonferroni threshold for the 12 regions tested (i.e., 0.05/12 = 0.004), GSDMB/ORMDL3 was significantly associated with ICS response for the combined effect of rare and common variants (p-value = 0.003) among White children in the PrecisionLink Biobank and replicated in the BIG Initiative (p-value = 0.02). Using WES data, the combined effect of rare and common variants for GSDMB/ORMDL3 was associated with ICS response among asthmatic children in the PrecisionLink Biobank and replicated in the BIG Initiative. This proof-of-concept study demonstrates the power of biobanks of pediatric real-life populations in asthma genomic investigations.
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Affiliation(s)
- Kirsten Voorhies
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA
| | - Akram Mohammed
- Center in Biomedical Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Lokesh Chinthala
- Center in Biomedical Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Sek Won Kong
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02115, USA
| | - In-Hee Lee
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02115, USA
| | - Alvin T Kho
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02115, USA
| | - Michael McGeachie
- Channing Division for Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02115, USA
| | - Benjamin Raby
- Division of Pulmonary Medicine, Boston Children's Hospital, Boston, MA 02115, USA
| | - Melanie Hayes
- Center in Biomedical Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Robert L Davis
- Center in Biomedical Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Ann Chen Wu
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA
| | - Sharon M Lutz
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
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Mandl KD, Gottlieb D, Mandel JC. Integration of AI in healthcare requires an interoperable digital data ecosystem. Nat Med 2024; 30:631-634. [PMID: 38291298 DOI: 10.1038/s41591-023-02783-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Affiliation(s)
- Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Daniel Gottlieb
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Central Square Solutions, Cambridge, MA, USA
| | - Joshua C Mandel
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Microsoft Research, Redmond, WA, USA
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McMurry AJ, Gottlieb DI, Miller TA, Jones JR, Atreja A, Crago J, Desai PM, Dixon BE, Garber M, Ignatov V, Kirchner LA, Payne PRO, Saldanha AJ, Shankar PRV, Solad YV, Sprouse EA, Terry M, Wilcox AB, Mandl KD. Cumulus: A federated EHR-based learning system powered by FHIR and AI. medRxiv 2024:2024.02.02.24301940. [PMID: 38370642 PMCID: PMC10871375 DOI: 10.1101/2024.02.02.24301940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Objective To address challenges in large-scale electronic health record (EHR) data exchange, we sought to develop, deploy, and test an open source, cloud-hosted app 'listener' that accesses standardized data across the SMART/HL7 Bulk FHIR Access application programming interface (API). Methods We advance a model for scalable, federated, data sharing and learning. Cumulus software is designed to address key technology and policy desiderata including local utility, control, and administrative simplicity as well as privacy preservation during robust data sharing, and AI for processing unstructured text. Results Cumulus relies on containerized, cloud-hosted software, installed within a healthcare organization's security envelope. Cumulus accesses EHR data via the Bulk FHIR interface and streamlines automated processing and sharing. The modular design enables use of the latest AI and natural language processing tools and supports provider autonomy and administrative simplicity. In an initial test, Cumulus was deployed across five healthcare systems each partnered with public health. Cumulus output is patient counts which were aggregated into a table stratifying variables of interest to enable population health studies. All code is available open source. A policy stipulating that only aggregate data leave the institution greatly facilitated data sharing agreements. Discussion and Conclusion Cumulus addresses barriers to data sharing based on (1) federally required support for standard APIs (2), increasing use of cloud computing, and (3) advances in AI. There is potential for scalability to support learning across myriad network configurations and use cases.
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Affiliation(s)
- Andrew J. McMurry
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA
- Department of Pediatrics, Harvard Medical School, Boston, MA
| | - Daniel I. Gottlieb
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Timothy A. Miller
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA
- Department of Pediatrics, Harvard Medical School, Boston, MA
| | - James R. Jones
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA
| | - Ashish Atreja
- Department of Health Information Technology, UC Davis Health, Rancho Cordova, CA
| | - Jennifer Crago
- Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN
| | - Pankaja M. Desai
- Department of Internal Medicine, Rush University Medical Center, Chicago IL
| | - Brian E. Dixon
- Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN
- Department of Health Policy and Management, Fairbanks School of Public Health, Indiana University, Indianapolis, IN
| | - Matthew Garber
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA
| | - Vladimir Ignatov
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA
| | | | - Philip R. O. Payne
- Department of Medicine, Washington University in St. Louis, St. Louis, MO
| | - Anil J. Saldanha
- Department of Health Innovation, Rush University Medical Center, Chicago, IL
| | - Prabhu R. V. Shankar
- Department of Health Information Technology, UC Davis Health, Rancho Cordova, CA
- Department of Public Health Sciences, UC Davis Health, Davis , CA
| | - Yauheni V. Solad
- Department of Health Information Technology, UC Davis Health, Rancho Cordova, CA
| | | | - Michael Terry
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA
| | - Adam B. Wilcox
- Department of Medicine, Washington University in St. Louis, St. Louis, MO
| | - Kenneth D. Mandl
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
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Miller TA, McMurry AJ, Jones J, Gottlieb D, Mandl KD. The SMART Text2FHIR Pipeline. AMIA Annu Symp Proc 2024; 2023:514-520. [PMID: 38222416 PMCID: PMC10785871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Objective: To implement an open source, free, and easily deployable high throughput natural language processing module to extract concepts from clinician notes and map them to Fast Healthcare Interoperability Resources (FHIR). Materials and Methods: Using a popular open-source NLP tool (Apache cTAKES), we create FHIR resources that use modifier extensions to represent negation and NLP sourcing, and another extension to represent provenance of extracted concepts. Results: The SMART Text2FHIR Pipeline is an open-source tool, released through standard package managers, and publicly available container images that implement the mappings, enabling ready conversion of clinical text to FHIR. Discussion: With the increased data liquidity because of new interoperability regulations, NLP processes that can output FHIR can enable a common language for transporting structured and unstructured data. This framework can be valuable for critical public health or clinical research use cases. Conclusion: Future work should include mapping more categories of NLP-extracted information into FHIR resources and mappings from additional open-source NLP tools.
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Affiliation(s)
- Timothy A Miller
- Boston Children's Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Andrew J McMurry
- Boston Children's Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - James Jones
- Boston Children's Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Daniel Gottlieb
- Boston Children's Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Kenneth D Mandl
- Boston Children's Hospital, Boston, MA
- Harvard Medical School, Boston, MA
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Blease C, Torous J, McMillan B, Hägglund M, Mandl KD. Generative Language Models and Open Notes: Exploring the Promise and Limitations. JMIR Med Educ 2024; 10:e51183. [PMID: 38175688 PMCID: PMC10797501 DOI: 10.2196/51183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/30/2023] [Accepted: 11/10/2023] [Indexed: 01/05/2024]
Abstract
Patients' online record access (ORA) is growing worldwide. In some countries, including the United States and Sweden, access is advanced with patients obtaining rapid access to their full records on the web including laboratory and test results, lists of prescribed medications, vaccinations, and even the very narrative reports written by clinicians (the latter, commonly referred to as "open notes"). In the United States, patient's ORA is also available in a downloadable form for use with other apps. While survey studies have shown that some patients report many benefits from ORA, there remain challenges with implementation around writing clinical documentation that patients may now read. With ORA, the functionality of the record is evolving; it is no longer only an aide memoire for doctors but also a communication tool for patients. Studies suggest that clinicians are changing how they write documentation, inviting worries about accuracy and completeness. Other concerns include work burdens; while few objective studies have examined the impact of ORA on workload, some research suggests that clinicians are spending more time writing notes and answering queries related to patients' records. Aimed at addressing some of these concerns, clinician and patient education strategies have been proposed. In this viewpoint paper, we explore these approaches and suggest another longer-term strategy: the use of generative artificial intelligence (AI) to support clinicians in documenting narrative summaries that patients will find easier to understand. Applied to narrative clinical documentation, we suggest that such approaches may significantly help preserve the accuracy of notes, strengthen writing clarity and signals of empathy and patient-centered care, and serve as a buffer against documentation work burdens. However, we also consider the current risks associated with existing generative AI. We emphasize that for this innovation to play a key role in ORA, the cocreation of clinical notes will be imperative. We also caution that clinicians will need to be supported in how to work alongside generative AI to optimize its considerable potential.
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Affiliation(s)
- Charlotte Blease
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
- Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - John Torous
- Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Brian McMillan
- Centre for Primary Care and Health Services Research, University of Manchester, Manchester, United Kingdom
| | - Maria Hägglund
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
- Medtech Science & Innovation Centre, Uppsala University Hospital, Uppsala, Sweden
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
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Sathyanarayana A, El Atrache R, Jackson M, Cantley S, Reece L, Ufongene C, Loddenkemper T, Mandl KD, Bosl WJ. Measuring Real-Time Medication Effects From Electroencephalography. J Clin Neurophysiol 2024; 41:72-82. [PMID: 35583401 PMCID: PMC9669285 DOI: 10.1097/wnp.0000000000000946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
PURPOSE Evaluating the effects of antiseizure medication (ASM) on patients with epilepsy remains a slow and challenging process. Quantifiable noninvasive markers that are measurable in real-time and provide objective and useful information could guide clinical decision-making. We examined whether the effect of ASM on patients with epilepsy can be quantitatively measured in real-time from EEGs. METHODS This retrospective analysis was conducted on 67 patients in the long-term monitoring unit at Boston Children's Hospital. Two 30-second EEG segments were selected from each patient premedication and postmedication weaning for analysis. Nonlinear measures including entropy and recurrence quantitative analysis values were computed for each segment and compared before and after medication weaning. RESULTS Our study found that ASM effects on the brain were measurable by nonlinear recurrence quantitative analysis on EEGs. Highly significant differences ( P < 1e-11) were found in several nonlinear measures within the seizure zone in response to antiseizure medication. Moreover, the size of the medication effect correlated with a patient's seizure frequency, seizure localization, number of medications, and reported seizure frequency reduction on medication. CONCLUSIONS Our findings show the promise of digital biomarkers to measure medication effects and epileptogenicity.
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Affiliation(s)
- Aarti Sathyanarayana
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, U.S.A.;
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, U.S.A.;
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, U.S.A.;
| | - Rima El Atrache
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, U.S.A.; and
| | - Michele Jackson
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, U.S.A.; and
| | - Sarah Cantley
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, U.S.A.; and
| | - Latania Reece
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, U.S.A.; and
| | - Claire Ufongene
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, U.S.A.; and
| | - Tobias Loddenkemper
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, U.S.A.;
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, U.S.A.; and
| | - Kenneth D. Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, U.S.A.;
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, U.S.A.;
| | - William J. Bosl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, U.S.A.;
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, U.S.A.;
- Department of Health Professions, University of San Francisco, San Francisco, California, U.S.A
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10
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Zipursky AR, Olson KL, Bode L, Geva A, Jones J, Mandl KD, McMurry A. Emergency department visits and boarding for pediatric patients with suicidality before and during the COVID-19 pandemic. PLoS One 2023; 18:e0286035. [PMID: 37910582 PMCID: PMC10619773 DOI: 10.1371/journal.pone.0286035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 08/15/2023] [Indexed: 11/03/2023] Open
Abstract
OBJECTIVE To quantify the increase in pediatric patients presenting to the emergency department with suicidality before and during the COVID-19 pandemic, and the subsequent impact on emergency department length of stay and boarding. METHODS This retrospective cohort study from June 1, 2016, to October 31, 2022, identified patients ages 6 to 21 presenting to the emergency department at a pediatric academic medical center with suicidality using ICD-10 codes. Number of emergency department encounters for suicidality, demographic characteristics of patients with suicidality, and emergency department length of stay were compared before and during the COVID-19 pandemic. Unobserved components models were used to describe monthly counts of emergency department encounters for suicidality. RESULTS There were 179,736 patient encounters to the emergency department during the study period, 6,215 (3.5%) for suicidality. There were, on average, more encounters for suicidality each month during the COVID-19 pandemic than before the COVID-19 pandemic. A time series unobserved components model demonstrated a temporary drop of 32.7 encounters for suicidality in April and May of 2020 (p<0.001), followed by a sustained increase of 31.2 encounters starting in July 2020 (p = 0.003). The average length of stay for patients that boarded in the emergency department with a diagnosis of suicidality was 37.4 hours longer during the COVID-19 pandemic compared to before the COVID-19 pandemic (p<0.001). CONCLUSIONS The number of encounters for suicidality among pediatric patients and the emergency department length of stay for psychiatry boarders has increased during the COVID-19 pandemic. There is a need for acute care mental health services and solutions to emergency department capacity issues.
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Affiliation(s)
- Amy R. Zipursky
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, United States of America
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Emergency Medicine, Boston Children’s Hospital, Boston, Massachusetts, United States of America
| | - Karen L. Olson
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, United States of America
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Louisa Bode
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, United States of America
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, Lower Saxony, Germany
| | - Alon Geva
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, United States of America
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Critical Care Medicine, Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children’s Hospital, Boston, Massachusetts, United States of America
| | - James Jones
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, United States of America
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Kenneth D. Mandl
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, United States of America
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Andrew McMurry
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, United States of America
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America
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11
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Jones JR, Gottlieb D, McMurry AJ, Atreja A, Desai PM, Dixon BE, Payne PRO, Saldanha AJ, Shankar P, Solad Y, Wilcox AB, Ali MS, Kang E, Martin AM, Sprouse E, Taylor D, Terry M, Ignatov V, Mandl KD. Real World Performance of the 21st Century Cures Act Population Level Application Programming Interface. medRxiv 2023:2023.10.05.23296560. [PMID: 37873390 PMCID: PMC10593080 DOI: 10.1101/2023.10.05.23296560] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Objective To evaluate the real-world performance in delivering patient data on populations, of the SMART/HL7 Bulk FHIR Access API, required in Electronic Health Records (EHRs) under the 21st Century Cures Act Rule. Materials and Methods We used an open-source Bulk FHIR Testing Suite at five healthcare sites from April to September 2023, including four hospitals using EHRs certified for interoperability, and one Health Information Exchange (HIE) using a custom, standards-compliant API build. We measured export speeds, data sizes, and completeness across six types of FHIR resources. Results Among the certified platforms, Oracle Cerner led in speed, managing 5-16 million resources at over 8,000 resources/min. Three Epic sites exported a FHIR data subset, achieving 1-12 million resources at 1,555-2,500 resources/min. Notably, the HIE's custom API outperformed, generating over 141 million resources at 12,000 resources/min. Discussion The HIE's custom API showcased superior performance, endorsing the effectiveness of SMART/HL7 Bulk FHIR in enabling large-scale data exchange while underlining the need for optimization in existing EHR platforms. Agility and scalability are essential for diverse health, research, and public health use cases. Conclusion To fully realize the interoperability goals of the 21st Century Cures Act, addressing the performance limitations of Bulk FHIR API is critical. It would be beneficial to include performance metrics in both certification and reporting processes.
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Affiliation(s)
- James R Jones
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
| | - Daniel Gottlieb
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Andrew J McMurry
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
- Department of Pediatrics, Harvard Medical School, Boston, MA
| | - Ashish Atreja
- Department of Health Innovation Technology, UC Davis Health, Rancho Cardova, CA
| | - Pankaja M Desai
- Department of Internal Medicine, Rush University Medical Center, Chicago IL
| | - Brian E Dixon
- Department of Health Policy & Management, Fairbanks School of Public Health, Indiana University, Indianapolis, IN
- Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN
| | - Philip R O Payne
- Department of Medicine, Washington University in St Louis, St Louis, MO
| | - Anil J Saldanha
- Department of Health Innovation, Rush University Medical Center, Chicago IL
| | - Prabhu Shankar
- Department of Health Innovation Technology, UC Davis Health, Rancho Cardova, CA
- Department of Public Health Sciences, UC Davis Health, Davis, CA
| | - Yauheni Solad
- Department of Health Innovation Technology, UC Davis Health, Rancho Cardova, CA
| | - Adam B Wilcox
- Department of Medicine, Washington University in St Louis, St Louis, MO
| | - Momeena S Ali
- Department of Health Innovation Technology, UC Davis Health, Rancho Cardova, CA
| | - Eugene Kang
- Department of Health Innovation Technology, UC Davis Health, Rancho Cardova, CA
| | - Andrew M Martin
- Department of Technical Services, Regenstrief Institute, Indianapolis, IN
| | | | - David Taylor
- Department of Technical Services, Regenstrief Institute, Indianapolis, IN
| | - Michael Terry
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
| | - Vladimir Ignatov
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
- Department of Pediatrics, Harvard Medical School, Boston, MA
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12
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Xiong X, Sweet SM, Liu M, Hong C, Bonzel CL, Panickan VA, Zhou D, Wang L, Costa L, Ho YL, Geva A, Mandl KD, Cheng S, Xia Z, Cho K, Gaziano JM, Liao KP, Cai T, Cai T. Knowledge-Driven Online Multimodal Automated Phenotyping System. medRxiv 2023:2023.09.29.23296239. [PMID: 37873131 PMCID: PMC10593060 DOI: 10.1101/2023.09.29.23296239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Though electronic health record (EHR) systems are a rich repository of clinical information with large potential, the use of EHR-based phenotyping algorithms is often hindered by inaccurate diagnostic records, the presence of many irrelevant features, and the requirement for a human-labeled training set. In this paper, we describe a knowledge-driven online multimodal automated phenotyping (KOMAP) system that i) generates a list of informative features by an online narrative and codified feature search engine (ONCE) and ii) enables the training of a multimodal phenotyping algorithm based on summary data. Powered by composite knowledge from multiple EHR sources, online article corpora, and a large language model, features selected by ONCE show high concordance with the state-of-the-art AI models (GPT4 and ChatGPT) and encourage large-scale phenotyping by providing a smaller but highly relevant feature set. Validation of the KOMAP system across four healthcare centers suggests that it can generate efficient phenotyping algorithms with robust performance. Compared to other methods requiring patient-level inputs and gold-standard labels, the fully online KOMAP provides a significant opportunity to enable multi-center collaboration.
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13
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Wang L, Zipursky AR, Geva A, McMurry AJ, Mandl KD, Miller TA. A computable case definition for patients with SARS-CoV2 testing that occurred outside the hospital. JAMIA Open 2023; 6:ooad047. [PMID: 37425487 PMCID: PMC10322650 DOI: 10.1093/jamiaopen/ooad047] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 06/13/2023] [Accepted: 06/30/2023] [Indexed: 07/11/2023] Open
Abstract
Objective To identify a cohort of COVID-19 cases, including when evidence of virus positivity was only mentioned in the clinical text, not in structured laboratory data in the electronic health record (EHR). Materials and Methods Statistical classifiers were trained on feature representations derived from unstructured text in patient EHRs. We used a proxy dataset of patients with COVID-19 polymerase chain reaction (PCR) tests for training. We selected a model based on performance on our proxy dataset and applied it to instances without COVID-19 PCR tests. A physician reviewed a sample of these instances to validate the classifier. Results On the test split of the proxy dataset, our best classifier obtained 0.56 F1, 0.6 precision, and 0.52 recall scores for SARS-CoV2 positive cases. In an expert validation, the classifier correctly identified 97.6% (81/84) as COVID-19 positive and 97.8% (91/93) as not SARS-CoV2 positive. The classifier labeled an additional 960 cases as not having SARS-CoV2 lab tests in hospital, and only 177 of those cases had the ICD-10 code for COVID-19. Discussion Proxy dataset performance may be worse because these instances sometimes include discussion of pending lab tests. The most predictive features are meaningful and interpretable. The type of external test that was performed is rarely mentioned. Conclusion COVID-19 cases that had testing done outside of the hospital can be reliably detected from the text in EHRs. Training on a proxy dataset was a suitable method for developing a highly performant classifier without labor-intensive labeling efforts.
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Affiliation(s)
- Lijing Wang
- Department of Data Science, New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Amy R Zipursky
- Computational Health Informatics Program and Department of Emergency Medicine, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Alon Geva
- Computational Health Informatics Program and Division of Critical Care Medicine, Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Andrew J McMurry
- Computational Health Informatics Program, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Timothy A Miller
- Computational Health Informatics Program, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
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14
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Sperotto F, Gutiérrez-Sacristán A, Makwana S, Li X, Rofeberg VN, Cai T, Bourgeois FT, Omenn GS, Hanauer DA, Sáez C, Bonzel CL, Bucholz E, Dionne A, Elias MD, García-Barrio N, González TG, Issitt RW, Kernan KF, Laird-Gion J, Maidlow SE, Mandl KD, Ahooyi TM, Moraleda C, Morris M, Moshal KL, Pedrera-Jiménez M, Shah MA, South AM, Spiridou A, Taylor DM, Verdy G, Visweswaran S, Wang X, Xia Z, Zachariasse JM, Newburger JW, Avillach P. Clinical phenotypes and outcomes in children with multisystem inflammatory syndrome across SARS-CoV-2 variant eras: a multinational study from the 4CE consortium. EClinicalMedicine 2023; 64:102212. [PMID: 37745025 PMCID: PMC10511777 DOI: 10.1016/j.eclinm.2023.102212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 08/22/2023] [Accepted: 08/29/2023] [Indexed: 09/26/2023] Open
Abstract
Background Multisystem inflammatory syndrome in children (MIS-C) is a severe complication of SARS-CoV-2 infection. It remains unclear how MIS-C phenotypes vary across SARS-CoV-2 variants. We aimed to investigate clinical characteristics and outcomes of MIS-C across SARS-CoV-2 eras. Methods We performed a multicentre observational retrospective study including seven paediatric hospitals in four countries (France, Spain, U.K., and U.S.). All consecutive confirmed patients with MIS-C hospitalised between February 1st, 2020, and May 31st, 2022, were included. Electronic Health Records (EHR) data were used to calculate pooled risk differences (RD) and effect sizes (ES) at site level, using Alpha as reference. Meta-analysis was used to pool data across sites. Findings Of 598 patients with MIS-C (61% male, 39% female; mean age 9.7 years [SD 4.5]), 383 (64%) were admitted in the Alpha era, 111 (19%) in the Delta era, and 104 (17%) in the Omicron era. Compared with patients admitted in the Alpha era, those admitted in the Delta era were younger (ES -1.18 years [95% CI -2.05, -0.32]), had fewer respiratory symptoms (RD -0.15 [95% CI -0.33, -0.04]), less frequent non-cardiogenic shock or systemic inflammatory response syndrome (SIRS) (RD -0.35 [95% CI -0.64, -0.07]), lower lymphocyte count (ES -0.16 × 109/uL [95% CI -0.30, -0.01]), lower C-reactive protein (ES -28.5 mg/L [95% CI -46.3, -10.7]), and lower troponin (ES -0.14 ng/mL [95% CI -0.26, -0.03]). Patients admitted in the Omicron versus Alpha eras were younger (ES -1.6 years [95% CI -2.5, -0.8]), had less frequent SIRS (RD -0.18 [95% CI -0.30, -0.05]), lower lymphocyte count (ES -0.39 × 109/uL [95% CI -0.52, -0.25]), lower troponin (ES -0.16 ng/mL [95% CI -0.30, -0.01]) and less frequently received anticoagulation therapy (RD -0.19 [95% CI -0.37, -0.04]). Length of hospitalization was shorter in the Delta versus Alpha eras (-1.3 days [95% CI -2.3, -0.4]). Interpretation Our study suggested that MIS-C clinical phenotypes varied across SARS-CoV-2 eras, with patients in Delta and Omicron eras being younger and less sick. EHR data can be effectively leveraged to identify rare complications of pandemic diseases and their variation over time. Funding None.
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Affiliation(s)
- Francesca Sperotto
- Department of Cardiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Alba Gutiérrez-Sacristán
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
| | - Simran Makwana
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
| | - Xiudi Li
- Department of Biostatistics, Harvard School of Public Health, 677 Huntington Ave, Boston, MA 02115, United States
| | - Valerie N. Rofeberg
- Department of Cardiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
| | - Florence T. Bourgeois
- Department of Pediatrics, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Gilbert S. Omenn
- Dept of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, & Public Health, University of Michigan, 2017 Palmer Commons, Ann Arbor, MI 48109-2218, United States
| | - David A. Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, 100-107 NCRC, 2800 Plymouth Road, Ann Arbor, MI 48109, United States
| | - Carlos Sáez
- Biomedical Data Science Lab, Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politécnica de Valéncia, Camino de Vera S/N, Valencia 46022, Spain
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
| | - Emily Bucholz
- Department of Cardiology, Children's Hospital Colorado, University of Colorado Anschutz, 13123 E. 16th Ave, Aurora, CO 80045, United States
| | - Audrey Dionne
- Department of Cardiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Matthew D. Elias
- Division of Cardiology, The Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA 19104, United States
| | - Noelia García-Barrio
- Health Informatics, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, Madrid 28041, Spain
| | - Tomás González González
- Health Informatics, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, Madrid 28041, Spain
| | - Richard W. Issitt
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, Great Ormond Street, London WC1N 3JH, United Kingdom
| | - Kate F. Kernan
- Department of Critical Care Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15213, United States
| | - Jessica Laird-Gion
- Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Sarah E. Maidlow
- Michigan Institute for Clinical and Health Research (MICHR) Informatics, University of Michigan, NCRC Bldg 400, 2800 Plymouth Road, Ann Arbor, MI 48109, United States
| | - Kenneth D. Mandl
- Computational Health Informatics Program, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA 02115, United States
| | - Taha Mohseni Ahooyi
- Department of Biomedical Health Informatics, The Children's Hospital of Philadelphia, Roberts Building, 734 Schuylkill Ave, Philadelphia, PA 19146, United States
| | - Cinta Moraleda
- Pediatric Infectious Disease Department, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, Madrid 28041, Spain
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA 15206, United States
| | - Karyn L. Moshal
- Department of Infectious Diseases, Great Ormond Street Hospital for Children, Great Ormond Street, London WC1N 3JH, United Kingdom
| | - Miguel Pedrera-Jiménez
- Health Informatics, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, Madrid 28041, Spain
| | - Mohsin A. Shah
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, DRIVE, 40 Bernard St, London WC1N 1LE, United Kingdom
| | - Andrew M. South
- Department of Pediatrics-Section of Nephrology, Brenner Children’s, Wake Forest University School of Medicine, Medical Center Boulevard, Winston Salem, NC 27157, United States
| | - Anastasia Spiridou
- Data Research, Innovation and Virtual Environments, Great Ormond Street Hospital for Children, DRIVE, 40 Bernard St, London WC1N 1LE, United Kingdom
| | - Deanne M. Taylor
- Department of Biomedical Health Informatics, The Children's Hospital of Philadelphia, United States
- The Department of Pediatrics, University of Pennsylvania Perelman Medical School, 3601 Civic Center Blvd, 6032 Colket, Philadelphia, PA 19104, United States
| | - Guillaume Verdy
- IAM Unit, Bordeaux University Hospital, Place amélie rabat Léon, Bordeaux 33076, France
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA 15206, United States
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, 3501 5th Avenue, BST-3 Suite 7014, Pittsburgh, PA 15260, United States
| | - Joany M. Zachariasse
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
| | - Jane W. Newburger
- Department of Cardiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
- Computational Health Informatics Program, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA 02115, United States
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15
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Tseng YJ, Olson KL, Bloch D, Mandl KD. Engaging a national-scale cohort of smart thermometer users in participatory surveillance. NPJ Digit Med 2023; 6:175. [PMID: 37730764 PMCID: PMC10511532 DOI: 10.1038/s41746-023-00917-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 09/04/2023] [Indexed: 09/22/2023] Open
Abstract
Participatory surveillance systems crowdsource individual reports to rapidly assess population health phenomena. The value of these systems increases when more people join and persistently contribute. We examine the level of and factors associated with engagement in participatory surveillance among a retrospective, national-scale cohort of individuals using smartphone-connected thermometers with a companion app that allows them to report demographic and symptom information. Between January 1, 2020 and October 29, 2022, 1,325,845 participants took 20,617,435 temperature readings, yielding 3,529,377 episodes of consecutive readings. There were 1,735,805 (49.2%) episodes with self-reported symptoms (including reports of no symptoms). Compared to before the pandemic, participants were more likely to report their symptoms during pandemic waves, especially after the winter wave began (September 13, 2020) (OR across pandemic periods range from 3.0 to 4.0). Further, symptoms were more likely to be reported during febrile episodes (OR = 2.6, 95% CI = 2.6-2.6), and for new participants, during their first episode (OR = 2.4, 95% CI = 2.4-2.5). Compared with participants aged 50-65 years old, participants over 65 years were less likely to report their symptoms (OR = 0.3, 95% CI = 0.3-0.3). Participants in a household with both adults and children (OR = 1.6 [1.6-1.7]) were more likely to report symptoms. We find that the use of smart thermometers with companion apps facilitates the collection of data on a large, national scale, and provides real time insight into transmissible disease phenomena. Nearly half of individuals using these devices are willing to report their symptoms after taking their temperature, although participation varies among individuals and over pandemic stages.
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Affiliation(s)
- Yi-Ju Tseng
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Karen L Olson
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | | | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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16
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Lee IH, Walker DI, Lin Y, Smith MR, Mandl KD, Jones DP, Kong SW. Association between Neuroligin-1 polymorphism and plasma glutamine levels in individuals with autism spectrum disorder. EBioMedicine 2023; 95:104746. [PMID: 37544204 PMCID: PMC10427990 DOI: 10.1016/j.ebiom.2023.104746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 07/21/2023] [Accepted: 07/24/2023] [Indexed: 08/08/2023] Open
Abstract
BACKGROUND Unravelling the relationships between candidate genes and autism spectrum disorder (ASD) phenotypes remains an outstanding challenge. Endophenotypes, defined as inheritable, measurable quantitative traits, might provide intermediary links between genetic risk factors and multifaceted ASD phenotypes. In this study, we sought to determine whether plasma metabolite levels could serve as endophenotypes in individuals with ASD and their family members. METHODS We employed an untargeted, high-resolution metabolomics platform to analyse 14,342 features across 1099 plasma samples. These samples were collected from probands and their family members participating in the Autism Genetic Resource Exchange (AGRE) (N = 658), compared with neurotypical individuals enrolled in the PrecisionLink Health Discovery (PLHD) program at Boston Children's Hospital (N = 441). We conducted a metabolite quantitative trait loci (mQTL) analysis using whole-genome genotyping data from each cohort in AGRE and PLHD, aiming to prioritize significant mQTL and metabolite pairs that were exclusively observed in AGRE. FINDINGS Within the AGRE group, we identified 54 significant associations between genotypes and metabolite levels (P < 5.27 × 10-11), 44 of which were not observed in the PLHD group. Plasma glutamine levels were found to be associated with variants in the NLGN1 gene, a gene that encodes post-synaptic cell-adhesion molecules in excitatory neurons. This association was not detected in the PLHD group. Notably, a significant negative correlation between plasma glutamine and glutamate levels was observed in the AGRE group, but not in the PLHD group. Furthermore, plasma glutamine levels showed a negative correlation with the severity of restrictive and repetitive behaviours (RRB) in ASD, although no direct association was observed between RRB severity and the NLGN1 genotype. INTERPRETATION Our findings suggest that plasma glutamine levels could potentially serve as an endophenotype, thus establishing a link between the genetic risk associated with NLGN1 and the severity of RRB in ASD. This identified association could facilitate the development of novel therapeutic targets, assist in selecting specific cohorts for clinical trials, and provide insights into target symptoms for future ASD treatment strategies. FUNDING This work was supported by the National Institute of Health (grant numbers: R01MH107205, U01TR002623, R24OD024622, OT2OD032720, and R01NS129188) and the PrecisionLink Biobank for Health Discovery at Boston Children's Hospital.
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Affiliation(s)
- In-Hee Lee
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, 02215, USA
| | - Douglas I Walker
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, USA
| | - Yufei Lin
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, 02215, USA
| | - Matthew Ryan Smith
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Emory University, Atlanta, GA, 30602, USA; Atlanta Department of Veterans Affairs Medical Center, Decatur, GA, 30033, USA
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, 02215, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA
| | - Dean P Jones
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Emory University, Atlanta, GA, 30602, USA
| | - Sek Won Kong
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, 02215, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA.
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Tseng YJ, Olson KL, Bloch D, Mandl KD. Smart Thermometer-Based Participatory Surveillance to Discern the Role of Children in Household Viral Transmission During the COVID-19 Pandemic. JAMA Netw Open 2023; 6:e2316190. [PMID: 37261828 PMCID: PMC10236238 DOI: 10.1001/jamanetworkopen.2023.16190] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 04/18/2023] [Indexed: 06/02/2023] Open
Abstract
Importance Children's role in spreading virus during the COVID-19 pandemic is yet to be elucidated, and measuring household transmission traditionally requires contact tracing. Objective To discern children's role in household viral transmission during the pandemic when enveloped viruses were at historic lows and the predominance of viral illnesses were attributed to COVID-19. Design, Setting, and Participants This cohort study of a voluntary US cohort tracked data from participatory surveillance using commercially available thermometers with a companion smartphone app from October 2019 to October 2022. Eligible participants were individuals with temperature measurements in households with multiple members between October 2019 and October 2022 who opted into data sharing. Main Outcomes and Measures Proportion of household transmissions with a pediatric index case and changes in transmissions during school breaks were assessed using app and thermometer data. Results A total of 862 577 individuals from 320 073 households with multiple participants (462 000 female [53.6%] and 463 368 adults [53.7%]) were included. The number of febrile episodes forecast new COVID-19 cases. Within-household transmission was inferred in 54 506 (15.4%) febrile episodes and increased from the fourth pandemic period, March to July 2021 (3263 of 32 294 [10.1%]) to the Omicron BA.1/BA.2 wave (16 516 of 94 316 [17.5%]; P < .001). Among 38 787 transmissions in 166 170 households with adults and children, a median (IQR) 70.4% (61.4%-77.6%) had a pediatric index case; proportions fluctuated weekly from 36.9% to 84.6%. A pediatric index case was 0.6 to 0.8 times less frequent during typical school breaks. The winter break decrease was from 68.4% (95% CI, 57.1%-77.8%) to 41.7% (95% CI, 34.3%-49.5%) at the end of 2020 (P < .001). At the beginning of 2022, it dropped from 80.3% (95% CI, 75.1%-84.6%) to 54.5% (95% CI, 51.3%-57.7%) (P < .001). During summer breaks, rates dropped from 81.4% (95% CI, 74.0%-87.1%) to 62.5% (95% CI, 56.3%-68.3%) by August 2021 (P = .02) and from 83.8% (95% CI, 79.2%-87.5) to 62.8% (95% CI, 57.1%-68.1%) by July 2022 (P < .001). These patterns persisted over 2 school years. Conclusions and Relevance In this cohort study using participatory surveillance to measure within-household transmission at a national scale, we discerned an important role for children in the spread of viral infection within households during the COVID-19 pandemic, heightened when schools were in session, supporting a role for school attendance in COVID-19 spread.
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Affiliation(s)
- Yi-Ju Tseng
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Karen L. Olson
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
| | | | - Kenneth D. Mandl
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
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18
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Miller TA, McMurry AJ, Jones J, Gottlieb D, Mandl KD. The SMART Text2FHIR Pipeline. medRxiv 2023:2023.03.21.23287499. [PMID: 37034815 PMCID: PMC10081439 DOI: 10.1101/2023.03.21.23287499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/22/2023]
Abstract
Objective To implement an open source, free, and easily deployable high throughput natural language processing module to extract concepts from clinician notes and map them to Fast Healthcare Interoperability Resources (FHIR). Materials and Methods Using a popular open-source NLP tool (Apache cTAKES), we create FHIR resources that use modifier extensions to represent negation and NLP sourcing, and another extension to represent provenance of extracted concepts. Results The SMART Text2FHIR Pipeline is an open-source tool, released through standard package managers, and publicly available container images that implement the mappings, enabling ready conversion of clinical text to FHIR. Discussion With the increased data liquidity because of new interoperability regulations, NLP processes that can output FHIR can enable a common language for transporting structured and unstructured data. This framework can be valuable for critical public health or clinical research use cases. Conclusion Future work should include mapping more categories of NLP-extracted information into FHIR resources and mappings from additional open-source NLP tools.
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Affiliation(s)
- Timothy A Miller
- Computational Health Informatics Program, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Andrew J McMurry
- Computational Health Informatics Program, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - James Jones
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
| | - Daniel Gottlieb
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Department of Pediatrics, Department of Biomedical Informatics, Harvard Medical School, 401 Park Drive, Landmark Center, 5th Floor East, Boston, MA 02215, U.S.A
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19
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Blease C, Kharko A, Bernstein M, Bradley C, Houston M, Walsh I, D Mandl K. Computerization of the Work of General Practitioners: Mixed Methods Survey of Final-Year Medical Students in Ireland. JMIR Med Educ 2023; 9:e42639. [PMID: 36939809 PMCID: PMC10131917 DOI: 10.2196/42639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 12/14/2022] [Accepted: 01/15/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND The potential for digital health technologies, including machine learning (ML)-enabled tools, to disrupt the medical profession is the subject of ongoing debate within biomedical informatics. OBJECTIVE We aimed to describe the opinions of final-year medical students in Ireland regarding the potential of future technology to replace or work alongside general practitioners (GPs) in performing key tasks. METHODS Between March 2019 and April 2020, using a convenience sample, we conducted a mixed methods paper-based survey of final-year medical students. The survey was administered at 4 out of 7 medical schools in Ireland across each of the 4 provinces in the country. Quantitative data were analyzed using descriptive statistics and nonparametric tests. We used thematic content analysis to investigate free-text responses. RESULTS In total, 43.1% (252/585) of the final-year students at 3 medical schools responded, and data collection at 1 medical school was terminated due to disruptions associated with the COVID-19 pandemic. With regard to forecasting the potential impact of artificial intelligence (AI)/ML on primary care 25 years from now, around half (127/246, 51.6%) of all surveyed students believed the work of GPs will change minimally or not at all. Notably, students who did not intend to enter primary care predicted that AI/ML will have a great impact on the work of GPs. CONCLUSIONS We caution that without a firm curricular foundation on advances in AI/ML, students may rely on extreme perspectives involving self-preserving optimism biases that demote the impact of advances in technology on primary care on the one hand and technohype on the other. Ultimately, these biases may lead to negative consequences in health care. Improvements in medical education could help prepare tomorrow's doctors to optimize and lead the ethical and evidence-based implementation of AI/ML-enabled tools in medicine for enhancing the care of tomorrow's patients.
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Affiliation(s)
- Charlotte Blease
- General Medicine and Primary Care, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Anna Kharko
- Healthcare Sciences and e-Health, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
- School of Psychology, University of Plymouth, Plymouth, United Kingdom
| | - Michael Bernstein
- Department of Behavioral and Social Sciences, School of Public Health, Brown University, Providence, RI, United States
- Department of Diagnostic Imaging, Warren Alpert Medical School, Brown University, Providence, RI, United States
| | - Colin Bradley
- School of Medicine, University College Cork, Cork, Ireland
| | - Muiris Houston
- School of Medicine, National University of Ireland Galway, Galway, Ireland
- School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Ian Walsh
- Dentistry and Biomedical Sciences, School of Medicine, Queen's University, Belfast, Ireland
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States
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Wang L, Zipursky A, Geva A, McMurry AJ, Mandl KD, Miller TA. A computable phenotype for patients with SARS-CoV2 testing that occurred outside the hospital. medRxiv 2023:2023.01.19.23284738. [PMID: 36711461 PMCID: PMC9882620 DOI: 10.1101/2023.01.19.23284738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Objective To identify a cohort of COVID-19 cases, including when evidence of virus positivity was only mentioned in the clinical text, not in structured laboratory data in the electronic health record (EHR). Materials and Methods Statistical classifiers were trained on feature representations derived from unstructured text in patient electronic health records (EHRs). We used a proxy dataset of patients with COVID-19 polymerase chain reaction (PCR) tests for training. We selected a model based on performance on our proxy dataset and applied it to instances without COVID-19 PCR tests. A physician reviewed a sample of these instances to validate the classifier. Results On the test split of the proxy dataset, our best classifier obtained 0.56 F1, 0.6 precision, and 0.52 recall scores for SARS-CoV2 positive cases. In an expert validation, the classifier correctly identified 90.8% (79/87) as COVID-19 positive and 97.8% (91/93) as not SARS-CoV2 positive. The classifier identified an additional 960 positive cases that did not have SARS-CoV2 lab tests in hospital, and only 177 of those cases had the ICD-10 code for COVID-19. Discussion Proxy dataset performance may be worse because these instances sometimes include discussion of pending lab tests. The most predictive features are meaningful and interpretable. The type of external test that was performed is rarely mentioned. Conclusion COVID-19 cases that had testing done outside of the hospital can be reliably detected from the text in EHRs. Training on a proxy dataset was a suitable method for developing a highly performant classifier without labor intensive labeling efforts.
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21
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Lee IH, Smith MR, Yazdani A, Sandhu S, Walker DI, Mandl KD, Jones DP, Kong SW. Comprehensive characterization of putative genetic influences on plasma metabolome in a pediatric cohort. Hum Genomics 2022; 16:67. [PMID: 36482414 PMCID: PMC9730628 DOI: 10.1186/s40246-022-00440-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 11/22/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The human exposome is composed of diverse metabolites and small chemical compounds originated from endogenous and exogenous sources, respectively. Genetic and environmental factors influence metabolite levels, while the extent of genetic contributions across metabolic pathways is not yet known. Untargeted profiling of human metabolome using high-resolution mass spectrometry (HRMS) combined with genome-wide genotyping allows comprehensive identification of genetically influenced metabolites. As such previous studies of adults discovered and replicated genotype-metabotype associations. However, these associations have not been characterized in children. RESULTS We conducted the largest genome by metabolome-wide association study to date of children (N = 441) using 619,688 common genetic variants and 14,342 features measured by HRMS. Narrow-sense heritability (h2) estimates of plasma metabolite concentrations using genomic relatedness matrix restricted maximum likelihood (GREML) method showed a bimodal distribution with high h2 (> 0.8) for 15.9% of features and low h2 (< 0.2) for most of features (62.0%). The features with high h2 were enriched for amino acid and nucleic acid metabolism, while carbohydrate and lipid concentrations showed low h2. For each feature, a metabolite quantitative trait loci (mQTL) analysis was performed to identify genetic variants that were potentially associated with plasma levels. Fifty-four associations among 29 features and 43 genetic variants were identified at a genome-wide significance threshold p < 3.5 × 10-12 (= 5 × 10-8/14,342 features). Previously reported associations such as UGT1A1 and bilirubin; PYROXD2 and methyl lysine; and ACADS and butyrylcarnitine were successfully replicated in our pediatric cohort. We found potential candidates for novel associations including CSMD1 and a monostearyl alcohol triglyceride (m/z 781.7483, retention time (RT) 89.3 s); CALN1 and Tridecanol (m/z 283.2741, RT 27.6). A gene-level enrichment analysis using MAGMA revealed highly interconnected modules for dADP biosynthesis, sterol synthesis, and long-chain fatty acid transport in the gene-feature network. CONCLUSION Comprehensive profiling of plasma metabolome across age groups combined with genome-wide genotyping revealed a wide range of genetic influence on diverse chemical species and metabolic pathways. The developmental trajectory of a biological system is shaped by gene-environment interaction especially in early life. Therefore, continuous efforts on generating metabolomics data in diverse human tissue types across age groups are required to understand gene-environment interaction toward healthy aging trajectories.
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Affiliation(s)
- In-Hee Lee
- grid.2515.30000 0004 0378 8438Computational Health Informatics Program, Boston Children’s Hospital, 401 Park Drive, Boston, MA 02215 USA
| | - Matthew Ryan Smith
- grid.189967.80000 0001 0941 6502Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Emory University, Atlanta, GA 30602 USA ,grid.414026.50000 0004 0419 4084Atlanta Department of Veterans Affairs Medical Center, Decatur, GA 30033 USA
| | - Azam Yazdani
- grid.38142.3c000000041936754XCenter of Perioperative Genetics and Genomics, Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 USA
| | - Sumiti Sandhu
- grid.2515.30000 0004 0378 8438Computational Health Informatics Program, Boston Children’s Hospital, 401 Park Drive, Boston, MA 02215 USA
| | - Douglas I. Walker
- grid.59734.3c0000 0001 0670 2351Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
| | - Kenneth D. Mandl
- grid.2515.30000 0004 0378 8438Computational Health Informatics Program, Boston Children’s Hospital, 401 Park Drive, Boston, MA 02215 USA ,grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA 02115 USA ,grid.38142.3c000000041936754XDepartment of Pediatrics, Harvard Medical School, Boston, MA 02115 USA
| | - Dean P. Jones
- grid.189967.80000 0001 0941 6502Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Emory University, Atlanta, GA 30602 USA
| | - Sek Won Kong
- grid.2515.30000 0004 0378 8438Computational Health Informatics Program, Boston Children’s Hospital, 401 Park Drive, Boston, MA 02215 USA ,grid.38142.3c000000041936754XDepartment of Pediatrics, Harvard Medical School, Boston, MA 02115 USA
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22
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Mandel JC, Pollak JP, Mandl KD. The Patient Role in a Federal National-Scale Health Information Exchange. J Med Internet Res 2022; 24:e41750. [DOI: 10.2196/41750] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/26/2022] [Accepted: 10/07/2022] [Indexed: 11/06/2022] Open
Abstract
The federal Trusted Exchange Framework and Common Agreement (TEFCA) aims to reduce fragmentation of patient records by expanding query-based health information exchange with nationwide connectivity for diverse purposes. TEFCA provides a common agreement and security framework allowing clinicians, and possibly insurance company staff, public health officials, and other authorized users, to query for health information about hundreds of millions of patients. TEFCA presents an opportunity to weave information exchange into the fabric of our national health information economy. We define 3 principles to promote patient autonomy and control within TEFCA: (1) patients can query for data about themselves, (2) patients can know when their data are queried and shared, and (3) patients can configure what is shared about them. We believe TEFCA should address these principles by the time it launches. While health information exchange already occurs on a large scale today, the launch of TEFCA introduces a major, new, and cohesive component of 21st-century US health care information infrastructure. We strongly advocate for a substantive role for the patient in TEFCA, one that will be a model for other systems and policies.
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23
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Adler-Milstein J, Aggarwal N, Ahmed M, Castner J, Evans BJ, Gonzalez AA, James CA, Lin S, Mandl KD, Matheny ME, Sendak MP, Shachar C, Williams A. Meeting the Moment: Addressing Barriers and Facilitating Clinical Adoption of Artificial Intelligence in Medical Diagnosis. NAM Perspect 2022; 2022:202209c. [PMID: 36713769 PMCID: PMC9875857 DOI: 10.31478/202209c] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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Abstract
This cross-sectional study examines the pattern of suicides from 2015 through 2020 among youth aged 10 to 19 years in 14 US states.
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Affiliation(s)
- Marie-Laure Charpignon
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge
| | | | - Saahil Sundaresan
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
| | - Anika Puri
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
| | - Jay Chandra
- Harvard College, Harvard University, Cambridge, Massachusetts
| | - Kenneth D. Mandl
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
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25
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Weber GM, Hong C, Xia Z, Palmer NP, Avillach P, L'Yi S, Keller MS, Murphy SN, Gutiérrez-Sacristán A, Bonzel CL, Serret-Larmande A, Neuraz A, Omenn GS, Visweswaran S, Klann JG, South AM, Loh NHW, Cannataro M, Beaulieu-Jones BK, Bellazzi R, Agapito G, Alessiani M, Aronow BJ, Bell DS, Benoit V, Bourgeois FT, Chiovato L, Cho K, Dagliati A, DuVall SL, Barrio NG, Hanauer DA, Ho YL, Holmes JH, Issitt RW, Liu M, Luo Y, Lynch KE, Maidlow SE, Malovini A, Mandl KD, Mao C, Matheny ME, Moore JH, Morris JS, Morris M, Mowery DL, Ngiam KY, Patel LP, Pedrera-Jimenez M, Ramoni RB, Schriver ER, Schubert P, Balazote PS, Spiridou A, Tan ALM, Tan BWL, Tibollo V, Torti C, Trecarichi EM, Wang X, Kohane IS, Cai T, Brat GA. International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality. NPJ Digit Med 2022; 5:74. [PMID: 35697747 PMCID: PMC9192605 DOI: 10.1038/s41746-022-00601-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 03/11/2022] [Indexed: 01/08/2023] Open
Abstract
Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.
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Affiliation(s)
- Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
- Department of Biostatistics and Bioinformatics, Duke University, Durham, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, USA
| | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Sehi L'Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Mark S Keller
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, USA
| | | | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Arnaud Serret-Larmande
- Department of biomedical informatics, Hôpital Européen Georges Pompidou, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Antoine Neuraz
- Department of biomedical informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris (APHP), University of Paris, Paris, France
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, USA
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
| | - Jeffrey G Klann
- Department of Medicine, Massachusetts General Hospital, Boston, USA
| | - Andrew M South
- Department of Pediatrics-Section of Nephrology, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, USA
| | - Ne Hooi Will Loh
- Department of Anaesthesia, National University Health System, Singapore, Singapore, Singapore
| | - Mario Cannataro
- Department of Medical and Surgical Sciences, Data Analytics Research Center, University Magna Graecia of Catanzaro, Italy, Catanzaro, Italy
| | | | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy, Pavia, Italy
| | - Giuseppe Agapito
- Department of Legal, Economic and Social Sciences, University Magna Graecia of Catanzaro, Italy, Catanzaro, Italy
| | - Mario Alessiani
- Department of Surgery, ASST Pavia, Lombardia Region Health System, Pavia, Italy
| | - Bruce J Aronow
- Departments of Biomedical Informatics, Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, USA
| | - Douglas S Bell
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Vincent Benoit
- IT department, Innovation & Data, APHP Greater Paris University Hospital, Paris, France
| | | | - Luca Chiovato
- Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, USA
| | - Arianna Dagliati
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Italy, Pavia, Italy
| | - Scott L DuVall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, USA
| | | | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan, Ann Arbor, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, USA
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Richard W Issitt
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, UK, London, UK
| | - Molei Liu
- Department of Biostatistics, Harvard School of Public Health, Boston, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, USA
| | - Kristine E Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, USA
| | - Sarah E Maidlow
- Michigan Institute for Clinical and Health Research, University of Michigan, Ann Arbor, USA
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, USA
| | - Chengsheng Mao
- Department of Preventive Medicine, Northwestern University, Chicago, USA
| | - Michael E Matheny
- VA Informatics and Computing Infrastructure, Tennessee Valley Healthcare System Veterans Affairs Medical Center, Nashville, USA
| | - Jason H Moore
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Jeffrey S Morris
- Department of Biostatistics, Epidemiology, and Biostatistics, University of Pennysylvania Perelman School of Medicine, Philadelphia, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Kee Yuan Ngiam
- Department of Biomedical informatics, WiSDM, National University Health Systems Singapore, Singapore, Singapore
| | - Lav P Patel
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, USA
| | | | - Rachel B Ramoni
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, USA
| | - Emily R Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, USA
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, USA
| | | | - Anastasia Spiridou
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, UK, London, UK
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Byorn W L Tan
- Department of Medicine, National University Hospital, Singapore, Singapore, Singapore
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Carlo Torti
- Department of Medical and Surgical Sciences, Infectious and Tropical Disease Unit, University Magna Graecia of Catanzaro, Italy, Catanzaro, Italy
| | - Enrico M Trecarichi
- Department of Medical and Surgical Sciences, Infectious and Tropical Disease Unit, University Magna Graecia of Catanzaro, Italy, Catanzaro, Italy
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA.
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA.
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26
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Abstract
[Figure: see text].
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Affiliation(s)
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Timo Minssen
- Centre for Advanced Studies in Biomedical Innovation Law (CeBIL), University of Copenhagen, Denmark
| | | | - Urs Gasser
- School of Social Sciences and Technology, Technical University of Munich, Munich, Germany
| | - Isaac Kohane
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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Blease C, Kharko A, Bernstein M, Bradley C, Houston M, Walsh I, Hägglund M, DesRoches C, Mandl KD. Machine learning in medical education: a survey of the experiences and opinions of medical students in Ireland. BMJ Health Care Inform 2022; 29:bmjhci-2021-100480. [PMID: 35105606 PMCID: PMC8808371 DOI: 10.1136/bmjhci-2021-100480] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Affiliation(s)
- Charlotte Blease
- Division of General Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Anna Kharko
- Faculty of Health and Human Sciences, University of Plymouth, Plymouth, UK.,Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Michael Bernstein
- School of Public Health, Brown University, Providence, Rhode Island, USA
| | - Colin Bradley
- School of Medicine, University College Cork, Cork, Ireland
| | - Muiris Houston
- School of Medicine, National University of Ireland Galway, Galway, Ireland.,School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Ian Walsh
- School of Medicine, Dentistry and Biomedical Sciences, Queen's University, Belfast, Belfast, Northern Ireland, UK
| | - Maria Hägglund
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Catherine DesRoches
- Division of General Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Kenneth D Mandl
- Harvard Medical School, Boston, Massachusetts, USA.,Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
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Geva A, Patel MM, Newhams MM, Young CC, Son MBF, Kong M, Maddux AB, Hall MW, Riggs BJ, Singh AR, Giuliano JS, Hobbs CV, Loftis LL, McLaughlin GE, Schwartz SP, Schuster JE, Babbitt CJ, Halasa NB, Gertz SJ, Doymaz S, Hume JR, Bradford TT, Irby K, Carroll CL, McGuire JK, Tarquinio KM, Rowan CM, Mack EH, Cvijanovich NZ, Fitzgerald JC, Spinella PC, Staat MA, Clouser KN, Soma VL, Dapul H, Maamari M, Bowens C, Havlin KM, Mourani PM, Heidemann SM, Horwitz SM, Feldstein LR, Tenforde MW, Newburger JW, Mandl KD, Randolph AG. Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents. EClinicalMedicine 2021; 40:101112. [PMID: 34485878 PMCID: PMC8405351 DOI: 10.1016/j.eclinm.2021.101112] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 08/11/2021] [Accepted: 08/13/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Multisystem inflammatory syndrome in children (MIS-C) consensus criteria were designed for maximal sensitivity and therefore capture patients with acute COVID-19 pneumonia. METHODS We performed unsupervised clustering on data from 1,526 patients (684 labeled MIS-C by clinicians) <21 years old hospitalized with COVID-19-related illness admitted between 15 March 2020 and 31 December 2020. We compared prevalence of assigned MIS-C labels and clinical features among clusters, followed by recursive feature elimination to identify characteristics of potentially misclassified MIS-C-labeled patients. FINDINGS Of 94 clinical features tested, 46 were retained for clustering. Cluster 1 patients (N = 498; 92% labeled MIS-C) were mostly previously healthy (71%), with mean age 7·2 ± 0·4 years, predominant cardiovascular (77%) and/or mucocutaneous (82%) involvement, high inflammatory biomarkers, and mostly SARS-CoV-2 PCR negative (60%). Cluster 2 patients (N = 445; 27% labeled MIS-C) frequently had pre-existing conditions (79%, with 39% respiratory), were similarly 7·4 ± 2·1 years old, and commonly had chest radiograph infiltrates (79%) and positive PCR testing (90%). Cluster 3 patients (N = 583; 19% labeled MIS-C) were younger (2·8 ± 2·0 y), PCR positive (86%), with less inflammation. Radiographic findings of pulmonary infiltrates and positive SARS-CoV-2 PCR accurately distinguished cluster 2 MIS-C labeled patients from cluster 1 patients. INTERPRETATION Using a data driven, unsupervised approach, we identified features that cluster patients into a group with high likelihood of having MIS-C. Other features identified a cluster of patients more likely to have acute severe COVID-19 pulmonary disease, and patients in this cluster labeled by clinicians as MIS-C may be misclassified. These data driven phenotypes may help refine the diagnosis of MIS-C.
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Affiliation(s)
- Alon Geva
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston,
These authors contributed equally to this work. A complete list of members and affiliations is provided in the Supplementary Appendix. MA, USA - Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
- Department of Anaesthesia, Harvard Medical School, Boston, MA, USA
| | - Manish M. Patel
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, GA, USA
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Margaret M. Newhams
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston,
These authors contributed equally to this work. A complete list of members and affiliations is provided in the Supplementary Appendix. MA, USA
| | - Cameron C. Young
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston,
These authors contributed equally to this work. A complete list of members and affiliations is provided in the Supplementary Appendix. MA, USA
| | - Mary Beth F. Son
- Department of Pediatrics, Division of Immunology, Boston Children's Hospital, Boston, MA, USA
| | - Michele Kong
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Aline B. Maddux
- Department of Pediatrics, Section of Critical Care Medicine, University of Colorado School of Medicine and Children's Hospital Colorado, Aurora, CO, USA
| | - Mark W. Hall
- Division of Critical Care Medicine, Department of Pediatrics, Nationwide Children's Hospital, Columbus, OH, USA
| | - Becky J. Riggs
- Department of Anesthesiology and Critical Care Medicine; Division of Pediatric Anesthesiology & Critical Care Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Aalok R. Singh
- Pediatric Critical Care Division, Maria Fareri Children's Hospital at Westchester Medical Center and New York Medical College, Valhalla, NY, USA
| | - John S. Giuliano
- Department of Pediatrics, Division of Critical Care, Yale University School of Medicine, New Haven, CT, USA
| | - Charlotte V. Hobbs
- Department of Pediatrics, Division of Disease; Microbiology; University of Mississippi Medical Center, Jackson, MS, USA
| | - Laura L. Loftis
- Section of Critical Care Medicine, Department of Pediatrics, Texas Children's Hospital, Houston, TX, USA
| | - Gwenn E. McLaughlin
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Stephanie P. Schwartz
- Department of Pediatrics, University of North Carolina at Chapel Hill Children's Hospital, Chapel Hill, NC, USA
| | - Jennifer E. Schuster
- Division of Pediatric Infectious Disease, Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO, USA
| | | | - Natasha B. Halasa
- Division of Pediatric Infectious Diseases, Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shira J. Gertz
- Division of Pediatric Critical Care, Department of Pediatrics, Saint Barnabas Medical Center, Livingston, NJ, USA
| | - Sule Doymaz
- Division of Pediatric Critical Care, Department of Pediatrics, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Janet R. Hume
- Division of Pediatric Critical Care, University of Minnesota Masonic Children's Hospital, Minneapolis, MN, USA
| | - Tamara T. Bradford
- Department of Pediatrics, Division of Cardiology, Louisiana State University Health Sciences Center and Children's Hospital of New Orleans, New Orleans, LA, USA
| | - Katherine Irby
- Section of Pediatric Critical Care, Department of Pediatrics, Arkansas Children's Hospital, Little Rock, AR, USA
| | | | - John K. McGuire
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, Seattle Children's Hospital and the University of Washington, Seattle, WA, USA
| | - Keiko M. Tarquinio
- Division of Critical Care Medicine, Department of Pediatrics, Emory University School of Medicine, Children's Healthcare of Atlanta, Atlanta, GA, USA
| | - Courtney M. Rowan
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, Indiana University School of Medicine, Riley Hospital for Children, Indianapolis, IN, USA
| | - Elizabeth H. Mack
- Division of Pediatric Critical Care Medicine, Medical University of South Carolina, Charleston, SC, USA
| | | | - Julie C. Fitzgerald
- Division of Critical Care, Department of Anesthesiology and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Philip C. Spinella
- Division of Critical Care, Department of Pediatrics, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Mary A. Staat
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Katharine N. Clouser
- Department of Pediatrics, Hackensack Meridian School of Medicine, Hackensack, NJ, USA
| | - Vijaya L. Soma
- Department of Pediatrics, Division of Infectious Diseases, New York University Grossman School of Medicine and Hassenfeld Children's Hospital, New York, NY, USA
| | - Heda Dapul
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, New York University Grossman School of Medicine and Hassenfeld Children's Hospital, New York, NY, USA
| | - Mia Maamari
- Department of Pediatrics, Division of Critical Care Medicine, University of Texas Southwestern, Children's Health Medical Center Dallas, TX, USA
| | - Cindy Bowens
- Department of Pediatrics, Division of Pediatric Critical Care Medicine, University of Louisville, and Norton Children's Hospital, Louisville, KY, USA
| | - Kevin M. Havlin
- Department of Pediatrics, Division of Pediatric Critical Care Medicine, Central Michigan University, Detroit, MI, USA
| | - Peter M. Mourani
- Department of Pediatrics, Section of Critical Care Medicine, University of Colorado School of Medicine and Children's Hospital Colorado, Aurora, CO, USA
| | - Sabrina M. Heidemann
- Department of Pediatrics, Division of Pediatric Critical Care Medicine, Central Michigan University, Detroit, MI, USA
| | - Steven M. Horwitz
- Department of Pediatrics, Division of Pediatric Critical Care Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Leora R. Feldstein
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, GA, USA
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Mark W. Tenforde
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, GA, USA
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Jane W. Newburger
- Department of Cardiology, Boston Children's Hospital, Boston, MA, USA
| | - Kenneth D. Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
- Departments of Biomedical Informatics and Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Adrienne G. Randolph
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston,
These authors contributed equally to this work. A complete list of members and affiliations is provided in the Supplementary Appendix. MA, USA - Departments of Anaesthesia and Pediatrics, Harvard Medical School, Boston, MA, USA
- Corresponding author at: Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Bader 634, 300 Longwood Avenue, Boston, MA 02115, USA.
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Feldman TC, Dienstag JL, Mandl KD, Tseng YJ. Machine-learning-based predictions of direct-acting antiviral therapy duration for patients with hepatitis C. Int J Med Inform 2021; 154:104562. [PMID: 34482150 DOI: 10.1016/j.ijmedinf.2021.104562] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 08/15/2021] [Accepted: 08/16/2021] [Indexed: 02/09/2023]
Abstract
INTRODUCTION Hepatitis C, which affects 71 million persons worldwide, is the most common blood-borne pathogen in the United States. Chronic infections can be treated effectively thanks to the availability of modern direct-acting antiviral (DAA) therapies. Real-world data on the duration of DAA therapy, which can be used to optimize and guide the course of therapy, may also be useful in determining quality of life enhancements based upon total required supply of medication and long-term improvements to quality of life. We developed a machine learning model to identify patient characteristics associated with prolonged DAA treatment duration. METHODS A nationwide U.S. commercial managed care plan with claims data that covers about 60 million beneficiaries from 2009 to 2019 were used in the retrospective study. We examined differences in age, gender, and multiple comorbidities among patients treated with different durations of DAA treatment. We also examined the performance of machine learning models for predicting a prolonged course of DAA based on the area under the receiver operating characteristic curve (AUC). RESULTS We identified 3943 cases with hepatitis C who received sofosbuvir/ledipasvir as the first course of DAA and were eligible for the study. Patients receiving prolonged treatment (n = 240, 6.1%) were more likely to have compensated cirrhosis, decompensated cirrhosis, and other comorbidities (P < 0.001). For distinguishing patients who received prolonged DAA treatment for hepatitis C from patients received standard treatment, the optimal predictive model, constructed with XGBoost, had an AUC of 0.745 ± 0.031 (P < 0.001). CONCLUSIONS The risk of antiviral resistance and the cost of DAA are strong motivators to ensure that first-round DAA therapy is effective. For the dominant DAA treatment during the course of this analysis, we present a model that identifies factors already captured in established guidelines and adds to those age, comorbidity burden, and type 2 diabetes status; patient characteristics that are predictive of extended treatment.
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Affiliation(s)
- Theodore C Feldman
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; VA Boston Healthcare System, Boston, MA, USA
| | - Jules L Dienstag
- Gastrointestinal Unit, Massachusetts Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Yi-Ju Tseng
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA; Department of Information Management, National Central University, Taoyuan, Taiwan.
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30
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Santoro JD, Kerr LM, Codden R, Casper TC, Greenberg BM, Waubant E, Kong SW, Mandl KD, Gorman MP. Increased Prevalence of Familial Autoimmune Disease in Children With Opsoclonus-Myoclonus Syndrome. Neurol Neuroimmunol Neuroinflamm 2021; 8:8/6/e1079. [PMID: 34475249 PMCID: PMC8422990 DOI: 10.1212/nxi.0000000000001079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 06/08/2021] [Indexed: 11/25/2022]
Abstract
Background and Objectives Opsoclonus-myoclonus syndrome (OMS) is a rare autoimmune disorder associated with neuroblastoma in children, although idiopathic and postinfectious etiologies are present in children and adults. Small cohort studies in homogenous populations have revealed elevated rates of autoimmunity in family members of patients with OMS, although no differentiation between paraneoplastic and nonparaneoplastic forms has been performed. The objective of this study was to investigate the prevalence of autoimmune disease in first-degree relatives of pediatric patients with paraneoplastic and nonparaneoplastic OMS. Methods A single-center cohort study of consecutively evaluated children with OMS was performed. Parents of patients were prospectively administered surveys on familial autoimmune disease. Rates of autoimmune disease in first-degree relatives of pediatric patients with OMS were compared using Fisher exact t test and χ2 analysis: (1) between those with and without a paraneoplastic cause and (2) between healthy and disease (pediatric multiple sclerosis [MS]) controls from the United States Pediatric MS Network. Results Thirty-five patients (18 paraneoplastic, median age at onset 19.0 months; 17 idiopathic, median age at onset 25.0 months) and 68 first-degree relatives (median age 41.9 years) were enrolled. One patient developed systemic lupus erythematosus 7 years after OMS onset. Paraneoplastic OMS was associated with a 50% rate of autoimmune disease in a first-degree relative compared with 29% in idiopathic OMS (p = 0.31). The rate of first-degree relative autoimmune disease per OMS case (14/35, 40%) was higher than healthy controls (86/709, 12%; p < 0.001) and children with pediatric MS (101/495, 20%; p = 0.007). Discussion In a cohort of pediatric patients with OMS, there were elevated rates of first-degree relative autoimmune disease, with no difference in rates observed between paraneoplastic and idiopathic etiologies, suggesting an autoimmune genetic contribution to the development of OMS in children.
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Affiliation(s)
- Jonathan D Santoro
- From the Department of Neurology (J.D.S.), Massachusetts General Hospital, Boston; Department of Neurology (J.D.S., J.M.K., M.P.G.), Boston Children's Hospital, MA; Harvard Medical School (J.D.S., K.D.M.), Boston, MA; Division of Neurology (J.D.S.), Department of Pediatrics, Children's Hospital of Los Angeles, CA; Department of Neurology (J.D.S.), Keck School of Medicine at the University of Southern California, Los Angeles; Department of Pediatrics (R.C., T.C.C.), University of Utah School of Medicine, Salt Lake City; Department of Neurology and Neurotherapeutics (B.M.G.), The University of Texas Southwestern Medical Center at Dallas, TX; UCSF Weill Institute for Neurosciences (E.W.), Department of Neurology, University of California San Francisco, CA; Computational Health Informatics Program (S.W.K., K.D.M.), Boston Children's Hospital, MA; and Department of Pediatrics (S.W.K., K.D.M.), Boston Children's Hospital, MA.
| | - Lauren M Kerr
- From the Department of Neurology (J.D.S.), Massachusetts General Hospital, Boston; Department of Neurology (J.D.S., J.M.K., M.P.G.), Boston Children's Hospital, MA; Harvard Medical School (J.D.S., K.D.M.), Boston, MA; Division of Neurology (J.D.S.), Department of Pediatrics, Children's Hospital of Los Angeles, CA; Department of Neurology (J.D.S.), Keck School of Medicine at the University of Southern California, Los Angeles; Department of Pediatrics (R.C., T.C.C.), University of Utah School of Medicine, Salt Lake City; Department of Neurology and Neurotherapeutics (B.M.G.), The University of Texas Southwestern Medical Center at Dallas, TX; UCSF Weill Institute for Neurosciences (E.W.), Department of Neurology, University of California San Francisco, CA; Computational Health Informatics Program (S.W.K., K.D.M.), Boston Children's Hospital, MA; and Department of Pediatrics (S.W.K., K.D.M.), Boston Children's Hospital, MA
| | - Rachel Codden
- From the Department of Neurology (J.D.S.), Massachusetts General Hospital, Boston; Department of Neurology (J.D.S., J.M.K., M.P.G.), Boston Children's Hospital, MA; Harvard Medical School (J.D.S., K.D.M.), Boston, MA; Division of Neurology (J.D.S.), Department of Pediatrics, Children's Hospital of Los Angeles, CA; Department of Neurology (J.D.S.), Keck School of Medicine at the University of Southern California, Los Angeles; Department of Pediatrics (R.C., T.C.C.), University of Utah School of Medicine, Salt Lake City; Department of Neurology and Neurotherapeutics (B.M.G.), The University of Texas Southwestern Medical Center at Dallas, TX; UCSF Weill Institute for Neurosciences (E.W.), Department of Neurology, University of California San Francisco, CA; Computational Health Informatics Program (S.W.K., K.D.M.), Boston Children's Hospital, MA; and Department of Pediatrics (S.W.K., K.D.M.), Boston Children's Hospital, MA
| | - Theron Charles Casper
- From the Department of Neurology (J.D.S.), Massachusetts General Hospital, Boston; Department of Neurology (J.D.S., J.M.K., M.P.G.), Boston Children's Hospital, MA; Harvard Medical School (J.D.S., K.D.M.), Boston, MA; Division of Neurology (J.D.S.), Department of Pediatrics, Children's Hospital of Los Angeles, CA; Department of Neurology (J.D.S.), Keck School of Medicine at the University of Southern California, Los Angeles; Department of Pediatrics (R.C., T.C.C.), University of Utah School of Medicine, Salt Lake City; Department of Neurology and Neurotherapeutics (B.M.G.), The University of Texas Southwestern Medical Center at Dallas, TX; UCSF Weill Institute for Neurosciences (E.W.), Department of Neurology, University of California San Francisco, CA; Computational Health Informatics Program (S.W.K., K.D.M.), Boston Children's Hospital, MA; and Department of Pediatrics (S.W.K., K.D.M.), Boston Children's Hospital, MA
| | - Benjamin M Greenberg
- From the Department of Neurology (J.D.S.), Massachusetts General Hospital, Boston; Department of Neurology (J.D.S., J.M.K., M.P.G.), Boston Children's Hospital, MA; Harvard Medical School (J.D.S., K.D.M.), Boston, MA; Division of Neurology (J.D.S.), Department of Pediatrics, Children's Hospital of Los Angeles, CA; Department of Neurology (J.D.S.), Keck School of Medicine at the University of Southern California, Los Angeles; Department of Pediatrics (R.C., T.C.C.), University of Utah School of Medicine, Salt Lake City; Department of Neurology and Neurotherapeutics (B.M.G.), The University of Texas Southwestern Medical Center at Dallas, TX; UCSF Weill Institute for Neurosciences (E.W.), Department of Neurology, University of California San Francisco, CA; Computational Health Informatics Program (S.W.K., K.D.M.), Boston Children's Hospital, MA; and Department of Pediatrics (S.W.K., K.D.M.), Boston Children's Hospital, MA
| | - Emmanuelle Waubant
- From the Department of Neurology (J.D.S.), Massachusetts General Hospital, Boston; Department of Neurology (J.D.S., J.M.K., M.P.G.), Boston Children's Hospital, MA; Harvard Medical School (J.D.S., K.D.M.), Boston, MA; Division of Neurology (J.D.S.), Department of Pediatrics, Children's Hospital of Los Angeles, CA; Department of Neurology (J.D.S.), Keck School of Medicine at the University of Southern California, Los Angeles; Department of Pediatrics (R.C., T.C.C.), University of Utah School of Medicine, Salt Lake City; Department of Neurology and Neurotherapeutics (B.M.G.), The University of Texas Southwestern Medical Center at Dallas, TX; UCSF Weill Institute for Neurosciences (E.W.), Department of Neurology, University of California San Francisco, CA; Computational Health Informatics Program (S.W.K., K.D.M.), Boston Children's Hospital, MA; and Department of Pediatrics (S.W.K., K.D.M.), Boston Children's Hospital, MA
| | - Sek Won Kong
- From the Department of Neurology (J.D.S.), Massachusetts General Hospital, Boston; Department of Neurology (J.D.S., J.M.K., M.P.G.), Boston Children's Hospital, MA; Harvard Medical School (J.D.S., K.D.M.), Boston, MA; Division of Neurology (J.D.S.), Department of Pediatrics, Children's Hospital of Los Angeles, CA; Department of Neurology (J.D.S.), Keck School of Medicine at the University of Southern California, Los Angeles; Department of Pediatrics (R.C., T.C.C.), University of Utah School of Medicine, Salt Lake City; Department of Neurology and Neurotherapeutics (B.M.G.), The University of Texas Southwestern Medical Center at Dallas, TX; UCSF Weill Institute for Neurosciences (E.W.), Department of Neurology, University of California San Francisco, CA; Computational Health Informatics Program (S.W.K., K.D.M.), Boston Children's Hospital, MA; and Department of Pediatrics (S.W.K., K.D.M.), Boston Children's Hospital, MA
| | - Kenneth D Mandl
- From the Department of Neurology (J.D.S.), Massachusetts General Hospital, Boston; Department of Neurology (J.D.S., J.M.K., M.P.G.), Boston Children's Hospital, MA; Harvard Medical School (J.D.S., K.D.M.), Boston, MA; Division of Neurology (J.D.S.), Department of Pediatrics, Children's Hospital of Los Angeles, CA; Department of Neurology (J.D.S.), Keck School of Medicine at the University of Southern California, Los Angeles; Department of Pediatrics (R.C., T.C.C.), University of Utah School of Medicine, Salt Lake City; Department of Neurology and Neurotherapeutics (B.M.G.), The University of Texas Southwestern Medical Center at Dallas, TX; UCSF Weill Institute for Neurosciences (E.W.), Department of Neurology, University of California San Francisco, CA; Computational Health Informatics Program (S.W.K., K.D.M.), Boston Children's Hospital, MA; and Department of Pediatrics (S.W.K., K.D.M.), Boston Children's Hospital, MA
| | - Mark P Gorman
- From the Department of Neurology (J.D.S.), Massachusetts General Hospital, Boston; Department of Neurology (J.D.S., J.M.K., M.P.G.), Boston Children's Hospital, MA; Harvard Medical School (J.D.S., K.D.M.), Boston, MA; Division of Neurology (J.D.S.), Department of Pediatrics, Children's Hospital of Los Angeles, CA; Department of Neurology (J.D.S.), Keck School of Medicine at the University of Southern California, Los Angeles; Department of Pediatrics (R.C., T.C.C.), University of Utah School of Medicine, Salt Lake City; Department of Neurology and Neurotherapeutics (B.M.G.), The University of Texas Southwestern Medical Center at Dallas, TX; UCSF Weill Institute for Neurosciences (E.W.), Department of Neurology, University of California San Francisco, CA; Computational Health Informatics Program (S.W.K., K.D.M.), Boston Children's Hospital, MA; and Department of Pediatrics (S.W.K., K.D.M.), Boston Children's Hospital, MA
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Klann JG, Estiri H, Weber GM, Moal B, Avillach P, Hong C, Tan ALM, Beaulieu-Jones BK, Castro V, Maulhardt T, Geva A, Malovini A, South AM, Visweswaran S, Morris M, Samayamuthu MJ, Omenn GS, Ngiam KY, Mandl KD, Boeker M, Olson KL, Mowery DL, Follett RW, Hanauer DA, Bellazzi R, Moore JH, Loh NHW, Bell DS, Wagholikar KB, Chiovato L, Tibollo V, Rieg S, Li ALLJ, Jouhet V, Schriver E, Xia Z, Hutch M, Luo Y, Kohane IS, Brat GA, Murphy SN. Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data. J Am Med Inform Assoc 2021; 28:1411-1420. [PMID: 33566082 PMCID: PMC7928835 DOI: 10.1093/jamia/ocab018] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 01/14/2021] [Accepted: 01/29/2021] [Indexed: 12/21/2022] Open
Abstract
OBJECTIVE The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity. MATERIALS AND METHODS Twelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site. RESULTS The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability-up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review. DISCUSSION We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions. CONCLUSIONS We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.
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Affiliation(s)
- Jeffrey G Klann
- Laboratory of Computer Science, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Hossein Estiri
- Laboratory of Computer Science, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Griffin M Weber
- Department of Biomedical Informatics, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Bertrand Moal
- IAM Unit, Public Health Department , Bordeaux University Hospital, Bordeaux, France
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Brett K Beaulieu-Jones
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Victor Castro
- Research Information Science and Computing, Mass General Brigham, Boston, Massachusetts, USA
| | - Thomas Maulhardt
- Institute of Medical Biometry and Statistics, Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Alon Geva
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts, USA.,Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy
| | - Andrew M South
- Section of Nephrology, Department of Pediatrics, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, North Carolina, USA
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Malarkodi J Samayamuthu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Kee Yuan Ngiam
- Department of Biomedical Informatics-WisDM, National University Health System, Singapore
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Martin Boeker
- Institute of Medical Biometry and Statistics, Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Karen L Olson
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics, Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Robert W Follett
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Riccardo Bellazzi
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.,Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy
| | - Jason H Moore
- Department of Biostatistics, Epidemiology, and Informatics, Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Ne-Hooi Will Loh
- Division of Critical Care, National University Health System, Singapore
| | - Douglas S Bell
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | | | - Luca Chiovato
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.,Department of Internal Medicine and Medical Therapy, University of Pavia, Pavia, Italy
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy
| | - Siegbert Rieg
- Division of Infectious Diseases, Department of Medicine II, Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Anthony L L J Li
- National Center for Infectious Diseases, Tan Tock Seng Hospital, Singapore
| | - Vianney Jouhet
- ERIAS-INSERM U1219 BPH, Bordeaux University Hospital, Bordeaux, France
| | - Emily Schriver
- Data Analytics Center, Penn Medicine, Philadelphia, Pennsylvania, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Meghan Hutch
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Research Information Science and Computing , Mass General Brigham, Boston, Massachusetts, USA
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Gordon WJ, Gottlieb D, Kreda D, Mandel JC, Mandl KD, Kohane IS. Patient-led data sharing for clinical bioinformatics research: USCDI and beyond. J Am Med Inform Assoc 2021; 28:2298-2300. [PMID: 34279631 DOI: 10.1093/jamia/ocab133] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 05/25/2021] [Accepted: 06/15/2021] [Indexed: 11/15/2022] Open
Abstract
The 21st Century Cures Act, passed in 2016, and the Final Rules it called for create a roadmap for enabling patient access to their electronic health information. The set of data to be made available, as determined by the Office of the National Coordinator for Health IT through the US Core Data for Interoperability expansion process, will impact the value creation of this improved data liquidity. In this commentary, we look at the potential for significant value creation from USCDI in the context of clinical bioinformatics research and advocate for the research community's involvement in the USCDI process to propel this value creation forward. We also describe 1 mechanism-using existing required APIs for full data export capabilities-that could pragmatically enable this value creation at minimal additional technical lift beyond the current regulatory requirements.
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Affiliation(s)
- William J Gordon
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.,Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Mass General Brigham, Boston, Massachusetts, USA
| | - Daniel Gottlieb
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.,Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
| | - David Kreda
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Joshua C Mandel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.,Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.,Microsoft Healthcare, Redmond, Washington, USA
| | - Kenneth D Mandl
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.,Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
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Abstract
Advances in new technologies, when incorporated into routine health screening, have tremendous promise to benefit children. The number of health screening tests, many of which have been developed with machine learning or genomics, has exploded. To assess efficacy of health screening, ideally, randomized trials of screening in youth would be conducted; however, these can take years to conduct and may not be feasible. Thus, innovative methods to evaluate the long-term outcomes of screening are needed to help clinicians and policymakers make informed decisions. These methods include using longitudinal and linked-data systems to evaluate screening in clinical and community settings, school data, simulation modeling approaches, and methods that take advantage of data available in the digital and genomic age. Future research is needed to evaluate how longitudinal and linked-data systems drawing on community and clinical settings can enable robust evaluations of the effects of screening on changes in health status. Additionally, future studies are needed to benchmark participating individuals and communities against similar counterparts and to link big data with natural experiments related to variation in screening policies. These novel approaches have great potential for identifying and addressing differences in access to screening and effectiveness of screening across population groups and communities.
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Affiliation(s)
- Ann Chen Wu
- Center for Healthcare Research in Pediatrics, Department of Population Medicine, Harvard Medical School, Harvard University and Harvard Pilgrim Health Care, Boston, Massachusetts
| | - Corina Graif
- Department of Sociology and Criminology, Population Research Institute, Pennsylvania State University, University Park, Pennsylvania
| | | | - John Meurer
- Division of Community Health, Medical College of Wisconsin, Milwaukie, Wisconsin
| | - Kenneth D. Mandl
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts,Departments of Biomedical Informatics and Pediatrics, Harvard Medical School, Boston, Massachusetts
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Levin JC, Beam AL, Fox KP, Mandl KD. Medication utilization in children born preterm in the first two years of life. J Perinatol 2021; 41:1732-1738. [PMID: 33547407 PMCID: PMC8277664 DOI: 10.1038/s41372-021-00930-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 10/12/2020] [Accepted: 01/15/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVE To compare medications dispensed during the first 2 years in children born preterm and full-term. STUDY DESIGN Retrospective analysis of claims data from a commercial national managed care plan 2008-2019. 329,855 beneficiaries were enrolled from birth through 2 years, of which 25,408 (7.7%) were preterm (<37 weeks). Filled prescription claims and paid amount over 2 years were identified. RESULTS In preterm children, the number of filled prescriptions was 1.4 times and cost was 3.8 times that of full-term children. Number and cost of medications were inversely related to gestational age. Differences peak at 4-9 months and resolve by 19 months after discharge. Palivizumab, ranitidine, albuterol, lansoprazole, budesonide, and prednisolone had the greatest differences in utilization. CONCLUSION Prescription medication utilization among preterm children under 2 years is driven by palivizumab, anti-reflux, and respiratory medications, despite little evidence regarding efficacy for many medications and concern for harm with certain classes.
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Affiliation(s)
- Jonathan C Levin
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, USA.
- Division of Pulmonary Medicine, Boston Children's Hospital, Boston, MA, USA.
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
| | - Andrew L Beam
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Kathe P Fox
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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35
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Abman SH, Mullen MP, Sleeper LA, Austin ED, Rosenzweig EB, Kinsella JP, Ivy D, Hopper RK, Usha Raj J, Fineman J, Keller RL, Bates A, Krishnan US, Avitabile CM, Davidson A, Natter MD, Mandl KD. Characterisation of Pediatric Pulmonary Hypertensive Vascular Disease from the PPHNet Registry. Eur Respir J 2021; 59:13993003.03337-2020. [PMID: 34140292 DOI: 10.1183/13993003.03337-2020] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 05/15/2021] [Indexed: 11/05/2022]
Abstract
BACKGROUND There are limited data about the range of diseases, natural history, age-appropriate endpoints and optimal care for children with pulmonary hypertension (PH), including the need for developing high quality patient registries of children with diverse forms of PH to enhance care and research. OBJECTIVE To characterise the distribution and clinical features of diseases associated with pediatric PH, including natural history, evaluation, therapeutic interventions and outcomes, as defined by the WSPH Classification. METHODS 1475 patients were enrolled into a multisite registry across the Pediatric Pulmonary Hypertension Network (PPHNet), comprised of 8 interdisciplinary PH programs. RESULTS WSPH Groups 1 (PAH) and 3 (lung disease) were the most common primary classifications (45% and 49% of subjects, respectively). The most common Group 3 conditions were BPD and CDH. Group 1 disease was predominantly associated with congenital heart disease (60%) and idiopathic (23% of Group 1 cases). In comparison with Group 1, Group 3 subjects had better disease resolution (HR=3.1, p<0.001), tended to be younger at diagnosis (0.3 (0.0,0.6) versus 1.6 (0.1,6.9) years (median (IQR); p<0.001), and were more often male (57% versus. 45%, p<0.001). Down syndrome (DS), the most common genetic syndrome in the registry, constituted 11% of the entire PH cohort. CONCLUSIONS We find a striking proportion of pediatric PH patients with Group 3 disorders, reflecting the growing recognition of PH in diverse developmental lung diseases. Greater precision of clinical phenotyping based on disease-specific characterization may further enhance care and research of pediatric PH.
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Affiliation(s)
- Steven H Abman
- From the Pediatric Heart Lung Center, Department of Pediatrics, University of Colorado Denver Anschutz Medical Center and Children's Hospital Colorado, Aurora, CO, USA .,co-first authors
| | - Mary P Mullen
- Department of Cardiology, Boston Children's Hospital, and Dept. of Pediatrics, Harvard Medical School, Boston, MA, USA.,co-first authors
| | - Lynn A Sleeper
- Department of Cardiology, Boston Children's Hospital, and Dept. of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Eric D Austin
- Department of Pediatrics, Vanderbilt University Medical Center, Monroe Carell Jr. Children's Hospital, Nashville, TN, USA
| | - Erika B Rosenzweig
- Division of Pediatric Cardiology, Columbia University Irving Medical Center, New York, NY, USA
| | - John P Kinsella
- Division of Neonatology, Department of Pediatrics, University of Colorado Denver Anschutz Medical Center and Children's Hospital Colorado, Aurora, CO, USA
| | - Dunbar Ivy
- Division of Cardiology, Department of Pediatrics, University of Colorado Denver Anschutz Medical Center and Children's Hospital Colorado, Aurora, CO, USA
| | - Rachel K Hopper
- Department of Pediatrics, Stanford University School of Medicine, Lucile Packard Children's Hospital Stanford, Palo Alto, CA, USA
| | - J Usha Raj
- Department of Pediatrics, University of Illinois at Chicago, Chicago, IL, USA
| | - Jeffrey Fineman
- Division of Critical Care, Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA
| | - Roberta L Keller
- Division of Neonatology, Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA
| | - Angela Bates
- Division of Cardiology, Department of Pediatrics, University of Alberta, Edmonton, Canada
| | - Usha S Krishnan
- Division of Pediatric Cardiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Catherine M Avitabile
- Division of Cardiology, Children's Hospital of Philadelphia, Departments of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Alexander Davidson
- Division of Cardiology, Children's Hospital of Philadelphia, Departments of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Marc D Natter
- Computational Health Informatics Program, Departments of Pediatrics and Biomedical Informatics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kenneth D Mandl
- Computational Health Informatics Program, Departments of Pediatrics and Biomedical Informatics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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36
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Jones J, Gottlieb D, Mandel JC, Ignatov V, Ellis A, Kubick W, Mandl KD. A landscape survey of planned SMART/HL7 bulk FHIR data access API implementations and tools. J Am Med Inform Assoc 2021; 28:1284-1287. [PMID: 33675659 DOI: 10.1093/jamia/ocab028] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 02/03/2021] [Indexed: 11/13/2022] Open
Abstract
The Office of National Coordinator for Health Information Technology final rule implementing the interoperability and information blocking provisions of the 21st Century Cures Act requires support for two SMART (Substitutable Medical Applications, Reusable Technologies) application programming interfaces (APIs) and instantiates Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR) as a lingua franca for health data. We sought to assess the current state and near-term plans for the SMART/HL7 Bulk FHIR Access API implementation across organizations including electronic health record vendors, cloud vendors, public health contractors, research institutions, payors, FHIR tooling developers, and other purveyors of health information technology platforms. We learned that many organizations not required through regulation to use standardized bulk data are rapidly implementing the API for a wide array of use cases. This may portend an unprecedented level of standardized population-level health data exchange that will support an apps and analytics ecosystem. Feedback from early adopters on the API's limitations and unsolved problems in the space of population health are highlighted.
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Affiliation(s)
- James Jones
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Daniel Gottlieb
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.,Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Joshua C Mandel
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Vladimir Ignatov
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Alyssa Ellis
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Wayne Kubick
- Health Level Seven International, Ann Arbor, Michigan, USA
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.,Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
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37
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Affiliation(s)
- Kenneth D Mandl
- From the Computational Health Informatics Program, Boston Children's Hospital, and the Department of Biomedical Informatics, Harvard Medical School - both in Boston (K.D.M.); and the Duke Clinical Research Institute, Duke University Medical Center, Durham, NC (E.D.P.)
| | - Eric D Perakslis
- From the Computational Health Informatics Program, Boston Children's Hospital, and the Department of Biomedical Informatics, Harvard Medical School - both in Boston (K.D.M.); and the Duke Clinical Research Institute, Duke University Medical Center, Durham, NC (E.D.P.)
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38
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Bourgeois FT, Gutiérrez-Sacristán A, Keller MS, Liu M, Hong C, Bonzel CL, Tan ALM, Aronow BJ, Boeker M, Booth J, Cruz Rojo J, Devkota B, García Barrio N, Gehlenborg N, Geva A, Hanauer DA, Hutch MR, Issitt RW, Klann JG, Luo Y, Mandl KD, Mao C, Moal B, Moshal KL, Murphy SN, Neuraz A, Ngiam KY, Omenn GS, Patel LP, Jiménez MP, Sebire NJ, Balazote PS, Serret-Larmande A, South AM, Spiridou A, Taylor DM, Tippmann P, Visweswaran S, Weber GM, Kohane IS, Cai T, Avillach P. International Analysis of Electronic Health Records of Children and Youth Hospitalized With COVID-19 Infection in 6 Countries. JAMA Netw Open 2021; 4:e2112596. [PMID: 34115127 PMCID: PMC8196345 DOI: 10.1001/jamanetworkopen.2021.12596] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
IMPORTANCE Additional sources of pediatric epidemiological and clinical data are needed to efficiently study COVID-19 in children and youth and inform infection prevention and clinical treatment of pediatric patients. OBJECTIVE To describe international hospitalization trends and key epidemiological and clinical features of children and youth with COVID-19. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study included pediatric patients hospitalized between February 2 and October 10, 2020. Patient-level electronic health record (EHR) data were collected across 27 hospitals in France, Germany, Spain, Singapore, the UK, and the US. Patients younger than 21 years who tested positive for COVID-19 and were hospitalized at an institution participating in the Consortium for Clinical Characterization of COVID-19 by EHR were included in the study. MAIN OUTCOMES AND MEASURES Patient characteristics, clinical features, and medication use. RESULTS There were 347 males (52%; 95% CI, 48.5-55.3) and 324 females (48%; 95% CI, 44.4-51.3) in this study's cohort. There was a bimodal age distribution, with the greatest proportion of patients in the 0- to 2-year (199 patients [30%]) and 12- to 17-year (170 patients [25%]) age range. Trends in hospitalizations for 671 children and youth found discrete surges with variable timing across 6 countries. Data from this cohort mirrored national-level pediatric hospitalization trends for most countries with available data, with peaks in hospitalizations during the initial spring surge occurring within 23 days in the national-level and 4CE data. A total of 27 364 laboratory values for 16 laboratory tests were analyzed, with mean values indicating elevations in markers of inflammation (C-reactive protein, 83 mg/L; 95% CI, 53-112 mg/L; ferritin, 417 ng/mL; 95% CI, 228-607 ng/mL; and procalcitonin, 1.45 ng/mL; 95% CI, 0.13-2.77 ng/mL). Abnormalities in coagulation were also evident (D-dimer, 0.78 ug/mL; 95% CI, 0.35-1.21 ug/mL; and fibrinogen, 477 mg/dL; 95% CI, 385-569 mg/dL). Cardiac troponin, when checked (n = 59), was elevated (0.032 ng/mL; 95% CI, 0.000-0.080 ng/mL). Common complications included cardiac arrhythmias (15.0%; 95% CI, 8.1%-21.7%), viral pneumonia (13.3%; 95% CI, 6.5%-20.1%), and respiratory failure (10.5%; 95% CI, 5.8%-15.3%). Few children were treated with COVID-19-directed medications. CONCLUSIONS AND RELEVANCE This study of EHRs of children and youth hospitalized for COVID-19 in 6 countries demonstrated variability in hospitalization trends across countries and identified common complications and laboratory abnormalities in children and youth with COVID-19 infection. Large-scale informatics-based approaches to integrate and analyze data across health care systems complement methods of disease surveillance and advance understanding of epidemiological and clinical features associated with COVID-19 in children and youth.
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Affiliation(s)
- Florence T. Bourgeois
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
| | | | - Mark S. Keller
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Molei Liu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Amelia L. M. Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Bruce J. Aronow
- Departments of Biomedical Informatics, Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Ohio
| | - Martin Boeker
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Germany
| | - John Booth
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, London, United Kingdom
| | - Jaime Cruz Rojo
- Department of Health Informatics, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Batsal Devkota
- Department of Biomedical Health Informatics and the Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Noelia García Barrio
- Department of Health Informatics, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Alon Geva
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children’s Hospital, Boston, Massachusetts
| | - David A. Hanauer
- Department of Learning Health Sciences, University of Michigan, Ann Arbor
| | - Meghan R. Hutch
- Department of Preventive Medicine, Northwestern University, Evanston, Illinois
| | - Richard W. Issitt
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, London, United Kingdom
| | | | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Evanston, Illinois
| | - Kenneth D. Mandl
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
| | - Chengsheng Mao
- Department of Preventive Medicine, Northwestern University, Evanston, Illinois
| | - Bertrand Moal
- IAM Unit, Bordeaux University Hospital, Bordeaux, France
| | - Karyn L. Moshal
- Department of Infectious Diseases, Great Ormond Street Hospital for Children, London, United Kingdom
| | - Shawn N. Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Antoine Neuraz
- Department of Biomedical Informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris, University of Paris, Paris, France
| | - Kee Yuan Ngiam
- Department of Biomedical informatics, WiSDM, National University Health Systems Singapore, Singapore
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, & School of Public Health, University of Michigan, Ann Arbor
| | - Lav P. Patel
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City
| | | | - Neil J. Sebire
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, London, United Kingdom
| | | | | | - Andrew M. South
- Department of Pediatrics-Section of Nephrology, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, North Carolina
| | - Anastasia Spiridou
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, London, United Kingdom
| | - Deanne M. Taylor
- Department of Biomedical Health Informatics and the Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Pediatrics, Perelman Medical School at the University of Pennsylvania, Philadelphia
| | - Patric Tippmann
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Germany
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Griffin M. Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Isaac S. Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Paul Avillach
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
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Lee IH, Lin Y, Alvarez WJ, Hernandez-Ferrer C, Mandl KD, Kong SW. WEScover: selection between clinical whole exome sequencing and gene panel testing. BMC Bioinformatics 2021; 22:259. [PMID: 34016036 PMCID: PMC8139020 DOI: 10.1186/s12859-021-04178-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 05/09/2021] [Indexed: 11/18/2022] Open
Abstract
Background Whole exome sequencing (WES) is widely adopted in clinical and research settings; however, one of the practical concerns is the potential false negatives due to incomplete breadth and depth of coverage for several exons in clinically implicated genes. In some cases, a targeted gene panel testing may be a dependable option to ascertain true negatives for genomic variants in known disease-associated genes. We developed a web-based tool to quickly gauge whether all genes of interest would be reliably covered by WES or whether targeted gene panel testing should be considered instead to minimize false negatives in candidate genes. Results WEScover is a novel web application that provides an intuitive user interface for discovering breadth and depth of coverage across population-scale WES datasets, searching either by phenotype, by targeted gene panel(s) or by gene(s). Moreover, the application shows metrics from the Genome Aggregation Database to provide gene-centric view on breadth of coverage. Conclusions WEScover allows users to efficiently query genes and phenotypes for the coverage of associated exons by WES and recommends use of panel tests for the genes with potential incomplete coverage by WES. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04178-5.
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Affiliation(s)
- In-Hee Lee
- Computational Health Informatics Program, Boston Children's Hospital, 401 Park Drive, Mail Stop BCH3187, LM5528.4, Boston, MA, 02115, USA
| | - Yufei Lin
- Computational Health Informatics Program, Boston Children's Hospital, 401 Park Drive, Mail Stop BCH3187, LM5528.4, Boston, MA, 02115, USA
| | - William Jefferson Alvarez
- Computational Health Informatics Program, Boston Children's Hospital, 401 Park Drive, Mail Stop BCH3187, LM5528.4, Boston, MA, 02115, USA.,Agios Pharmaceuticals, Boston, MA, USA
| | - Carles Hernandez-Ferrer
- Computational Health Informatics Program, Boston Children's Hospital, 401 Park Drive, Mail Stop BCH3187, LM5528.4, Boston, MA, 02115, USA.,Centre Nacional d'Anàlisi Genòmica (CNAG-CRG), Barcelona, Spain
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, 401 Park Drive, Mail Stop BCH3187, LM5528.4, Boston, MA, 02115, USA.,Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Sek Won Kong
- Computational Health Informatics Program, Boston Children's Hospital, 401 Park Drive, Mail Stop BCH3187, LM5528.4, Boston, MA, 02115, USA. .,Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA.
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Geva A, Abman SH, Manzi SF, Ivy DD, Mullen MP, Griffin J, Lin C, Savova GK, Mandl KD. Adverse drug event rates in pediatric pulmonary hypertension: a comparison of real-world data sources. J Am Med Inform Assoc 2021; 27:294-300. [PMID: 31769835 PMCID: PMC7025334 DOI: 10.1093/jamia/ocz194] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Revised: 10/08/2019] [Accepted: 10/21/2019] [Indexed: 11/14/2022] Open
Abstract
Objective Real-world data (RWD) are increasingly used for pharmacoepidemiology and regulatory innovation. Our objective was to compare adverse drug event (ADE) rates determined from two RWD sources, electronic health records and administrative claims data, among children treated with drugs for pulmonary hypertension. Materials and Methods Textual mentions of medications and signs/symptoms that may represent ADEs were identified in clinical notes using natural language processing. Diagnostic codes for the same signs/symptoms were identified in our electronic data warehouse for the patients with textual evidence of taking pulmonary hypertension-targeted drugs. We compared rates of ADEs identified in clinical notes to those identified from diagnostic code data. In addition, we compared putative ADE rates from clinical notes to those from a healthcare claims dataset from a large, national insurer. Results Analysis of clinical notes identified up to 7-fold higher ADE rates than those ascertained from diagnostic codes. However, certain ADEs (eg, hearing loss) were more often identified in diagnostic code data. Similar results were found when ADE rates ascertained from clinical notes and national claims data were compared. Discussion While administrative claims and clinical notes are both increasingly used for RWD-based pharmacovigilance, ADE rates substantially differ depending on data source. Conclusion Pharmacovigilance based on RWD may lead to discrepant results depending on the data source analyzed. Further work is needed to confirm the validity of identified ADEs, to distinguish them from disease effects, and to understand tradeoffs in sensitivity and specificity between data sources.
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Affiliation(s)
- Alon Geva
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.,Division of Critical Care Medicine, Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts, USA.,Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts, USA
| | - Steven H Abman
- Division of Pediatric Pulmonary Medicine, Children's Hospital Colorado, Aurora, Colorado, USA.,Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Shannon F Manzi
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.,Division of Genetics & Genomics, Clinical Pharmacogenomics Service, Department of Pharmacy, Boston Children's Hospital, Boston, Massachusetts, USA.,Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Dunbar D Ivy
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado, USA.,Division of Cardiology, Heart Institute, Children's Hospital Colorado, Aurora, Colorado, USA
| | - Mary P Mullen
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.,Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - John Griffin
- Division of Critical Care Medicine, Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Chen Lin
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Guergana K Savova
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.,Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.,Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
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Kohane IS, Aronow BJ, Avillach P, Beaulieu-Jones BK, Bellazzi R, Bradford RL, Brat GA, Cannataro M, Cimino JJ, García-Barrio N, Gehlenborg N, Ghassemi M, Gutiérrez-Sacristán A, Hanauer DA, Holmes JH, Hong C, Klann JG, Loh NHW, Luo Y, Mandl KD, Daniar M, Moore JH, Murphy SN, Neuraz A, Ngiam KY, Omenn GS, Palmer N, Patel LP, Pedrera-Jiménez M, Sliz P, South AM, Tan ALM, Taylor DM, Taylor BW, Torti C, Vallejos AK, Wagholikar KB, Weber GM, Cai T. What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask. J Med Internet Res 2021; 23:e22219. [PMID: 33600347 PMCID: PMC7927948 DOI: 10.2196/22219] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 09/14/2020] [Accepted: 01/10/2021] [Indexed: 12/13/2022] Open
Abstract
Coincident with the tsunami of COVID-19–related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.
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Affiliation(s)
- Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Bruce J Aronow
- Biomedical Informatics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, United States
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | | | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.,ICS Maugeri, Pavia, Italy
| | - Robert L Bradford
- North Carolina Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Mario Cannataro
- Data Analytics Research Center, University Magna Graecia of Catanzaro, Catanzaro, Italy.,Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - James J Cimino
- Informatics Institute, University of Alabama at Birmingham, Birmingham, AL, United States
| | | | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Marzyeh Ghassemi
- Department of Computer Science and Medicine, University of Toronto, Toronto, ON, Canada
| | | | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, United States
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Jeffrey G Klann
- Department of Medicine, Harvard Medical School, Boston, MA, United States.,Laboratory of Computer Science, Massachusetts General Hospital, Boston, MA, United States
| | | | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States
| | - Mohamad Daniar
- Clinical Research Informatics, Boston Children's Hospital, Boston, MA, United States
| | - Jason H Moore
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
| | - Shawn N Murphy
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.,Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Antoine Neuraz
- Department of Biomedical Informatics, Necker-Enfant Malades Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France.,Centre de Recherche des Cordeliers, INSERM UMRS 1138 Team 22, Université de Paris, Paris, France
| | - Kee Yuan Ngiam
- National University Health Systems, Singapore, Singapore
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Nathan Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Lav P Patel
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, KS, United States
| | | | - Piotr Sliz
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States
| | - Andrew M South
- Section of Nephrology, Department of Pediatrics, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Amelia Li Min Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.,Department of Biomedical Informatics, National University of Singapore, Singapore, Singapore
| | - Deanne M Taylor
- Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Pediatrics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, United States
| | - Bradley W Taylor
- Clinical and Translational Science Institute, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Carlo Torti
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Andrew K Vallejos
- Clinical and Translational Science Institute, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Kavishwar B Wagholikar
- Department of Medicine, Harvard Medical School, Boston, MA, United States.,Laboratory of Computer Science, Massachusetts General Hospital, Boston, MA, United States
| | | | - Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
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42
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Sayeed R, Jones J, Gottlieb D, Mandel JC, Mandl KD. A proposal for shoring up Federal Trade Commission protections for electronic health record-connected consumer apps under 21st Century Cures. J Am Med Inform Assoc 2021; 28:640-645. [PMID: 33306804 PMCID: PMC7936404 DOI: 10.1093/jamia/ocaa227] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 08/28/2020] [Accepted: 09/09/2020] [Indexed: 12/03/2022] Open
Abstract
Under the 21st Century Cures Act and the Office of the National Coordinator for Health Information Technology (ONC) rule implementing its interoperability provisions, a patient’s rights to easily request and obtain digital access to portions of their medical records are now supported by both technology and policy. Data, once directed by a patient to leave a Health Insurance Portability and Accountability Act–covered health entity and enter a consumer app, will usually fall under Federal Trade Commission oversight. Because the statutory authority of the ONC does not extend to health data protection, there is not yet regulation to specifically address privacy protections for consumer apps. A technologically feasible workflow that could be widely adopted and permissible under ONC’s rule, involves using the SMART on FHIR OAuth authorization routine to present standardized information about app behavior. This approach would not bias the patient in a way that triggers penalties under information blocking provisions of the rule.
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Affiliation(s)
- Raheel Sayeed
- Computational Health Informatics Program, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.,Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - James Jones
- Computational Health Informatics Program, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Daniel Gottlieb
- Computational Health Informatics Program, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.,Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Joshua C Mandel
- Computational Health Informatics Program, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.,Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
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43
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Geva A, Liu M, Panickan VA, Avillach P, Cai T, Mandl KD. A high-throughput phenotyping algorithm is portable from adult to pediatric populations. J Am Med Inform Assoc 2021; 28:1265-1269. [PMID: 33594412 DOI: 10.1093/jamia/ocaa343] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 11/27/2020] [Accepted: 12/28/2020] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE Multimodal automated phenotyping (MAP) is a scalable, high-throughput phenotyping method, developed using electronic health record (EHR) data from an adult population. We tested transportability of MAP to a pediatric population. MATERIALS AND METHODS Without additional feature engineering or supervised training, we applied MAP to a pediatric population enrolled in a biobank and evaluated performance against physician-reviewed medical records. We also compared performance of MAP at the pediatric institution and the original adult institution where MAP was developed, including for 6 phenotypes validated at both institutions against physician-reviewed medical records. RESULTS MAP performed equally well in the pediatric setting (average AUC 0.98) as it did at the general adult hospital system (average AUC 0.96). MAP's performance in the pediatric sample was similar across the 6 specific phenotypes also validated against gold-standard labels in the adult biobank. CONCLUSIONS MAP is highly transportable across diverse populations and has potential for wide-scale use.
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Affiliation(s)
- Alon Geva
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.,Division of Critical Care Medicine, Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts, USA.,Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts, USA
| | - Molei Liu
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Vidul A Panickan
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Paul Avillach
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.,Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Tianxi Cai
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.,Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
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Weber GM, Hong C, Palmer NP, Avillach P, Murphy SN, Gutiérrez-Sacristán A, Xia Z, Serret-Larmande A, Neuraz A, Omenn GS, Visweswaran S, Klann JG, South AM, Loh NHW, Cannataro M, Beaulieu-Jones BK, Bellazzi R, Agapito G, Alessiani M, Aronow BJ, Bell DS, Bellasi A, Benoit V, Beraghi M, Boeker M, Booth J, Bosari S, Bourgeois FT, Brown NW, Bucalo M, Chiovato L, Chiudinelli L, Dagliati A, Devkota B, DuVall SL, Follett RW, Ganslandt T, García Barrio N, Gradinger T, Griffier R, Hanauer DA, Holmes JH, Horki P, Huling KM, Issitt RW, Jouhet V, Keller MS, Kraska D, Liu M, Luo Y, Lynch KE, Malovini A, Mandl KD, Mao C, Maram A, Matheny ME, Maulhardt T, Mazzitelli M, Milano M, Moore JH, Morris JS, Morris M, Mowery DL, Naughton TP, Ngiam KY, Norman JB, Patel LP, Pedrera Jimenez M, Ramoni RB, Schriver ER, Scudeller L, Sebire NJ, Serrano Balazote P, Spiridou A, Tan AL, Tan BW, Tibollo V, Torti C, Trecarichi EM, Vitacca M, Zambelli A, Zucco C, Kohane IS, Cai T, Brat GA. International Comparisons of Harmonized Laboratory Value Trajectories to Predict Severe COVID-19: Leveraging the 4CE Collaborative Across 342 Hospitals and 6 Countries: A Retrospective Cohort Study. medRxiv 2021:2020.12.16.20247684. [PMID: 33564777 PMCID: PMC7872369 DOI: 10.1101/2020.12.16.20247684] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Objectives To perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions. Design Retrospective cohort study. Setting The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), an international multi-site data-sharing collaborative of 342 hospitals in the US and in Europe. Participants Patients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2. Primary and secondary outcome measures Patients were categorized as "ever-severe" or "never-severe" using the validated 4CE severity criteria. Eighteen laboratory tests associated with poor COVID-19-related outcomes were evaluated for predictive accuracy by area under the curve (AUC), compared between the severity categories. Subgroup analysis was performed to validate a subset of laboratory values as predictive of severity against a published algorithm. A subset of laboratory values (CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin) was compared between North American and European sites for severity prediction. Results Of 36,447 patients with COVID-19, 19,953 (43.7%) were categorized as ever-severe. Most patients (78.7%) were 50 years of age or older and male (60.5%). Longitudinal trajectories of CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin showed association with disease severity. Significant differences of laboratory values at admission were found between the two groups. With the exception of D-dimer, predictive discrimination of laboratory values did not improve after admission. Sub-group analysis using age, D-dimer, CRP, and lymphocyte count as predictive of severity at admission showed similar discrimination to a published algorithm (AUC=0.88 and 0.91, respectively). Both models deteriorated in predictive accuracy as the disease progressed. On average, no difference in severity prediction was found between North American and European sites. Conclusions Laboratory test values at admission can be used to predict severity in patients with COVID-19. Prediction models show consistency across international sites highlighting the potential generalizability of these models.
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Affiliation(s)
- Griffin M Weber
- Harvard Medical School, Department of Biomedical Informatics
| | - Chuan Hong
- Harvard Medical School, Department of Biomedical Informatics
| | - Nathan P Palmer
- Harvard Medical School, Department of Biomedical Informatics
| | - Paul Avillach
- Harvard Medical School, Department of Biomedical Informatics
| | | | | | | | - Arnaud Serret-Larmande
- Ho pital Européen Georges Pompidou, Assistance Publique - Ho pitaux de Paris, Department of biomedical informatics
| | | | - Gilbert S Omenn
- University of Michigan, Dept of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - John Booth
- Great Ormond Street Hospital for Children
| | - Silvano Bosari
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico
| | | | | | - Mauro Bucalo
- BIOMERIS (BIOMedical Research Informatics Solutions)
| | | | | | | | | | | | | | - Thomas Ganslandt
- Ruprecht Karls University Heidelberg Faculty of Medicine Mannheim
| | | | - Tobias Gradinger
- Ruprecht Karls University Heidelberg Faculty of Medicine Mannheim
| | | | - David A Hanauer
- University of Michigan Institute for Healthcare Policy & Innovation
| | - John H Holmes
- University of Pennsylvania Perelman School of Medicine
| | | | | | | | | | - Mark S Keller
- Harvard Medical School, Department of Biomedical Informatics
| | | | - Molei Liu
- Harvard University T H Chan School of Public Health
| | | | | | | | - Kenneth D Mandl
- Boston Children's Hospital, Computational Health Informatics Program
| | | | | | | | | | | | | | - Jason H Moore
- University of Pennsylvania Perelman School of Medicine
| | | | | | | | | | | | - James B Norman
- Harvard Medical School, Department of Biomedical Informatics
| | | | | | | | | | | | | | | | | | - Amelia Lm Tan
- Harvard Medical School, Department of Biomedical Informatics
| | | | | | | | | | | | | | | | - Isaac S Kohane
- Harvard Medical School, Department of Biomedical Informatics
| | - Tianxi Cai
- Harvard Medical School, Department of Biomedical Informatics
| | - Gabriel A Brat
- Beth Israel Deaconess Medical Center, Surgery
- Harvard Medical School, Department of Biomedical Informatics
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Gutiérrez-Sacristán A, De Niz C, Kothari C, Kong SW, Mandl KD, Avillach P. GenoPheno: cataloging large-scale phenotypic and next-generation sequencing data within human datasets. Brief Bioinform 2021; 22:55-65. [PMID: 32249310 PMCID: PMC7820848 DOI: 10.1093/bib/bbaa033] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 01/31/2020] [Indexed: 12/17/2022] Open
Abstract
Precision medicine promises to revolutionize treatment, shifting therapeutic approaches from the classical one-size-fits-all to those more tailored to the patient's individual genomic profile, lifestyle and environmental exposures. Yet, to advance precision medicine's main objective-ensuring the optimum diagnosis, treatment and prognosis for each individual-investigators need access to large-scale clinical and genomic data repositories. Despite the vast proliferation of these datasets, locating and obtaining access to many remains a challenge. We sought to provide an overview of available patient-level datasets that contain both genotypic data, obtained by next-generation sequencing, and phenotypic data-and to create a dynamic, online catalog for consultation, contribution and revision by the research community. Datasets included in this review conform to six specific inclusion parameters that are: (i) contain data from more than 500 human subjects; (ii) contain both genotypic and phenotypic data from the same subjects; (iii) include whole genome sequencing or whole exome sequencing data; (iv) include at least 100 recorded phenotypic variables per subject; (v) accessible through a website or collaboration with investigators and (vi) make access information available in English. Using these criteria, we identified 30 datasets, reviewed them and provided results in the release version of a catalog, which is publicly available through a dynamic Web application and on GitHub. Users can review as well as contribute new datasets for inclusion (Web: https://avillachlab.shinyapps.io/genophenocatalog/; GitHub: https://github.com/hms-dbmi/GenoPheno-CatalogShiny).
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Affiliation(s)
| | - Carlos De Niz
- Department of Biomedical Informatics, Harvard Medical School
| | - Cartik Kothari
- Department of Biomedical Informatics, Harvard Medical School
| | - Sek Won Kong
- Department of Biomedical Informatics, Harvard Medical School; Computational Health Informatics Program, Boston Children's Hospital
| | - Kenneth D Mandl
- Department of Biomedical Informatics, Harvard Medical School; Computational Health Informatics Program, Boston Children's Hospital
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School; Computational Health Informatics Program, Boston Children's Hospital
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McGraw D, Mandl KD. Privacy protections to encourage use of health-relevant digital data in a learning health system. NPJ Digit Med 2021; 4:2. [PMID: 33398052 PMCID: PMC7782585 DOI: 10.1038/s41746-020-00362-8] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 10/30/2020] [Indexed: 11/09/2022] Open
Abstract
The National Academy of Medicine has long advocated for a "learning healthcare system" that produces constantly updated reference data during the care process. Moving toward a rapid learning system to solve intractable problems in health demands a balance between protecting patients and making data available to improve health and health care. Public concerns in the U.S. about privacy and the potential for unethical or harmful uses of this data, if not proactively addressed, could upset this balance. New federal laws prioritize sharing health data, including with patient digital tools. U.S. health privacy laws do not cover data collected by many consumer digital technologies and have not been updated to address concerns about the entry of large technology companies into health care. Further, there is increasing recognition that many classes of data not traditionally considered to be healthcare-related, for example consumer credit histories, are indeed predictive of health status and outcomes. We propose a multi-pronged approach to protecting health-relevant data while promoting and supporting beneficial uses and disclosures to improve health and health care for individuals and populations. Such protections should apply to entities collecting health-relevant data regardless of whether they are covered by federal health privacy laws. We focus largely on privacy but also address protections against harms as a critical component of a comprehensive approach to governing health-relevant data. U.S. policymakers and regulators should consider these recommendations in crafting privacy bills and rules. However, our recommendations also can inform best practices even in the absence of new federal requirements.
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Affiliation(s)
| | - Kenneth D Mandl
- Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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47
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Estiri H, Klann JG, Weiler SR, Alema-Mensah E, Joseph Applegate R, Lozinski G, Patibandla N, Wei K, Adams WG, Natter MD, Ofili EO, Ostasiewski B, Quarshie A, Rosenthal GE, Bernstam EV, Mandl KD, Murphy SN. A federated EHR network data completeness tracking system. J Am Med Inform Assoc 2020; 26:637-645. [PMID: 30925587 PMCID: PMC6586954 DOI: 10.1093/jamia/ocz014] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 01/04/2019] [Accepted: 01/17/2019] [Indexed: 02/03/2023] Open
Abstract
OBJECTIVE The study sought to design, pilot, and evaluate a federated data completeness tracking system (CTX) for assessing completeness in research data extracted from electronic health record data across the Accessible Research Commons for Health (ARCH) Clinical Data Research Network. MATERIALS AND METHODS The CTX applies a systems-based approach to design workflow and technology for assessing completeness across distributed electronic health record data repositories participating in a queryable, federated network. The CTX invokes 2 positive feedback loops that utilize open source tools (DQe-c and Vue) to integrate technology and human actors in a system geared for increasing capacity and taking action. A pilot implementation of the system involved 6 ARCH partner sites between January 2017 and May 2018. RESULTS The ARCH CTX has enabled the network to monitor and, if needed, adjust its data management processes to maintain complete datasets for secondary use. The system allows the network and its partner sites to profile data completeness both at the network and partner site levels. Interactive visualizations presenting the current state of completeness in the context of the entire network as well as changes in completeness across time were valued among the CTX user base. DISCUSSION Distributed clinical data networks are complex systems. Top-down approaches that solely rely on technology to report data completeness may be necessary but not sufficient for improving completeness (and quality) of data in large-scale clinical data networks. Improving and maintaining complete (high-quality) data in such complex environments entails sociotechnical systems that exploit technology and empower human actors to engage in the process of high-quality data curating. CONCLUSIONS The CTX has increased the network's capacity to rapidly identify data completeness issues and empowered ARCH partner sites to get involved in improving the completeness of respective data in their repositories.
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Affiliation(s)
- Hossein Estiri
- Laboratory of Computer Science, Massachusetts General Hospital, Boston, Massachusetts, USA.,Research Information Science and Computing, Partners HealthCare, Charlestown, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Jeffrey G Klann
- Laboratory of Computer Science, Massachusetts General Hospital, Boston, Massachusetts, USA.,Research Information Science and Computing, Partners HealthCare, Charlestown, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | | | | | - R Joseph Applegate
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Galina Lozinski
- Boston University School of Medicine/Boston Medical Center, Boston, Massachusetts, USA
| | - Nandan Patibandla
- Information Services Department, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Kun Wei
- Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - William G Adams
- Department of Pediatrics, Boston University School of Medicine/Boston Medical Center, Boston, Massachusetts, USA
| | - Marc D Natter
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.,Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.,Program in Pediatric Rheumatology, Department of Pediatrics, Mass General Hospital for Children, Boston, Massachusetts, USA
| | | | | | | | - Gary E Rosenthal
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Elmer V Bernstam
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA.,Division of General Internal Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.,Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Shawn N Murphy
- Laboratory of Computer Science, Massachusetts General Hospital, Boston, Massachusetts, USA.,Research Information Science and Computing, Partners HealthCare, Charlestown, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.,Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
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48
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Gordon WJ, Mandl KD. The 21st Century Cures Act: A Competitive Apps Market and the Risk of Innovation Blocking. J Med Internet Res 2020; 22:e24824. [PMID: 33306034 PMCID: PMC7762678 DOI: 10.2196/24824] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 11/01/2020] [Accepted: 11/09/2020] [Indexed: 01/19/2023] Open
Abstract
The 21st Century Cures Act and the recently published "final rule" define standardized methods for obtaining electronic copies of electronic health record (EHR) data through application programming interfaces. The rule is meant to create an ecosystem of reusable, substitutable apps that can be built once but run at any hospital system "without special effort." Yet, despite numerous provisions around information blocking in the final rule, there is concern that the business practices that govern EHR vendors and health care organizations in the United States could still stifle innovation. We describe potential app ecosystems that may form. We caution that misaligned incentives may result in anticompetitive behavior and purposefully limited functionality. Closed proprietary ecosystems may result, limiting the value derived from interoperability. The 21st Century Cures Act and final rule are an exciting step in the direction of improved interoperability. However, realizing the vision of a truly interoperable app ecosystem is not predetermined.
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Affiliation(s)
- William J Gordon
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, United States.,Mass General Brigham, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Kenneth D Mandl
- Harvard Medical School, Boston, MA, United States.,Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States
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49
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Mandl KD, Gottlieb D, Mandel JC, Ignatov V, Sayeed R, Grieve G, Jones J, Ellis A, Culbertson A. Push Button Population Health: The SMART/HL7 FHIR Bulk Data Access Application Programming Interface. NPJ Digit Med 2020; 3:151. [PMID: 33299056 PMCID: PMC7678833 DOI: 10.1038/s41746-020-00358-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Accepted: 10/20/2020] [Indexed: 01/19/2023] Open
Abstract
The 21st Century Cures Act requires that certified health information technology have an application programming interface (API) giving access to all data elements of a patient's electronic health record, "without special effort". In the spring of 2020, the Office of the National Coordinator of Health Information Technology (ONC) published a rule-21st Century Cures Act Interoperability, Information Blocking, and the ONC Health IT Certification Program-regulating the API requirement along with protections against information blocking. The rule specifies the SMART/HL7 FHIR Bulk Data Access API, which enables access to patient-level data across a patient population, supporting myriad use cases across healthcare, research, and public health ecosystems. The API enables "push button population health" in that core data elements can readily and standardly be extracted from electronic health records, enabling local, regional, and national-scale data-driven innovation.
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Affiliation(s)
- Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.
- Departments of Pediatrics, Harvard Medical School, Boston, MA, USA.
- Departments of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Daniel Gottlieb
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
- Departments of Pediatrics, Harvard Medical School, Boston, MA, USA
- Central Square Solutions, Cambridge, MA, USA
| | - Joshua C Mandel
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
- Departments of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Microsoft Healthcare, Redmond, WA, USA
| | - Vladimir Ignatov
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
| | - Raheel Sayeed
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
- Departments of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Grahame Grieve
- Health Level 7, Ann Arbor, MI, USA
- Health Intersections, Pty Ltd, Warrandyte, Australia
| | - James Jones
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
| | - Alyssa Ellis
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
| | - Adam Culbertson
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
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50
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Dunn AG, Surian D, Dalmazzo J, Rezazadegan D, Steffens M, Dyda A, Leask J, Coiera E, Dey A, Mandl KD. Limited Role of Bots in Spreading Vaccine-Critical Information Among Active Twitter Users in the United States: 2017-2019. Am J Public Health 2020; 110:S319-S325. [PMID: 33001719 DOI: 10.2105/ajph.2020.305902] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Objectives. To examine the role that bots play in spreading vaccine information on Twitter by measuring exposure and engagement among active users from the United States.Methods. We sampled 53 188 US Twitter users and examined who they follow and retweet across 21 million vaccine-related tweets (January 12, 2017-December 3, 2019). Our analyses compared bots to human-operated accounts and vaccine-critical tweets to other vaccine-related tweets.Results. The median number of potential exposures to vaccine-related tweets per user was 757 (interquartile range [IQR] = 168-4435), of which 27 (IQR = 6-169) were vaccine critical, and 0 (IQR = 0-12) originated from bots. We found that 36.7% of users retweeted vaccine-related content, 4.5% retweeted vaccine-critical content, and 2.1% retweeted vaccine content from bots. Compared with other users, the 5.8% for whom vaccine-critical tweets made up most exposures more often retweeted vaccine content (62.9%; odds ratio [OR] = 2.9; 95% confidence interval [CI] = 2.7, 3.1), vaccine-critical content (35.0%; OR = 19.0; 95% CI = 17.3, 20.9), and bots (8.8%; OR = 5.4; 95% CI = 4.7, 6.3).Conclusions. A small proportion of vaccine-critical information that reaches active US Twitter users comes from bots.
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Affiliation(s)
- Adam G Dunn
- Adam G. Dunn and Jason Dalmazzo are with the Discipline of Biomedical Informatics and Digital Health, University of Sydney, Sydney, Australia. Didi Surian, Maryke Steffens, Amalie Dyda, and Enrico Coiera are with the Centre for Health Informatics, Macquarie University, Sydney, Australia. Dana Rezazadegan is with the Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, Australia. Julie Leask is with the Susan Wakil School of Nursing and Midwifery, University of Sydney, Sydney, Australia. Aditi Dey is with the National Centre for Immunisation Research and Surveillance, University of Sydney, Sydney, Australia. Kenneth D. Mandl is with the Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
| | - Didi Surian
- Adam G. Dunn and Jason Dalmazzo are with the Discipline of Biomedical Informatics and Digital Health, University of Sydney, Sydney, Australia. Didi Surian, Maryke Steffens, Amalie Dyda, and Enrico Coiera are with the Centre for Health Informatics, Macquarie University, Sydney, Australia. Dana Rezazadegan is with the Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, Australia. Julie Leask is with the Susan Wakil School of Nursing and Midwifery, University of Sydney, Sydney, Australia. Aditi Dey is with the National Centre for Immunisation Research and Surveillance, University of Sydney, Sydney, Australia. Kenneth D. Mandl is with the Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
| | - Jason Dalmazzo
- Adam G. Dunn and Jason Dalmazzo are with the Discipline of Biomedical Informatics and Digital Health, University of Sydney, Sydney, Australia. Didi Surian, Maryke Steffens, Amalie Dyda, and Enrico Coiera are with the Centre for Health Informatics, Macquarie University, Sydney, Australia. Dana Rezazadegan is with the Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, Australia. Julie Leask is with the Susan Wakil School of Nursing and Midwifery, University of Sydney, Sydney, Australia. Aditi Dey is with the National Centre for Immunisation Research and Surveillance, University of Sydney, Sydney, Australia. Kenneth D. Mandl is with the Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
| | - Dana Rezazadegan
- Adam G. Dunn and Jason Dalmazzo are with the Discipline of Biomedical Informatics and Digital Health, University of Sydney, Sydney, Australia. Didi Surian, Maryke Steffens, Amalie Dyda, and Enrico Coiera are with the Centre for Health Informatics, Macquarie University, Sydney, Australia. Dana Rezazadegan is with the Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, Australia. Julie Leask is with the Susan Wakil School of Nursing and Midwifery, University of Sydney, Sydney, Australia. Aditi Dey is with the National Centre for Immunisation Research and Surveillance, University of Sydney, Sydney, Australia. Kenneth D. Mandl is with the Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
| | - Maryke Steffens
- Adam G. Dunn and Jason Dalmazzo are with the Discipline of Biomedical Informatics and Digital Health, University of Sydney, Sydney, Australia. Didi Surian, Maryke Steffens, Amalie Dyda, and Enrico Coiera are with the Centre for Health Informatics, Macquarie University, Sydney, Australia. Dana Rezazadegan is with the Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, Australia. Julie Leask is with the Susan Wakil School of Nursing and Midwifery, University of Sydney, Sydney, Australia. Aditi Dey is with the National Centre for Immunisation Research and Surveillance, University of Sydney, Sydney, Australia. Kenneth D. Mandl is with the Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
| | - Amalie Dyda
- Adam G. Dunn and Jason Dalmazzo are with the Discipline of Biomedical Informatics and Digital Health, University of Sydney, Sydney, Australia. Didi Surian, Maryke Steffens, Amalie Dyda, and Enrico Coiera are with the Centre for Health Informatics, Macquarie University, Sydney, Australia. Dana Rezazadegan is with the Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, Australia. Julie Leask is with the Susan Wakil School of Nursing and Midwifery, University of Sydney, Sydney, Australia. Aditi Dey is with the National Centre for Immunisation Research and Surveillance, University of Sydney, Sydney, Australia. Kenneth D. Mandl is with the Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
| | - Julie Leask
- Adam G. Dunn and Jason Dalmazzo are with the Discipline of Biomedical Informatics and Digital Health, University of Sydney, Sydney, Australia. Didi Surian, Maryke Steffens, Amalie Dyda, and Enrico Coiera are with the Centre for Health Informatics, Macquarie University, Sydney, Australia. Dana Rezazadegan is with the Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, Australia. Julie Leask is with the Susan Wakil School of Nursing and Midwifery, University of Sydney, Sydney, Australia. Aditi Dey is with the National Centre for Immunisation Research and Surveillance, University of Sydney, Sydney, Australia. Kenneth D. Mandl is with the Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
| | - Enrico Coiera
- Adam G. Dunn and Jason Dalmazzo are with the Discipline of Biomedical Informatics and Digital Health, University of Sydney, Sydney, Australia. Didi Surian, Maryke Steffens, Amalie Dyda, and Enrico Coiera are with the Centre for Health Informatics, Macquarie University, Sydney, Australia. Dana Rezazadegan is with the Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, Australia. Julie Leask is with the Susan Wakil School of Nursing and Midwifery, University of Sydney, Sydney, Australia. Aditi Dey is with the National Centre for Immunisation Research and Surveillance, University of Sydney, Sydney, Australia. Kenneth D. Mandl is with the Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
| | - Aditi Dey
- Adam G. Dunn and Jason Dalmazzo are with the Discipline of Biomedical Informatics and Digital Health, University of Sydney, Sydney, Australia. Didi Surian, Maryke Steffens, Amalie Dyda, and Enrico Coiera are with the Centre for Health Informatics, Macquarie University, Sydney, Australia. Dana Rezazadegan is with the Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, Australia. Julie Leask is with the Susan Wakil School of Nursing and Midwifery, University of Sydney, Sydney, Australia. Aditi Dey is with the National Centre for Immunisation Research and Surveillance, University of Sydney, Sydney, Australia. Kenneth D. Mandl is with the Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
| | - Kenneth D Mandl
- Adam G. Dunn and Jason Dalmazzo are with the Discipline of Biomedical Informatics and Digital Health, University of Sydney, Sydney, Australia. Didi Surian, Maryke Steffens, Amalie Dyda, and Enrico Coiera are with the Centre for Health Informatics, Macquarie University, Sydney, Australia. Dana Rezazadegan is with the Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, Australia. Julie Leask is with the Susan Wakil School of Nursing and Midwifery, University of Sydney, Sydney, Australia. Aditi Dey is with the National Centre for Immunisation Research and Surveillance, University of Sydney, Sydney, Australia. Kenneth D. Mandl is with the Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
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