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Campion TR, Craven CK, Dorr DA, Bernstam EV, Knosp BM. Understanding enterprise data warehouses to support clinical and translational research: impact, sustainability, demand management, and accessibility. J Am Med Inform Assoc 2024:ocae111. [PMID: 38777803 DOI: 10.1093/jamia/ocae111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 04/10/2024] [Accepted: 05/05/2024] [Indexed: 05/25/2024] Open
Abstract
OBJECTIVES Healthcare organizations, including Clinical and Translational Science Awards (CTSA) hubs funded by the National Institutes of Health, seek to enable secondary use of electronic health record (EHR) data through an enterprise data warehouse for research (EDW4R), but optimal approaches are unknown. In this qualitative study, our goal was to understand EDW4R impact, sustainability, demand management, and accessibility. MATERIALS AND METHODS We engaged a convenience sample of informatics leaders from CTSA hubs (n = 21) for semi-structured interviews and completed a directed content analysis of interview transcripts. RESULTS EDW4R have created institutional capacity for single- and multi-center studies, democratized access to EHR data for investigators from multiple disciplines, and enabled the learning health system. Bibliometrics have been challenging due to investigator non-compliance, but one hub's requirement to link all study protocols with funding records enabled quantifying an EDW4R's multi-million dollar impact. Sustainability of EDW4R has relied on multiple funding sources with a general shift away from the CTSA grant toward institutional and industry support. To address EDW4R demand, institutions have expanded staff, used different governance approaches, and provided investigator self-service tools. EDW4R accessibility can benefit from improved tools incorporating user-centered design, increased data literacy among scientists, expansion of informaticians in the workforce, and growth of team science. DISCUSSION As investigator demand for EDW4R has increased, approaches to tracking impact, ensuring sustainability, and improving accessibility of EDW4R resources have varied. CONCLUSION This study adds to understanding of how informatics leaders seek to support investigators using EDW4R across the CTSA consortium and potentially elsewhere.
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Affiliation(s)
- Thomas R Campion
- Clinical & Translational Science Center, Weill Cornell Medicine, New York, NY 10022, United States
| | - Catherine K Craven
- Division of Clinical Research Informatics, Department of Population Health Sciences, The University of Texas Health San Antonio, San Antonio, TX 78229, United States
| | - David A Dorr
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, United States
- Department of Medicine, Oregon Health & Science University, Portland, OR 97239, United States
| | - Elmer V Bernstam
- D. Bradley McWilliams School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX 77030, United States
- Division of General Internal Medicine, McGovern Medical School and Center for Clinical and Translational Sciences, The University of Texas Health Science Center, Houston, TX 77030, United States
| | - Boyd M Knosp
- Roy J. and Lucille A. Carver College of Medicine and the Institute for Clinical & Translational Science, University of Iowa, Iowa City, IA 52242, United States
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Davis HA, Santillan DA, Ortman CE, Hoberg AA, Hetrick JP, McBrearty CW, Zeng E, Vaughan Sarrazin MS, Dunn Lopez K, Chapman CG, Carnahan RM, Michaelson JJ, Knosp BM. The Iowa Health Data Resource (IHDR): an innovative framework for transforming the clinical health data ecosystem. J Am Med Inform Assoc 2024; 31:720-726. [PMID: 38102790 PMCID: PMC10873835 DOI: 10.1093/jamia/ocad236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/13/2023] [Accepted: 11/22/2023] [Indexed: 12/17/2023] Open
Abstract
IMPORTANCE This manuscript will be of interest to most Clinical and Translational Science Awards (CTSA) as they retool for the increasing emphasis on translational science from translational research. This effort is an extension of the EDW4R work that most CTSAs have done to deploy infrastructure and tools for researchers to access clinical data. OBJECTIVES The Iowa Health Data Resource (IHDR) is a strategic investment made by the University of Iowa to improve access to real-world health data. The goals of IHDR are to improve the speed of translational health research, to boost interdisciplinary collaboration, and to improve literacy about health data. The first objective toward this larger goal was to address gaps in data access, data literacy, lack of computational environments for processing Personal Health Information (PHI) and the lack of processes and expertise for creating transformative datasets. METHODS A three-pronged approach was taken to address the objective. The approach involves integration of an intercollegiate team of non-informatics faculty and staff, a data enclave for secure patient data analyses, and novel comprehensive datasets. RESULTS To date, all five of the health science colleges (dentistry, medicine, nursing, pharmacy, and public health) have had at least one staff and one faculty member complete the two-month experiential learning curriculum. Over the first two years of this project, nine cohorts totaling 36 data liaisons have been trained, including 18 faculty and 18 staff. IHDR data enclave eliminated the need to duplicate computational infrastructure inside the hospital firewall which reduced infrastructure, hardware and human resource costs while leveraging the existing expertise embedded in the university research computing team. The creation of a process to develop and implement transformative datasets has resulted in the creation of seven domain specific datasets to date. CONCLUSION The combination of people, process, and technology facilitates collaboration and interdisciplinary research in a secure environment using curated data sets. While other organizations have implemented individual components to address EDW4R operational demands, the IHDR combines multiple resources into a novel, comprehensive ecosystem IHDR enables scientists to use analysis tools with electronic patient data to accelerate time to science.
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Affiliation(s)
- Heath A Davis
- Biomedical Informatics, Institute for Clinical & Translational Science, University of Iowa, Iowa City, IA 52242, United States
- Office of Information Technology, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA 52242, United States
| | - Donna A Santillan
- Biomedical Informatics, Institute for Clinical & Translational Science, University of Iowa, Iowa City, IA 52242, United States
- Obstetrics and Gynecology, University of Iowa, Iowa City, IA 52242, United States
| | - Chris E Ortman
- Biomedical Informatics, Institute for Clinical & Translational Science, University of Iowa, Iowa City, IA 52242, United States
- Office of Information Technology, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA 52242, United States
| | - Asher A Hoberg
- Biomedical Informatics, Institute for Clinical & Translational Science, University of Iowa, Iowa City, IA 52242, United States
| | - Joseph P Hetrick
- Research Services, Information Technology Services, University of Iowa, Iowa City, IA 52245, United States
| | - Charles W McBrearty
- Department of Preventive and Community Dentistry, College of Dentistry, College of Dentistry, University of Iowa, Iowa City, IA 52242, United States
| | - Erliang Zeng
- Department of Preventive and Community Dentistry, College of Dentistry, College of Dentistry, University of Iowa, Iowa City, IA 52242, United States
- Division of Biostatistics and Computational Biology, College of Dentistry, University of Iowa, Iowa City, IA 52242, United States
| | - Mary S Vaughan Sarrazin
- Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA 52242, United States
- Center for Access and Delivery Research and Evaluation, Veterans Affairs Medical Center, Iowa City, IA 52246, United States
| | - Karen Dunn Lopez
- Center for Nursing Classification and Clinical Effectiveness, College of Nursing, University of Iowa, Iowa City, IA 52242, United States
| | - Cole G Chapman
- Pharmacy Practice and Science, College of Pharmacy, University of Iowa, Iowa City, IA 52242, United States
| | - Ryan M Carnahan
- Epidemiology, College of Public Health, University of Iowa, Iowa City, IA 52242, United States
| | - Jacob J Michaelson
- Psychiatry, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA 52242, United States
| | - Boyd M Knosp
- Biomedical Informatics, Institute for Clinical & Translational Science, University of Iowa, Iowa City, IA 52242, United States
- Office of Information Technology, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA 52242, United States
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Davila MA, Sholle ET, Fuld X, Israel ML, Cole CL, Campion TR. Linking Patient Encounters across Primary and Ancillary Electronic Health Record Systems: A Comparison of Two Approaches. ACI OPEN 2024; 8:e43-e48. [PMID: 38765555 PMCID: PMC11101195 DOI: 10.1055/s-0044-1782679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Background To achieve scientific goals, researchers often require integration of data from a primary electronic health record (EHR) system and one or more ancillary EHR systems used during the same patient care encounter. Although studies have demonstrated approaches for linking patient identity records across different EHR systems, little is known about linking patient encounter records across primary and ancillary EHR systems. Objectives We compared a patients-first approach versus an encounters-first approach for linking patient encounter records across multiple EHR systems. Methods We conducted a retrospective observational study of 348,904 patients with 533,283 encounters from 2010 to 2020 across our institution's primary EHR system and an ancillary EHR system used in perioperative settings. For the patients-first approach and the encounters-first approach, we measured the number of patient and encounter links created as well as runtime. Results While the patients-first approach linked 43% of patients and 49% of encounters, the encounters-first approach linked 98% of patients and 100% of encounters. The encounters-first approach was 20 times faster than the patients-first approach for linking patients and 33% slower for linking encounters. Conclusion Findings suggest that common patient and encounter identifiers shared among EHR systems via automated interfaces may be clinically useful but not "research-ready" and thus require an encounters-first linkage approach to enable secondary use for scientific purposes. Based on our search, this study is among the first to demonstrate approaches for linking patient encounters across multiple EHR systems. Enterprise data warehouse for research efforts elsewhere may benefit from an encounters-first approach.
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Affiliation(s)
- Marcos A. Davila
- Information Technologies & Services Department, Weill Cornell Medicine, New York, New York, United States
| | - Evan T. Sholle
- Information Technologies & Services Department, Weill Cornell Medicine, New York, New York, United States
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States
| | - Xiaobo Fuld
- Information Technologies & Services Department, Weill Cornell Medicine, New York, New York, United States
| | - Mark L. Israel
- Clinical IT Shared Services, NewYork-Presbyterian, New York, New York, United States
| | - Curtis L. Cole
- Information Technologies & Services Department, Weill Cornell Medicine, New York, New York, United States
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States
- Clinical IT Shared Services, NewYork-Presbyterian, New York, New York, United States
- Department of Medicine, Weill Cornell Medicine, New York, New York, United States
- Clinical and Translational Science Center, Weill Cornell Medicine, New York, New York, United States
| | - Thomas R. Campion
- Information Technologies & Services Department, Weill Cornell Medicine, New York, New York, United States
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States
- Clinical and Translational Science Center, Weill Cornell Medicine, New York, New York, United States
- Department of Pediatrics, Weill Cornell Medicine, New York, New York, United States
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Waters R, Malecki S, Lail S, Mak D, Saha S, Jung HY, Imrit MA, Razak F, Verma AA. Automated identification of unstandardized medication data: a scalable and flexible data standardization pipeline using RxNorm on GEMINI multicenter hospital data. JAMIA Open 2023; 6:ooad062. [PMID: 37565023 PMCID: PMC10409892 DOI: 10.1093/jamiaopen/ooad062] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 07/18/2023] [Accepted: 07/24/2023] [Indexed: 08/12/2023] Open
Abstract
Objective Patient data repositories often assemble medication data from multiple sources, necessitating standardization prior to analysis. We implemented and evaluated a medication standardization procedure for use with a wide range of pharmacy data inputs across all drug categories, which supports research queries at multiple levels of granularity. Methods The GEMINI-RxNorm system automates the use of multiple RxNorm tools in tandem with other datasets to identify drug concepts from pharmacy orders. GEMINI-RxNorm was used to process 2 090 155 pharmacy orders from 245 258 hospitalizations between 2010 and 2017 at 7 hospitals in Ontario, Canada. The GEMINI-RxNorm system matches drug-identifying information from pharmacy data (including free-text fields) to RxNorm concept identifiers. A user interface allows researchers to search for drug terms and returns the relevant original pharmacy data through the matched RxNorm concepts. Users can then manually validate the predicted matches and discard false positives. We designed the system to maximize recall (sensitivity) and enable excellent precision (positive predictive value) with efficient manual validation. We compared the performance of this system to manual coding (by a physician and pharmacist) of 13 medication classes. Results Manual coding was performed for 1 948 817 pharmacy orders and GEMINI-RxNorm successfully returned 1 941 389 (99.6%) orders. Recall was greater than 0.985 in all 13 drug classes, and the F1-score and precision remained above 0.90 in all drug classes, facilitating efficient manual review to achieve 100% precision. GEMINI-RxNorm saved time substantially compared with manual standardization, reducing the time taken to review a pharmacy order row from an estimated 30 to 5 s and reducing the number of rows needed to be reviewed by up to 99.99%. Discussion and Conclusion GEMINI-RxNorm presents a novel combination of RxNorm tools and other datasets to enable accurate, efficient, flexible, and scalable standardization of pharmacy data. By facilitating efficient manual validation, the GEMINI-RxNorm system can allow researchers to achieve near-perfect accuracy in medication data standardization.
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Affiliation(s)
- Riley Waters
- St. Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada
| | - Sarah Malecki
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Sharan Lail
- St. Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada
| | - Denise Mak
- St. Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada
| | - Sudipta Saha
- St. Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada
| | - Hae Young Jung
- St. Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada
| | | | - Fahad Razak
- St. Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada
| | - Amol A Verma
- St. Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada
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Wen A, He H, Fu S, Liu S, Miller K, Wang L, Roberts KE, Bedrick SD, Hersh WR, Liu H. The IMPACT framework and implementation for accessible in silico clinical phenotyping in the digital era. NPJ Digit Med 2023; 6:132. [PMID: 37479735 PMCID: PMC10362064 DOI: 10.1038/s41746-023-00878-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 07/13/2023] [Indexed: 07/23/2023] Open
Abstract
Clinical phenotyping is often a foundational requirement for obtaining datasets necessary for the development of digital health applications. Traditionally done via manual abstraction, this task is often a bottleneck in development due to time and cost requirements, therefore raising significant interest in accomplishing this task via in-silico means. Nevertheless, current in-silico phenotyping development tends to be focused on a single phenotyping task resulting in a dearth of reusable tools supporting cross-task generalizable in-silico phenotyping. In addition, in-silico phenotyping remains largely inaccessible for a substantial portion of potentially interested users. Here, we highlight the barriers to the usage of in-silico phenotyping and potential solutions in the form of a framework of several desiderata as observed during our implementation of such tasks. In addition, we introduce an example implementation of said framework as a software application, with a focus on ease of adoption, cross-task reusability, and facilitating the clinical phenotyping algorithm development process.
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Affiliation(s)
- Andrew Wen
- Department of AI & Informatics, Mayo Clinic, Rochester, MN, 55905, USA
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Huan He
- Department of AI & Informatics, Mayo Clinic, Rochester, MN, 55905, USA
| | - Sunyang Fu
- Department of AI & Informatics, Mayo Clinic, Rochester, MN, 55905, USA
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Sijia Liu
- Department of AI & Informatics, Mayo Clinic, Rochester, MN, 55905, USA
| | - Kurt Miller
- Department of AI & Informatics, Mayo Clinic, Rochester, MN, 55905, USA
| | - Liwei Wang
- Department of AI & Informatics, Mayo Clinic, Rochester, MN, 55905, USA
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Kirk E Roberts
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Steven D Bedrick
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, 97239, USA
| | - William R Hersh
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Hongfang Liu
- Department of AI & Informatics, Mayo Clinic, Rochester, MN, 55905, USA.
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA.
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Magoc T, Everson R, Harle CA. Enhancing an enterprise data warehouse for research with data extracted using natural language processing. J Clin Transl Sci 2023; 7:e149. [PMID: 37456264 PMCID: PMC10346024 DOI: 10.1017/cts.2023.575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 05/14/2023] [Accepted: 05/31/2023] [Indexed: 07/18/2023] Open
Abstract
Objective This study aims to develop a generalizable architecture for enhancing an enterprise data warehouse for research (EDW4R) with results from a natural language processing (NLP) model, which allows discrete data derived from clinical notes to be made broadly available for research use without need for NLP expertise. The study also quantifies the additional value that information extracted from clinical narratives brings to EDW4R. Materials and methods Clinical notes written during one month at an academic health center were used to evaluate the performance of an existing NLP model and to quantify its value added to the structured data. Manual review was utilized for performance analysis. The architecture for enhancing the EDW4R is described in detail to enable reproducibility. Results Two weeks were needed to enhance EDW4R with data from 250 million clinical notes. NLP generated 16 and 39% increase in data availability for two variables. Discussion Our architecture is highly generalizable to a new NLP model. The positive predictive value obtained by an independent team showed only slightly lower NLP performance than the values reported by the NLP developers. The NLP showed significant value added to data already available in structured format. Conclusion Given the value added by data extracted using NLP, it is important to enhance EDW4R with these data to enable research teams without NLP expertise to benefit from value added by NLP models.
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Affiliation(s)
- Tanja Magoc
- College of Medicine, University of Florida, Gainesville, FL, USA
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Carson MB, Gonzales S, Shaw P, Schneider D, Holmes K. Bridging the gap: A library-based collaboration to enhance data skills for clinical researchers. Learn Health Syst 2023; 7:e10339. [PMID: 37066097 PMCID: PMC10091201 DOI: 10.1002/lrh2.10339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 08/06/2022] [Accepted: 08/17/2022] [Indexed: 11/05/2022] Open
Abstract
Introduction Enterprise data warehouses (EDWs) serve as foundational infrastructure in a modern learning health system, housing clinical and other system-wide data and making it available for research, strategic, and quality improvement purposes. Building on a longstanding partnership between Northwestern University's Galter Health Sciences Library and the Northwestern Medicine Enterprise Data Warehouse (NMEDW), an end-to-end clinical research data management (cRDM) program was created to enhance clinical data workforce capacity and further expand related library-based services for the campus. Methods The training program covers topics such as clinical database architecture, clinical coding standards, and translation of research questions into queries for proper data extraction. Here we describe this program, including partners and motivations, technical and social components, integration of FAIR principles into clinical data research workflows, and the long-term implications for this work to serve as a blueprint of best practice workflows for clinical research to support library and EDW partnerships at other institutions. Results This training program has enhanced the partnership between our institution's health sciences library and clinical data warehouse to provide support services for researchers, resulting in more efficient training workflows. Through instruction on best practices for preserving and sharing outputs, researchers are given the tools to improve the reproducibility and reusability of their work, which has positive effects for the researchers as well as for the university. All training resources have been made publicly available so that those who support this critical need at other institutions can build on our efforts. Conclusions Library-based partnerships to support training and consultation offer an important vehicle for clinical data science capacity building in learning health systems. The cRDM program launched by Galter Library and the NMEDW is an example of this type of partnership and builds on a strong foundation of past collaboration, expanding the scope of clinical data support services and training on campus.
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Affiliation(s)
- Matthew B. Carson
- Galter Health Sciences Library & Learning CenterNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Sara Gonzales
- Galter Health Sciences Library & Learning CenterNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Pamela Shaw
- Galter Health Sciences Library & Learning CenterNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Daniel Schneider
- Northwestern Medicine Enterprise Data WarehouseNorthwestern Medicine and Northwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Kristi Holmes
- Department of Preventive Medicine (Health and Biomedical Informatics) and Galter Health Sciences Library & Learning CenterNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
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Alexander J, Beatty A. Nonspecific deidentification of date-like text in deidentified clinical notes enables reidentification of dates. J Am Med Inform Assoc 2022; 29:1967-1971. [PMID: 36217861 PMCID: PMC9552287 DOI: 10.1093/jamia/ocac147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/19/2022] [Accepted: 08/14/2022] [Indexed: 06/16/2023] Open
Abstract
To facilitate the secondary usage of electronic health record data for research, the University of California, San Francisco (UCSF) recently implemented a clinical data warehouse including, among other data, deidentified clinical notes and reports, which are available to UCSF researchers without Institutional Review Board approval. For deidentification of these notes, most of the Health Insurance Portability and Accountability Act identifiers are redacted, but dates are transformed by shifting all dates for a patient back by the same random number of days. We describe an issue in which nonspecific (ie, excess) transformation of nondate, date-like text by this deidentification process enables reidentification of all dates, including birthdates, for certain patients. This issue undercuts the common assumption that excess deidentification is a safe tradeoff to protect patient privacy. We present this issue as a caution to other institutions that may also be considering releasing deidentified notes for research.
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Affiliation(s)
- Jes Alexander
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, California, USA
| | - Alexis Beatty
- Department of Epidemiology and Biostatistics and Department of Medicine, Division of Cardiology, University of California, San Francisco, San Francisco, California, USA
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Honeyford K, Expert P, Mendelsohn E, Post B, Faisal A, Glampson B, Mayer E, Costelloe C. Challenges and recommendations for high quality research using electronic health records. Front Digit Health 2022; 4:940330. [PMID: 36060540 PMCID: PMC9437583 DOI: 10.3389/fdgth.2022.940330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 07/28/2022] [Indexed: 12/02/2022] Open
Abstract
Harnessing Real World Data is vital to improve health care in the 21st Century. Data from Electronic Health Records (EHRs) are a rich source of patient centred data, including information on the patient's clinical condition, laboratory results, diagnoses and treatments. They thus reflect the true state of health systems. However, access and utilisation of EHR data for research presents specific challenges. We assert that using data from EHRs effectively is dependent on synergy between researchers, clinicians and health informaticians, and only this will allow state of the art methods to be used to answer urgent and vital questions for patient care. We propose that there needs to be a paradigm shift in the way this research is conducted - appreciating that the research process is iterative rather than linear. We also make specific recommendations for organisations, based on our experience of developing and using EHR data in trusted research environments.
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Affiliation(s)
- K Honeyford
- Global Digital Health Unit, School of Public Health, Imperial College London, London, United Kingdom
- Health Informatics Team, Division of Clinical studies, Institute of Cancer Research, London, United Kingdom
| | - P Expert
- Global Digital Health Unit, School of Public Health, Imperial College London, London, United Kingdom
- Global Business School for Health, University College London, London, United Kingdom
| | - E.E Mendelsohn
- Global Digital Health Unit, School of Public Health, Imperial College London, London, United Kingdom
| | - B Post
- Department of Computing, Imperial College London, London, United Kingdom
- UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, United Kingdom
| | - A.A Faisal
- Department of Computing, Imperial College London, London, United Kingdom
- UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, United Kingdom
- Chair in Digital Health, Faculty of Life Sciences, University of Bayreuth, Bayreuth, Germany
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - B Glampson
- Translational Data Analytics and Informatics in Healthcare, Department of Surgery & Cancer, Imperial College London, London, United Kingdom
- Imperial Clinical Analytics, Informatics and Evaluation (iCARE), NIHR Imperial BRC, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - E.K Mayer
- UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, United Kingdom
- Translational Data Analytics and Informatics in Healthcare, Department of Surgery & Cancer, Imperial College London, London, United Kingdom
- Imperial Clinical Analytics, Informatics and Evaluation (iCARE), NIHR Imperial BRC, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - C.E Costelloe
- Global Digital Health Unit, School of Public Health, Imperial College London, London, United Kingdom
- Health Informatics Team, Division of Clinical studies, Institute of Cancer Research, London, United Kingdom
- Health Informatics Team, Royal Marsden Hospital, London, United Kingdom
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Relative Incidence of Emergency Department Visits After Treatment for Prostate Cancer with Radiation Therapy or Radical Prostatectomy. Pract Radiat Oncol 2022; 12:e415-e422. [PMID: 35595216 DOI: 10.1016/j.prro.2022.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/12/2022] [Accepted: 05/03/2022] [Indexed: 11/21/2022]
Abstract
PURPOSE Side effect profiles play an important role in treatment decisions for localized prostate cancer. Emergency department (ED) visits, which may be due to side effects from treatment, can be measured in real-world, structured, electronic health record (EHR) data. The goal of this study was to determine whether treatments for localized prostate cancer are associated with ED visits, as a measure of side effects, using EHR data. METHODS AND MATERIALS We used a self-controlled case series study (SCCS) design, including patients treated at an urban academic medical center with radiation therapy (RT) or radical prostatectomy (RP) for prostate cancer between 2011 and 2020 who had visits documented for ≥ 6 months before and after treatment and ≥1 ED visit. We estimated relative incidences (RI) of ED visits, comparing incidence in the exposed and unexposed periods, with the exposed period being between start of treatment and 1 month after completion, and the unexposed period consisting of all other documented time. RESULTS Among men who had at least one ED visit and after adjusting for age, there were higher rates of ED visits after RP (RI 20.4, 95% confidence interval [CI] 15.4-27.0, p<0.001), RT overall (RI 2.4, CI 1.7-3.4, p<0.001), intensity modulated radiation therapy with high dose-rate brachytherapy (HDR) (RI 3.4, CI 1.7-6.8, p<0.001) or stereotactic body radiation therapy boost (RI 7.1, CI 3.4-14.8, p < 0.001), and HDR alone (RI 16.3, CI 7.2-36.9, p<0.001), compared to unexposed time. The number needed to harm to result in an ED visit was less for RP (17, CI 13-23) than RT overall (43, CI 25-126), but varied by RT modality. CONCLUSIONS In summary, relative rates of ED visits vary by treatment type, suggesting differing severities of side effects. These data may aid in selecting treatments and demonstrate the feasibility of using the SCCS study design on ED visits in real-world, structured EHR data to better understand side effects of treatment.
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Ragon B, Volkov BB, Pulley C, Holmes K. Using informatics to advance translational science: Environmental scan of adaptive capacity and preparedness of Clinical and Translational Science Award Program hubs. J Clin Transl Sci 2022; 6:e76. [PMID: 35836790 PMCID: PMC9274387 DOI: 10.1017/cts.2022.402] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 04/06/2022] [Indexed: 11/29/2022] Open
Abstract
As the USA and the rest of the world raced to fight the COVID-19 pandemic, years of investments from the National Center for Advancing Translational Sciences allowed for informatics services and resources at CTSA hubs to play a significant role in addressing the crisis. CTSA hubs partnered with local and regional partners to collect data on the pandemic, provide access to relevant patient data, and produce data dashboards to support decision-making. Coordinated efforts, like the National COVID Cohort Collaborative (N3C), helped to aggregate and harmonize clinical data nationwide. Even with significant informatics investments, some CTSA hubs felt unprepared in their ability to respond to the fast-moving public health crisis. Many hubs were forced to quickly evolve to meet local needs. Informatics teams expanded critical support at their institutions which included an engagement platform for clinical research, COVID-19 awareness and education activities in the community, and COVID-19 data dashboards. Continued investments in informatics resources will aid in ensuring that tools, resources, practices, and policies are aligned to meet local and national public health needs.
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Affiliation(s)
- Bart Ragon
- Integrated Translational Health Research Institute of Virginia, Charlottesville, VA, USA
- University of Virginia, Charlottesville, VA, USA
| | - Boris B. Volkov
- University of Minnesota Clinical and Translational Science Institute, Minneapolis, MN, USA
- Institute for Health Informatics and Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Chris Pulley
- University of Minnesota Clinical and Translational Science Institute, Minneapolis, MN, USA
| | - Kristi Holmes
- Northwestern University Clinical and Translational Sciences Institute, Chicago, IL, USA
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
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12
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Knosp BM, Craven CK, Dorr DA, Bernstam EV, Campion TR. Understanding enterprise data warehouses to support clinical and translational research: enterprise information technology relationships, data governance, workforce, and cloud computing. J Am Med Inform Assoc 2022; 29:671-676. [PMID: 35289370 PMCID: PMC8922193 DOI: 10.1093/jamia/ocab256] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 11/05/2021] [Indexed: 01/22/2023] Open
Abstract
OBJECTIVE Among National Institutes of Health Clinical and Translational Science Award (CTSA) hubs, effective approaches for enterprise data warehouses for research (EDW4R) development, maintenance, and sustainability remain unclear. The goal of this qualitative study was to understand CTSA EDW4R operations within the broader contexts of academic medical centers and technology. MATERIALS AND METHODS We performed a directed content analysis of transcripts generated from semistructured interviews with informatics leaders from 20 CTSA hubs. RESULTS Respondents referred to services provided by health system, university, and medical school information technology (IT) organizations as "enterprise information technology (IT)." Seventy-five percent of respondents stated that the team providing EDW4R service at their hub was separate from enterprise IT; strong relationships between EDW4R teams and enterprise IT were critical for success. Managing challenges of EDW4R staffing was made easier by executive leadership support. Data governance appeared to be a work in progress, as most hubs reported complex and incomplete processes, especially for commercial data sharing. Although nearly all hubs (n = 16) described use of cloud computing for specific projects, only 2 hubs reported using a cloud-based EDW4R. Respondents described EDW4R cloud migration facilitators, barriers, and opportunities. DISCUSSION Descriptions of approaches to how EDW4R teams at CTSA hubs work with enterprise IT organizations, manage workforces, make decisions about data, and approach cloud computing provide insights for institutions seeking to leverage patient data for research. CONCLUSION Identification of EDW4R best practices is challenging, and this study helps identify a breadth of viable options for CTSA hubs to consider when implementing EDW4R services.
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Affiliation(s)
- Boyd M Knosp
- Roy J. and Lucille A. Carver College of Medicine and the Institute for Clinical & Translational Science, University of Iowa, Iowa City, Iowa, USA
| | - Catherine K Craven
- Division of Clinical Research Informatics, Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, Texas, USA
| | - David A Dorr
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
- Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | - Elmer V Bernstam
- Center for Clinical and Translational Sciences, University of Texas Health Science Center, Houston, Texas, USA
| | - Thomas R Campion
- Clinical & Translational Science Center, Weill Cornell Medicine, New York, New York, USA
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
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13
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Meeker D, Fu P, Garcia G, Dyer IE, Yadav K, Fleishman R, Yee HF. Establishing a research informatics program in a public healthcare system: a case report with model documents. J Am Med Inform Assoc 2022; 29:694-700. [PMID: 35289368 PMCID: PMC8922175 DOI: 10.1093/jamia/ocab226] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 08/25/2021] [Accepted: 10/19/2021] [Indexed: 09/20/2023] Open
Abstract
While much is known about governance models for research informatics programs in academic medical centers and similarly situated cancer centers, community and public health systems have been less well-characterized. As part of implementing an enterprise research governance framework, leaders in the Los Angeles County Department of Health Services established a research informatics program, including research data warehousing. The strategy is focused on high-priority, patient-centered research that leverages the investment in health IT and an efficient, sustained contribution from 2 affiliated Clinical Translational Sciences Institutes. This case study describes the foundational governance framework and policies that were developed. We share the results of several years of planning, implementation, and operations of an academically funded research informatics service core embedded in a large, multicenter county health system. We include herein a Supplementary Appendix of governance documents that may serve as pragmatic models for similar initiatives.
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Affiliation(s)
- Daniella Meeker
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA
| | - Paul Fu
- Department of Pediatrics, City of Hope, Duarte, California, USA
| | - Gary Garcia
- Department of Health Services, Los Angeles County, Los Angeles, California, USA
| | - Irene E Dyer
- Department of Health Services, Los Angeles County, Los Angeles, California, USA
| | - Kabir Yadav
- Department of Health Services, Los Angeles County, Los Angeles, California, USA
| | - Ross Fleishman
- Department of Health Services, Los Angeles County, Los Angeles, California, USA
| | - Hal F Yee
- Department of Health Services, Los Angeles County, Los Angeles, California, USA
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14
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Kahn MG, Mui JY, Ames MJ, Yamsani AK, Pozdeyev N, Rafaels N, Brooks IM. Migrating a research data warehouse to a public cloud: challenges and opportunities. J Am Med Inform Assoc 2021; 29:592-600. [PMID: 34919694 PMCID: PMC8922165 DOI: 10.1093/jamia/ocab278] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 11/15/2021] [Accepted: 12/03/2021] [Indexed: 11/13/2022] Open
Abstract
Objective Clinical research data warehouses (RDWs) linked to genomic pipelines and open data archives are being created to support innovative, complex data-driven discoveries. The computing and storage needs of these research environments may quickly exceed the capacity of on-premises systems. New RDWs are migrating to cloud platforms for the scalability and flexibility needed to meet these challenges. We describe our experience in migrating a multi-institutional RDW to a public cloud. Materials and Methods This study is descriptive. Primary materials included internal and public presentations before and after the transition, analysis documents, and actual billing records. Findings were aggregated into topical categories. Results Eight categories of migration issues were identified. Unanticipated challenges included legacy system limitations; network, computing, and storage architectures that realize performance and cost benefits in the face of hyper-innovation, complex security reviews and approvals, and limited cloud consulting expertise. Discussion Cloud architectures enable previously unavailable capabilities, but numerous pitfalls can impede realizing the full benefits of a cloud environment. Rapid changes in cloud capabilities can quickly obsolete existing architectures and associated institutional policies. Touchpoints with on-premise networks and systems can add unforeseen complexity. Governance, resource management, and cost oversight are critical to allow rapid innovation while minimizing wasted resources and unnecessary costs. Conclusions Migrating our RDW to the cloud has enabled capabilities and innovations that would not have been possible with an on-premises environment. Notwithstanding the challenges of managing cloud resources, the resulting RDW capabilities have been highly positive to our institution, research community, and partners.
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Affiliation(s)
- Michael G Kahn
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA.,Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Joyce Y Mui
- Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | | | - Anoop K Yamsani
- Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Nikita Pozdeyev
- Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA.,Division of Endocrinology, Metabolism and Diabetes, University of Colorado School of Medicine, Aurora, CO, USA.,Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado, Aurora, CO, USA
| | - Nicholas Rafaels
- Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA.,Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado, Aurora, CO, USA
| | - Ian M Brooks
- Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA.,Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado, Aurora, CO, USA
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15
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Hogan WR, Shenkman EA, Robinson T, Carasquillo O, Robinson PS, Essner RZ, Bian J, Lipori G, Harle C, Magoc T, Manini L, Mendoza T, White S, Loiacono A, Hall J, Nelson D. The OneFlorida Data Trust: a centralized, translational research data infrastructure of statewide scope. J Am Med Inform Assoc 2021; 29:686-693. [PMID: 34664656 PMCID: PMC8922180 DOI: 10.1093/jamia/ocab221] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 09/03/2021] [Accepted: 09/29/2021] [Indexed: 01/22/2023] Open
Abstract
The OneFlorida Data Trust is a centralized research patient data repository created and managed by the OneFlorida Clinical Research Consortium ("OneFlorida"). It comprises structured electronic health record (EHR), administrative claims, tumor registry, death, and other data on 17.2 million individuals who received healthcare in Florida between January 2012 and the present. Ten healthcare systems in Miami, Orlando, Tampa, Jacksonville, Tallahassee, Gainesville, and rural areas of Florida contribute EHR data, covering the major metropolitan regions in Florida. Deduplication of patients is accomplished via privacy-preserving entity resolution (precision 0.97-0.99, recall 0.75), thereby linking patients' EHR, claims, and death data. Another unique feature is the establishment of mother-baby relationships via Florida vital statistics data. Research usage has been significant, including major studies launched in the National Patient-Centered Clinical Research Network ("PCORnet"), where OneFlorida is 1 of 9 clinical research networks. The Data Trust's robust, centralized, statewide data are a valuable and relatively unique research resource.
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Affiliation(s)
- William R Hogan
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA,Corresponding Author: William R. Hogan, MD, MS, FACMI, Clinical & Translational Research Building, 2004 Mowry Road, PO Box 100219, Gainesville, FL 32610, USA;
| | - Elizabeth A Shenkman
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | | | | | | | | | - Jiang Bian
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | | | - Christopher Harle
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA,UF Health, Gainesville, Florida, USA
| | | | - Lizabeth Manini
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | - Tona Mendoza
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | - Sonya White
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | - Alex Loiacono
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | - Jackie Hall
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA
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16
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Tang C, Ma J, Zhou L, Plasek J, He Y, Xiong Y, Zhu Y, Huang Y, Bates D. Improving Research Patient Data Repositories from a Health Data Industry Viewpoint (Preprint). J Med Internet Res 2021; 24:e32845. [PMID: 35544299 PMCID: PMC9133984 DOI: 10.2196/32845] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 01/12/2022] [Accepted: 02/07/2022] [Indexed: 11/13/2022] Open
Abstract
Organizational, administrative, and educational challenges in establishing and sustaining biomedical data science infrastructures lead to the inefficient use of Research Patient Data Repositories (RPDRs). The challenges, including but not limited to deployment, sustainability, cost optimization, collaboration, governance, security, rapid response, reliability, stability, scalability, and convenience, restrict each other and may not be naturally alleviated through traditional hardware upgrades or protocol enhancements. This article attempts to borrow data science thinking and practices in the business realm, which we call the data industry viewpoint, to improve RPDRs.
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Affiliation(s)
- Chunlei Tang
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Jing Ma
- Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Sichuan, China
| | - Li Zhou
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Joseph Plasek
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Yuqing He
- School of Economics, Fudan University, Shanghai, China
| | - Yun Xiong
- School of Computer Science, Fudan University, Shanghai, China
| | - Yangyong Zhu
- School of Computer Science, Fudan University, Shanghai, China
| | - Yajun Huang
- School of Economics, Fudan University, Shanghai, China
| | - David Bates
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
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17
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He W, Kirchoff KG, Sampson RR, McGhee KK, Cates AM, Obeid JS, Lenert LA. Research Integrated Network of Systems (RINS): a virtual data warehouse for the acceleration of translational research. J Am Med Inform Assoc 2021; 28:1440-1450. [PMID: 33729486 DOI: 10.1093/jamia/ocab023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 01/28/2021] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Integrated, real-time data are crucial to evaluate translational efforts to accelerate innovation into care. Too often, however, needed data are fragmented in disparate systems. The South Carolina Clinical & Translational Research Institute at the Medical University of South Carolina (MUSC) developed and implemented a universal study identifier-the Research Master Identifier (RMID)-for tracking research studies across disparate systems and a data warehouse-inspired model-the Research Integrated Network of Systems (RINS)-for integrating data from those systems. MATERIALS AND METHODS In 2017, MUSC began requiring the use of RMIDs in informatics systems that support human subject studies. We developed a web-based tool to create RMIDs and application programming interfaces to synchronize research records and visualize linkages to protocols across systems. Selected data from these disparate systems were extracted and merged nightly into an enterprise data mart, and performance dashboards were created to monitor key translational processes. RESULTS Within 4 years, 5513 RMIDs were created. Among these were 726 (13%) bridged systems needed to evaluate research study performance, and 982 (18%) linked to the electronic health records, enabling patient-level reporting. DISCUSSION Barriers posed by data fragmentation to assessment of program impact have largely been eliminated at MUSC through the requirement for an RMID, its distribution via RINS to disparate systems, and mapping of system-level data to a single integrated data mart. CONCLUSION By applying data warehousing principles to federate data at the "study" level, the RINS project reduced data fragmentation and promoted research systems integration.
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Affiliation(s)
- Wenjun He
- College of Medicine, South Carolina Clinical & Translational Research Institute, Medical University of South Carolina, Charleston, SC, USA
| | - Katie G Kirchoff
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA
| | - Royce R Sampson
- College of Medicine, South Carolina Clinical & Translational Research Institute, Medical University of South Carolina, Charleston, SC, USA.,Department of Psychiatry & Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Kimberly K McGhee
- College of Medicine, South Carolina Clinical & Translational Research Institute, Medical University of South Carolina, Charleston, SC, USA.,Academic Affairs Faculty, Medical University of South Carolina, Charleston, SC, USA
| | - Andrew M Cates
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA
| | - Jihad S Obeid
- College of Medicine, South Carolina Clinical & Translational Research Institute, Medical University of South Carolina, Charleston, SC, USA.,Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA.,Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Leslie A Lenert
- College of Medicine, South Carolina Clinical & Translational Research Institute, Medical University of South Carolina, Charleston, SC, USA.,Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA.,Department of Medicine, Medical University of South Carolina, Charleston, SC, USA
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