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Gazzarata R, Almeida J, Lindsköld L, Cangioli G, Gaeta E, Fico G, Chronaki CE. HL7 Fast Healthcare Interoperability Resources (HL7 FHIR) in digital healthcare ecosystems for chronic disease management: Scoping review. Int J Med Inform 2024; 189:105507. [PMID: 38870885 DOI: 10.1016/j.ijmedinf.2024.105507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 05/14/2024] [Accepted: 05/27/2024] [Indexed: 06/15/2024]
Abstract
BACKGROUND The prevalence of chronic diseases has shifted the burden of disease from incidental acute inpatient admissions to long-term coordinated care across healthcare institutions and the patient's home. Digital healthcare ecosystems emerge to target increasing healthcare costs and invest in standard Application Programming Interfaces (API), such as HL7 Fast Healthcare Interoperability Resources (HL7 FHIR) for trusted data flows. OBJECTIVES This scoping review assessed the role and impact of HL7 FHIR and associated Implementation Guides (IGs) in digital healthcare ecosystems focusing on chronic disease management. METHODS To study trends and developments relevant to HL7 FHIR, a scoping review of the scientific and gray English literature from 2017 to 2023 was used. RESULTS The selection of 93 of 524 scientific papers reviewed in English indicates that the popularity of HL7 FHIR as a robust technical interface standard for the health sector has been steadily rising since its inception in 2010, reaching a peak in 2021. Digital Health applications use HL7 FHIR in cancer (45 %), cardiovascular disease (CVD) (more than 15 %), and diabetes (almost 15 %). The scoping review revealed that references to HL7 FHIR IGs are limited to ∼ 20 % of articles reviewed. HL7 FHIR R4 was most frequently referenced when the HL7 FHIR version was mentioned. In HL7 FHIR IGs registries and the internet, we found 35 HL7 FHIR IGs addressing chronic disease management, i.e., cancer (40 %), chronic disease management (25 %), and diabetes (20 %). HL7 FHIR IGs frequently complement the information in the article. CONCLUSIONS HL7 FHIR matures with each revision of the standard as HL7 FHIR IGs are developed with validated data sets, common shared HL7 FHIR resources, and supporting tools. Referencing HL7 FHIR IGs cataloged in official registries and in scientific publications is recommended to advance data quality and facilitate mutual learning in growing digital healthcare ecosystems that nurture interoperability in digital health innovation.
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Affiliation(s)
- Roberta Gazzarata
- HL7 Europe Foundation, 38-40 Square de Meeus, Brussels, 1000, Belgium; Healthropy Srl, Corso Vittorio Veneto 14B, Savona, 17100, Italy.
| | - Joao Almeida
- HL7 Europe Foundation, 38-40 Square de Meeus, Brussels, 1000, Belgium; MEDCIDS - Faculty of Medicine of University of Porto, Porto, Portugal; PDH - Pharma Data Hub, Porto, Portugal.
| | - Lars Lindsköld
- European Federation for Medical Informatics, Ch de Maillefer 37, CH-1052 Le Mont-sur-Lausanne, Switzerland; SciLifeLab Datacenter, University of Uppsala, S-752 37 Uppsala, Sweden.
| | - Giorgio Cangioli
- HL7 Europe Foundation, 38-40 Square de Meeus, Brussels, 1000, Belgium.
| | - Eugenio Gaeta
- Life Supporting Technologies, Universidad Politécnica de Madrid, Avenida Complutense 30, 28040 Madrid, Spain.
| | - Giuseppe Fico
- Life Supporting Technologies, Universidad Politécnica de Madrid, Avenida Complutense 30, 28040 Madrid, Spain.
| | - Catherine E Chronaki
- HL7 Europe Foundation, 38-40 Square de Meeus, Brussels, 1000, Belgium; European Federation for Medical Informatics, Ch de Maillefer 37, CH-1052 Le Mont-sur-Lausanne, Switzerland.
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Marshall DA, Hua N, Buchanan J, Christensen KD, Frederix GWJ, Goranitis I, Ijzerman M, Jansen JP, Lavelle TA, Regier DA, Smith HS, Ungar WJ, Weymann D, Wordsworth S, Phillips KA. Paving the path for implementation of clinical genomic sequencing globally: Are we ready? HEALTH AFFAIRS SCHOLAR 2024; 2:qxae053. [PMID: 38783891 PMCID: PMC11115369 DOI: 10.1093/haschl/qxae053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/18/2024] [Accepted: 04/25/2024] [Indexed: 05/25/2024]
Abstract
Despite the emerging evidence in recent years, successful implementation of clinical genomic sequencing (CGS) remains limited and is challenged by a range of barriers. These include a lack of standardized practices, limited economic assessments for specific indications, limited meaningful patient engagement in health policy decision-making, and the associated costs and resource demand for implementation. Although CGS is gradually becoming more available and accessible worldwide, large variations and disparities remain, and reflections on the lessons learned for successful implementation are sparse. In this commentary, members of the Global Economics and Evaluation of Clinical Genomics Sequencing Working Group (GEECS) describe the global landscape of CGS in the context of health economics and policy and propose evidence-based solutions to address existing and future barriers to CGS implementation. The topics discussed are reflected as two overarching themes: (1) system readiness for CGS and (2) evidence, assessments, and approval processes. These themes highlight the need for health economics, public health, and infrastructure and operational considerations; a robust patient- and family-centered evidence base on CGS outcomes; and a comprehensive, collaborative, interdisciplinary approach.
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Affiliation(s)
- Deborah A Marshall
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta T2N 4Z6, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta T2N 4N1, Canada
| | - Nicolle Hua
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta T2N 4Z6, Canada
| | - James Buchanan
- Health Economics and Policy Research Unit, Centre for Evaluation and Methods, Wolfson Institute of Population Health, Queen Mary University of London, London E1 2AB, United Kingdom
| | - Kurt D Christensen
- PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215, United States
| | - Geert W J Frederix
- Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3584 CG Utrecht, The Netherlands
| | - Ilias Goranitis
- Health Economics Unit, Centre for Health Policy, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria 3010, Australia
- Australian Genomics, Parkville, Victoria 3052, Australia
| | - Maarten Ijzerman
- University of Melbourne Centre for Cancer Research, University of Melbourne, Melbourne, Victoria 3000, Australia
- Erasmus School of Health Policy & Management, Eramus University Rotterdam, 3062 PA Rotterdam, The Netherlands
| | - Jeroen P Jansen
- Center for Translational and Policy Research on Precision Medicine (TRANSPERS), Department of Clinical Pharmacy, School of Pharmacy, University of California, San Francisco, San Francisco, CA 94158, United States
| | - Tara A Lavelle
- Center for the Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA 02111, United States
| | - Dean A Regier
- Canadian Centre for Applied Research in Cancer Control, Cancer Control Research, BC Cancer Research Institute, Vancouver, British Columbia V5Z 1L3, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
| | - Hadley S Smith
- PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215, United States
| | - Wendy J Ungar
- Program of Child Health Evaluative Sciences, The Hospital for Sick Children Research Institute, Toronto, Ontario M5G 0A4, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario M5T 3M6, Canada
| | - Deirdre Weymann
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
- Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
| | - Sarah Wordsworth
- Health Economics Research Centre, Nuffield Department of Population Health and NIHR Biomedical Research Centre, University of Oxford, Oxford OX3 7LF, United Kingdom
| | - Kathryn A Phillips
- Center for Translational and Policy Research on Precision Medicine (TRANSPERS), Department of Clinical Pharmacy, School of Pharmacy, University of California, San Francisco, San Francisco, CA 94158, United States
- Health Affairs Scholar Emerging & Global Health Policy, Health Affairs, Washington, DC 20036, United States
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Walton NA, Nagarajan R, Wang C, Sincan M, Freimuth RR, Everman DB, Walton DC, McGrath SP, Lemas DJ, Benos PV, Alekseyenko AV, Song Q, Gamsiz Uzun E, Taylor CO, Uzun A, Person TN, Rappoport N, Zhao Z, Williams MS. Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup. J Am Med Inform Assoc 2024; 31:536-541. [PMID: 38037121 PMCID: PMC10797281 DOI: 10.1093/jamia/ocad211] [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: 02/11/2023] [Revised: 10/09/2023] [Accepted: 10/26/2023] [Indexed: 12/02/2023] Open
Abstract
OBJECTIVE Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space. PROCESS A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members. CONCLUSIONS Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making.
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Affiliation(s)
- Nephi A Walton
- Division of Medical Genetics, University of Utah School of Medicine, Salt Lake City, UT 84112 ,United States
| | - Radha Nagarajan
- Enterprise Information Services, Cedars-Sinai Medical Center, Los Angeles, CA 90025, United States
- Information Services Department, Children’s Hospital of Orange County, Orange, CA 92868, United States
| | - Chen Wang
- Division of Computational Biology, Department of Quantitative Health Sciences, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - Murat Sincan
- Flatiron Health, New York, NY 10013, United States
- Department of Internal Medicine, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD 57107, United States
| | - Robert R Freimuth
- Department of Artificial Intelligence and Informatics, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - David B Everman
- EverMed Genetics and Genomics Consulting LLC, Greenville, SC 29607, United States
| | | | - Scott P McGrath
- CITRIS Health, CITRIS and Banatao Institute, University of California Berkeley, Berkeley, CA 94720, United States
| | - Dominick J Lemas
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32610, United States
| | - Panayiotis V Benos
- Department of Epidemiology, University of Florida, Gainesville, FL 32610, United States
| | - Alexander V Alekseyenko
- Department of Public Health Sciences, Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC 29403, United States
| | - Qianqian Song
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32610, United States
| | - Ece Gamsiz Uzun
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Providence, RI 02915, United States
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI 02915, United States
| | - Casey Overby Taylor
- Departments of Medicine and Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21205, United States
| | - Alper Uzun
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI 02915, United States
- Legorreta Cancer Center, Brown University, Providence, RI 02915, United States
| | - Thomas Nate Person
- Department of Bioinformatics and Genomics, Huck Institutes of the Life Sciences, Penn State University, Bloomsburg, PA 16802, United States
| | - Nadav Rappoport
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Marc S Williams
- Department of Genomic Health, Geisinger, Danville, PA 17822, United States
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Nan J, Xu LQ. Designing Interoperable Health Care Services Based on Fast Healthcare Interoperability Resources: Literature Review. JMIR Med Inform 2023; 11:e44842. [PMID: 37603388 PMCID: PMC10477925 DOI: 10.2196/44842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 04/07/2023] [Accepted: 07/10/2023] [Indexed: 08/22/2023] Open
Abstract
BACKGROUND With the advent of the digital economy and the aging population, the demand for diversified health care services and innovative care delivery models has been overwhelming. This trend has accelerated the urgency to implement effective and efficient data exchange and service interoperability, which underpins coordinated care services among tiered health care institutions, improves the quality of oversight of regulators, and provides vast and comprehensive data collection to support clinical medicine and health economics research, thus improving the overall service quality and patient satisfaction. To meet this demand and facilitate the interoperability of IT systems of stakeholders, after years of preparation, Health Level 7 formally introduced, in 2014, the Fast Healthcare Interoperability Resources (FHIR) standard. It has since continued to evolve. FHIR depends on the Implementation Guide (IG) to ensure feasibility and consistency while developing an interoperable health care service. The IG defines rules with associated documentation on how FHIR resources are used to tackle a particular problem. However, a gap remains between IGs and the process of building actual services because IGs are rules without specifying concrete methods, procedures, or tools. Thus, stakeholders may feel it nontrivial to participate in the ecosystem, giving rise to the need for a more actionable practice guideline (PG) for promoting FHIR's fast adoption. OBJECTIVE This study aimed to propose a general FHIR PG to facilitate stakeholders in the health care ecosystem to understand FHIR and quickly develop interoperable health care services. METHODS We selected a collection of FHIR-related papers about the latest studies or use cases on designing and building FHIR-based interoperable health care services and tagged each use case as belonging to 1 of the 3 dominant innovation feature groups that are also associated with practice stages, that is, data standardization, data management, and data integration. Next, we reviewed each group's detailed process and key techniques to build respective care services and collate a complete FHIR PG. Finally, as an example, we arbitrarily selected a use case outside the scope of the reviewed papers and mapped it back to the FHIR PG to demonstrate the effectiveness and generalizability of the PG. RESULTS The FHIR PG includes 2 core elements: one is a practice design that defines the responsibilities of stakeholders and outlines the complete procedure from data to services, and the other is a development architecture for practice design, which lists the available tools for each practice step and provides direct and actionable recommendations. CONCLUSIONS The FHIR PG can bridge the gap between IGs and the process of building actual services by proposing actionable methods, procedures, and tools. It assists stakeholders in identifying participants' roles, managing the scope of responsibilities, and developing relevant modules, thus helping promote FHIR-based interoperable health care services.
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Affiliation(s)
- Jingwen Nan
- Health IT Research, China Mobile (Chengdu) Industrial Research Institute, Chengdu, China
| | - Li-Qun Xu
- Health IT Research, China Mobile (Chengdu) Industrial Research Institute, Chengdu, China
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5
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Sreejith R, Senthil S. Smart Contract Authentication assisted GraphMap-Based HL7 FHIR architecture for interoperable e-healthcare system. Heliyon 2023; 9:e15180. [PMID: 37089400 PMCID: PMC10114202 DOI: 10.1016/j.heliyon.2023.e15180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/28/2023] [Accepted: 03/29/2023] [Indexed: 04/05/2023] Open
Abstract
The exponential growth in the global population and significant advancements in healthcare broadened the scope of intervention for e-Healthcare through decentralized data access and information exchange, making complex clinical decisions. e-Healthcare can perform several functionalities, including EHR communication, telemedicine, and complex clinical decision systems (CCDS), but large-scale users still find it challenging to maintain interoperability, stability, and scalability. Accommodating an extensive array of stakeholders, which includes patients, doctors, hospitals, and laboratories, demands interoperability to serve scalable services. FHIR frameworks have played a vital role in e-Healthcare designs. Most of the existing HL7-FHIR frameworks have used REST-API using HTTP-query for CRUD tasks that impose numerous rules and constraints, making the process more complex and time-consuming, violating the quality-of-service (QoS) standards on different levels. This paper develops a novel, robust Smart-Contract Authentication Assisted HL7-FHIR framework toward an interoperable e-Healthcare solution. Unlike classical REST API-based FHIR, our proposed method applies a Graph-mapping concept that transforms each resource variable into an equivalent Graph-Mapped Data Structure (GMS), which is subsequently stored in the NoSQL MongoDB database, reducing computational costs and time to meet QoS demands. The proposed model employs three key components, GMS-driven HL7 FHIR Gateway Model, Smart Contract Authentication and Client Model. The Smart Contract function helped verify and authenticate users to ensure privacy and secure EHR exchange. The assessment of the performance of the proposed model reveals a significant reduction in computational time with optimal resource utilization making it a significant and viable option to better the real-world e-Healthcare mechanisms.
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6
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Dunn T, Cosgun E. A cloud-based pipeline for analysis of FHIR and long-read data. BIOINFORMATICS ADVANCES 2023; 3:vbac095. [PMID: 36726729 PMCID: PMC9872570 DOI: 10.1093/bioadv/vbac095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/02/2022] [Accepted: 01/19/2023] [Indexed: 01/22/2023]
Abstract
Motivation As genome sequencing becomes cheaper and more accurate, it is becoming increasingly viable to merge this data with electronic health information to inform clinical decisions. Results In this work, we demonstrate a full pipeline for working with both PacBio sequencing data and clinical FHIR® data, from initial data to tertiary analysis. The electronic health records are stored in FHIR® (Fast Healthcare Interoperability Resource) format, the current leading standard for healthcare data exchange. For the genomic data, we perform variant calling on long-read PacBio HiFi data using Cromwell on Azure. Both data formats are parsed, processed and merged in a single scalable pipeline which securely performs tertiary analyses using cloud-based Jupyter notebooks. We include three example applications: exporting patient information to a database, clustering patients and performing a simple pharmacogenomic study. Availability and implementation https://github.com/microsoft/genomicsnotebook/tree/main/fhirgenomics. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Tim Dunn
- To whom correspondence should be addressed.
| | - Erdal Cosgun
- Biomedical Platforms and Genomics, Microsoft Research, Redmond, WA 98052, USA
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Goar W, Babb L, Chamala S, Cline M, Freimuth RR, Hart RK, Kuzma K, Lee J, Nelson T, Prlić A, Riehle K, Smith A, Stahl K, Yates AD, Rehm HL, Wagner AH. Development and application of a computable genotype model in the GA4GH Variation Representation Specification. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2023; 28:383-394. [PMID: 36540993 PMCID: PMC9782714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
As the diversity of genomic variation data increases with our growing understanding of the role of variation in health and disease, it is critical to develop standards for precise inter-system exchange of these data for research and clinical applications. The Global Alliance for Genomics and Health (GA4GH) Variation Representation Specification (VRS) meets this need through a technical terminology and information model for disambiguating and concisely representing variation concepts. Here we discuss the recent Genotype model in VRS, which may be used to represent the allelic composition of a genetic locus. We demonstrate the use of the Genotype model and the constituent Haplotype model for the precise and interoperable representation of pharmacogenomic diplotypes, HGVS variants, and VCF records using VRS and discuss how this can be leveraged to enable interoperable exchange and search operations between assayed variation and genomic knowledgebases.
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Affiliation(s)
- Wesley Goar
- Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
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Wiley K, Findley L, Goldrich M, Rakhra-Burris TK, Stevens A, Williams P, Bult CJ, Chisholm R, Deverka P, Ginsburg GS, Green ED, Jarvik G, Mensah GA, Ramos E, Relling MV, Roden DM, Rowley R, Alterovitz G, Aronson S, Bastarache L, Cimino JJ, Crowgey EL, Del Fiol G, Freimuth RR, Hoffman MA, Jeff J, Johnson K, Kawamoto K, Madhavan S, Mendonca EA, Ohno-Machado L, Pratap S, Taylor CO, Ritchie MD, Walton N, Weng C, Zayas-Cabán T, Manolio TA, Williams MS. A research agenda to support the development and implementation of genomics-based clinical informatics tools and resources. J Am Med Inform Assoc 2022; 29:1342-1349. [PMID: 35485600 PMCID: PMC9277642 DOI: 10.1093/jamia/ocac057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/22/2022] [Accepted: 04/08/2022] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVE The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled "Developing a Clinical Genomic Informatics Research Agenda". The meeting's goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings. MATERIALS AND METHODS Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy. RESULTS Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address. DISCUSSION Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them.
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Affiliation(s)
- Ken Wiley
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Laura Findley
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Madison Goldrich
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Tejinder K Rakhra-Burris
- Department of Medicine, Center for Applied Genomics & Precision Medicine, Duke University, Durham, North Carolina, USA
| | - Ana Stevens
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Pamela Williams
- Department of Medicine, Center for Applied Genomics & Precision Medicine, Duke University, Durham, North Carolina, USA
| | | | - Rex Chisholm
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Patricia Deverka
- Center for Translational and Policy Research in Precision Medicine, University of California at San Francisco, San Francisco, California, USA
| | - Geoffrey S Ginsburg
- All of Us Research Program, National Institutes of Health, Bethesda, Maryland, USA
| | - Eric D Green
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Gail Jarvik
- Division of Medical Genetics, University of Washington, Seattle, Washington, USA
| | - George A Mensah
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Erin Ramos
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Mary V Relling
- Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Dan M Roden
- Departments of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Robb Rowley
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Gil Alterovitz
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Samuel Aronson
- Mass General Brigham, Research Information Sciences and Computing, Somerville, Massachusetts, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - James J Cimino
- Heersink School of Medicine, University of Alabama at Birmingham, Alabama, USA
| | | | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Robert R Freimuth
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark A Hoffman
- School of Medicine, Children's Mercy Hospital Kansas City, University of Missouri Kansas City, Lees Summit, Missouri, USA
| | | | - Kevin Johnson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics, Georgetown University, Washington, District of Columbia, USA
| | - Eneida A Mendonca
- Regenstrief Institute, Inc., Indianapolis, Indiana, USA.,Department of Pediatrics, Department of Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Lucila Ohno-Machado
- Department of Biomedical Informatics, University of California San Diego, La Jolla, California, USA
| | - Siddharth Pratap
- Bioinformatics Core, Meharry Medical College, Nashville, Tennessee, USA
| | | | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, Institute for Biomedical Informatics, Penn Center for Precision Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Nephi Walton
- Intermountain Precision Genomics, Intermountain Healthcare, St George, Utah, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Teresa Zayas-Cabán
- National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Teri A Manolio
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Marc S Williams
- Geisinger, Genomic Medicine Institute, Danville, Pennsylvania, USA
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9
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Duda SN, Kennedy N, Conway D, Cheng AC, Nguyen V, Zayas-Cabán T, Harris PA. HL7 FHIR-based tools and initiatives to support clinical research: a scoping review. J Am Med Inform Assoc 2022; 29:1642-1653. [PMID: 35818340 DOI: 10.1093/jamia/ocac105] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 05/23/2022] [Accepted: 06/20/2022] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVES The HL7® fast healthcare interoperability resources (FHIR®) specification has emerged as the leading interoperability standard for the exchange of healthcare data. We conducted a scoping review to identify trends and gaps in the use of FHIR for clinical research. MATERIALS AND METHODS We reviewed published literature, federally funded project databases, application websites, and other sources to discover FHIR-based papers, projects, and tools (collectively, "FHIR projects") available to support clinical research activities. RESULTS Our search identified 203 different FHIR projects applicable to clinical research. Most were associated with preparations to conduct research, such as data mapping to and from FHIR formats (n = 66, 32.5%) and managing ontologies with FHIR (n = 30, 14.8%), or post-study data activities, such as sharing data using repositories or registries (n = 24, 11.8%), general research data sharing (n = 23, 11.3%), and management of genomic data (n = 21, 10.3%). With the exception of phenotyping (n = 19, 9.4%), fewer FHIR-based projects focused on needs within the clinical research process itself. DISCUSSION Funding and usage of FHIR-enabled solutions for research are expanding, but most projects appear focused on establishing data pipelines and linking clinical systems such as electronic health records, patient-facing data systems, and registries, possibly due to the relative newness of FHIR and the incentives for FHIR integration in health information systems. Fewer FHIR projects were associated with research-only activities. CONCLUSION The FHIR standard is becoming an essential component of the clinical research enterprise. To develop FHIR's full potential for clinical research, funding and operational stakeholders should address gaps in FHIR-based research tools and methods.
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Affiliation(s)
- Stephany N Duda
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Nan Kennedy
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Douglas Conway
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Alex C Cheng
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Viet Nguyen
- Stratametrics LLC, Salt Lake City, Utah, USA.,HL7 Da Vinci Project, Ann Arbor, Michigan, USA
| | - Teresa Zayas-Cabán
- National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Paul A Harris
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
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Hernandez S, Fairchild K, Pemberton M, Dahmer J, Zhang W, Palchuk MB, Topaloglu U. Applying FHIR Genomics for Research - From Sequencing to Database. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2022; 2022:236-243. [PMID: 35854733 PMCID: PMC9285172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/16/2023]
Abstract
The availability of next-generation sequencing (NGS) technologies and their continually declining costs have resulted in the accumulation of large genomic data sets. NGS results have traditionally been delivered in PDF format, and in some cases, structured data, e.g., XML or JSON formats, are also made available, but there is a lack of uniformity around the profiling of external vendor testing platforms. Atrium Health Wake Forest Baptist and TriNetX have harmonized and mapped genomic data to FHIR Genomic standards and imported it into the TriNetX database through a data pipeline. This process is translatable to other sequencing platforms and to other institutions. The addition of genotypic data to the TriNetX database to the reservoir of phenotypic data will promote enhanced industry trial recruitment, (ii) comprehensive intra-institutional genomic benchmarking/quality improvement, and eventually (iii) sweeping inter-institutional genomic research and treatment paradigm shifts.
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Affiliation(s)
| | - Karen Fairchild
- TriNetX, LLC, Cambridge, MA
- HL7 Clinical Genomics Working Group, Ann Arbor, MI
| | - Mark Pemberton
- Information Technology Services, Atrium Health Wake Forest Baptist, Winston-Salem, NC
| | | | - Wei Zhang
- Wake Forest School of Medicine, Winston-Salem, NC
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11
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Wake DT, Smith DM, Kazi S, Dunnenberger HM. Pharmacogenomic Clinical Decision Support: A Review, How-to Guide, and Future Vision. Clin Pharmacol Ther 2021; 112:44-57. [PMID: 34365648 PMCID: PMC9291515 DOI: 10.1002/cpt.2387] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 07/28/2021] [Indexed: 02/06/2023]
Abstract
Clinical decision support (CDS) is an essential part of any pharmacogenomics (PGx) implementation. Increasingly, institutions have implemented CDS tools in the clinical setting to bring PGx data into patient care, and several have published their experiences with these implementations. However, barriers remain that limit the ability of some programs to create CDS tools to fit their PGx needs. Therefore, the purpose of this review is to summarize the types, functions, and limitations of PGx CDS currently in practice. Then, we provide an approachable step‐by‐step how‐to guide with a case example to help implementers bring PGx to the front lines of care regardless of their setting. Particular focus is paid to the five “rights” of CDS as a core around designing PGx CDS tools. Finally, we conclude with a discussion of opportunities and areas of growth for PGx CDS.
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Affiliation(s)
- Dyson T Wake
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA
| | - D Max Smith
- MedStar Health, Columbia, Maryland, USA.,Georgetown University Medical Center, Washington, DC, USA
| | - Sadaf Kazi
- Georgetown University Medical Center, Washington, DC, USA.,National Center for Human Factors in Healthcare, MedStar Health Research Institute Washington, Washington, DC, USA
| | - Henry M Dunnenberger
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA
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12
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Genetic testing in ambulatory cardiology clinics reveals high rate of findings with clinical management implications. Genet Med 2021; 23:2404-2414. [PMID: 34363016 DOI: 10.1038/s41436-021-01294-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 07/19/2021] [Accepted: 07/20/2021] [Indexed: 11/08/2022] Open
Abstract
PURPOSE Cardiovascular disease (CVD) is the leading cause of death in adults in the United States, yet the benefits of genetic testing are not universally accepted. METHODS We developed the "HeartCare" panel of genes associated with CVD, evaluating high-penetrance Mendelian conditions, coronary artery disease (CAD) polygenic risk, LPA gene polymorphisms, and specific pharmacogenetic (PGx) variants. We enrolled 709 individuals from cardiology clinics at Baylor College of Medicine, and samples were analyzed in a CAP/CLIA-certified laboratory. Results were returned to the ordering physician and uploaded to the electronic medical record. RESULTS Notably, 32% of patients had a genetic finding with clinical management implications, even after excluding PGx results, including 9% who were molecularly diagnosed with a Mendelian condition. Among surveyed physicians, 84% reported medical management changes based on these results, including specialist referrals, cardiac tests, and medication changes. LPA polymorphisms and high polygenic risk of CAD were found in 20% and 9% of patients, respectively, leading to diet, lifestyle, and other changes. Warfarin and simvastatin pharmacogenetic variants were present in roughly half of the cohort. CONCLUSION Our results support the use of genetic information in routine cardiovascular health management and provide a roadmap for accompanying research.
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