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Johnson D, Del Fiol G, Kawamoto K, Romagnoli KM, Sanders N, Isaacson G, Jenkins E, Williams MS. Genetically guided precision medicine clinical decision support tools: a systematic review. J Am Med Inform Assoc 2024; 31:1183-1194. [PMID: 38558013 PMCID: PMC11031215 DOI: 10.1093/jamia/ocae033] [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: 08/17/2023] [Revised: 02/06/2024] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
OBJECTIVES Patient care using genetics presents complex challenges. Clinical decision support (CDS) tools are a potential solution because they provide patient-specific risk assessments and/or recommendations at the point of care. This systematic review evaluated the literature on CDS systems which have been implemented to support genetically guided precision medicine (GPM). MATERIALS AND METHODS A comprehensive search was conducted in MEDLINE and Embase, encompassing January 1, 2011-March 14, 2023. The review included primary English peer-reviewed research articles studying humans, focused on the use of computers to guide clinical decision-making and delivering genetically guided, patient-specific assessments, and/or recommendations to healthcare providers and/or patients. RESULTS The search yielded 3832 unique articles. After screening, 41 articles were identified that met the inclusion criteria. Alerts and reminders were the most common form of CDS used. About 27 systems were integrated with the electronic health record; 2 of those used standards-based approaches for genomic data transfer. Three studies used a framework to analyze the implementation strategy. DISCUSSION Findings include limited use of standards-based approaches for genomic data transfer, system evaluations that do not employ formal frameworks, and inconsistencies in the methodologies used to assess genetic CDS systems and their impact on patient outcomes. CONCLUSION We recommend that future research on CDS system implementation for genetically GPM should focus on implementing more CDS systems, utilization of standards-based approaches, user-centered design, exploration of alternative forms of CDS interventions, and use of formal frameworks to systematically evaluate genetic CDS systems and their effects on patient care.
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Affiliation(s)
- Darren Johnson
- Department of Genomic Health, Geisinger Health Systems, Danville, PA 17822, United States
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, United States
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, United States
| | - Katrina M Romagnoli
- Department of Genomic Health, Geisinger Health Systems, Danville, PA 17822, United States
| | - Nathan Sanders
- School of Medicine, Geisinger Health Systems, Danville, PA 17822, United States
| | - Grace Isaacson
- Family Medicine, Penn Highlands Healthcare, DuBois, PA 16830, United States
| | - Elden Jenkins
- School of Medicine, Noorda College of Osteopathic Medicine, Provo, UT 84606, United States
| | - Marc S Williams
- Department of Genomic Health, Geisinger Health Systems, Danville, PA 17822, 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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Lau-Min KS, McKenna D, Asher SB, Bardakjian T, Wollack C, Bleznuck J, Biros D, Anantharajah A, Clark DF, Condit C, Ebrahimzadeh JE, Long JM, Powers J, Raper A, Schoenbaum A, Feldman M, Steinfeld L, Tuteja S, VanZandbergen C, Domchek SM, Ritchie MD, Landgraf J, Chen J, Nathanson KL. Impact of integrating genomic data into the electronic health record on genetics care delivery. Genet Med 2022; 24:2338-2350. [PMID: 36107166 PMCID: PMC10176082 DOI: 10.1016/j.gim.2022.08.009] [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: 02/18/2022] [Revised: 08/15/2022] [Accepted: 08/15/2022] [Indexed: 11/30/2022] Open
Abstract
PURPOSE Integrating genomic data into the electronic health record (EHR) is key for optimally delivering genomic medicine. METHODS The PennChart Genomics Initiative (PGI) at the University of Pennsylvania is a multidisciplinary collaborative that has successfully linked orders and results from genetic testing laboratories with discrete genetic data in the EHR. We quantified the use of the genomic data within the EHR, performed a time study with genetic counselors, and conducted key informant interviews with PGI members to evaluate the effect of the PGI's efforts on genetics care delivery. RESULTS The PGI has interfaced with 4 genetic testing laboratories, resulting in the creation of 420 unique computerized genetic testing orders that have been used 4073 times to date. In a time study of 96 genetic testing activities, EHR use was associated with significant reductions in time spent ordering (2 vs 8 minutes, P < .001) and managing (1 vs 5 minutes, P < .001) genetic results compared with the use of online laboratory-specific portals. In key informant interviews, multidisciplinary collaboration and institutional buy-in were identified as key ingredients for the PGI's success. CONCLUSION The PGI's efforts to integrate genomic medicine into the EHR have substantially streamlined the delivery of genomic medicine.
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Affiliation(s)
- Kelsey S Lau-Min
- Division of Hematology and Oncology, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Danielle McKenna
- Division of Hematology and Oncology, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Stephanie Byers Asher
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Tanya Bardakjian
- Department of Neurology, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Colin Wollack
- Information Services, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Joseph Bleznuck
- Information Services, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Daniel Biros
- Information Services, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Arravinth Anantharajah
- Division of Hematology and Oncology, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Dana F Clark
- Division of Hematology and Oncology, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Courtney Condit
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jessica E Ebrahimzadeh
- Division of Hematology and Oncology, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jessica M Long
- Division of Hematology and Oncology, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jacquelyn Powers
- Division of Hematology and Oncology, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Anna Raper
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Anna Schoenbaum
- Information Services, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Michael Feldman
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | | | - Sony Tuteja
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | | | - Susan M Domchek
- Division of Hematology and Oncology, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA; Abramson Cancer Center, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jeffrey Landgraf
- Information Services, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jessica Chen
- Information Services, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Katherine L Nathanson
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA; Abramson Cancer Center, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA.
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