Applying Implementation Science to Advance Electronic Health Record–Driven Learning Health Systems: Case Studies, Challenges, and Recommendations

With the widespread implementation of electronic health records (EHRs), there has been significant progress in developing learning health systems (LHSs) aimed at improving health and health care delivery through rapid and continuous knowledge generation and translation. To support LHSs in achieving...

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Published in:Journal of medical Internet research Vol. 26; no. 8; p. e55472
Main Authors: Trinkley, Katy E, Maw, Anna M, Torres, Cristina Huebner, Huebschmann, Amy G, Glasgow, Russell E
Format: Journal Article
Language:English
Published: Canada Journal of Medical Internet Research 07.10.2024
JMIR Publications
Subjects:
Case studies
Decision support systems
Electronic Health Records
Electronic records
Evidence-based medicine
Health care industry
Health care reform
Humans
Implementation Science
Information management
Learning Health System - methods
Medical informatics
Medical records
Methods
Quality management
Technology application
Viewpoint
United States
information technology
chronic care
learning health systems
electronic health record
research and technology
evidence-based medicine
implementation science
ISSN:1438-8871, 1439-4456, 1438-8871
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Abstract With the widespread implementation of electronic health records (EHRs), there has been significant progress in developing learning health systems (LHSs) aimed at improving health and health care delivery through rapid and continuous knowledge generation and translation. To support LHSs in achieving these goals, implementation science (IS) and its frameworks are increasingly being leveraged to ensure that LHSs are feasible, rapid, iterative, reliable, reproducible, equitable, and sustainable. However, 6 key challenges limit the application of IS to EHR-driven LHSs: barriers to team science, limited IS experience, data and technology limitations, time and resource constraints, the appropriateness of certain IS approaches, and equity considerations. Using 3 case studies from diverse health settings and 1 IS framework, we illustrate these challenges faced by LHSs and offer solutions to overcome the bottlenecks in applying IS and utilizing EHRs, which often stymie LHS progress. We discuss the lessons learned and provide recommendations for future research and practice, including the need for more guidance on the practical application of IS methods and a renewed emphasis on generating and accessing inclusive data.
AbstractList With the widespread implementation of electronic health records (EHRs), there has been significant progress in developing learning health systems (LHSs) aimed at improving health and health care delivery through rapid and continuous knowledge generation and translation. To support LHSs in achieving these goals, implementation science (IS) and its frameworks are increasingly being leveraged to ensure that LHSs are feasible, rapid, iterative, reliable, reproducible, equitable, and sustainable. However, 6 key challenges limit the application of IS to EHR-driven LHSs: barriers to team science, limited IS experience, data and technology limitations, time and resource constraints, the appropriateness of certain IS approaches, and equity considerations. Using 3 case studies from diverse health settings and 1 IS framework, we illustrate these challenges faced by LHSs and offer solutions to overcome the bottlenecks in applying IS and utilizing EHRs, which often stymie LHS progress. We discuss the lessons learned and provide recommendations for future research and practice, including the need for more guidance on the practical application of IS methods and a renewed emphasis on generating and accessing inclusive data.
With the widespread implementation of electronic health records (EHRs), there has been significant progress in developing learning health systems (LHSs) aimed at improving health and health care delivery through rapid and continuous knowledge generation and translation. To support LHSs in achieving these goals, implementation science (IS) and its frameworks are increasingly being leveraged to ensure that LHSs are feasible, rapid, iterative, reliable, reproducible, equitable, and sustainable. However, 6 key challenges limit the application of IS to EHR-driven LHSs: barriers to team science, limited IS experience, data and technology limitations, time and resource constraints, the appropriateness of certain IS approaches, and equity considerations. Using 3 case studies from diverse health settings and 1 IS framework, we illustrate these challenges faced by LHSs and offer solutions to overcome the bottlenecks in applying IS and utilizing EHRs, which often stymie LHS progress. We discuss the lessons learned and provide recommendations for future research and practice, including the need for more guidance on the practical application of IS methods and a renewed emphasis on generating and accessing inclusive data.With the widespread implementation of electronic health records (EHRs), there has been significant progress in developing learning health systems (LHSs) aimed at improving health and health care delivery through rapid and continuous knowledge generation and translation. To support LHSs in achieving these goals, implementation science (IS) and its frameworks are increasingly being leveraged to ensure that LHSs are feasible, rapid, iterative, reliable, reproducible, equitable, and sustainable. However, 6 key challenges limit the application of IS to EHR-driven LHSs: barriers to team science, limited IS experience, data and technology limitations, time and resource constraints, the appropriateness of certain IS approaches, and equity considerations. Using 3 case studies from diverse health settings and 1 IS framework, we illustrate these challenges faced by LHSs and offer solutions to overcome the bottlenecks in applying IS and utilizing EHRs, which often stymie LHS progress. We discuss the lessons learned and provide recommendations for future research and practice, including the need for more guidance on the practical application of IS methods and a renewed emphasis on generating and accessing inclusive data.
Audience Academic
Author Maw, Anna M
Huebschmann, Amy G
Glasgow, Russell E
Trinkley, Katy E
Torres, Cristina Huebner
AuthorAffiliation 3 Department of Biomedical Informatics School of Medicine University of Colorado Anschutz Medical Campus Aurora, CO United States
2 Adult and Child Center for Outcomes Research and Delivery Science Center University of Colorado Anschutz Medical Campus Aurora, CO United States
9 VA Eastern Colorado Geriatric Research Education and Clinical Center Aurora, CO United States
5 Division of Hospital Medicine School of Medicine University of Colorado Anschutz Medical Campus Aurora, CO United States
1 Department of Family Medicine School of Medicine University of Colorado Anschutz Medical Campus Aurora, CO United States
8 Ludeman Family Center for Women’s Health Research University of Colorado Anschutz Medical Campus Aurora, CO United States
6 Caring Health Center Springfield, MA United States
7 Division of General Internal Medicine School of Medicine University of Colorado Anschutz Medical Campus Aurora, CO United States
4 Colorado Center for Personalized Medicine School of Medicine University of Colo
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Copyright Katy E Trinkley, Anna M Maw, Cristina Huebner Torres, Amy G Huebschmann, Russell E Glasgow. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 07.10.2024.
COPYRIGHT 2024 Journal of Medical Internet Research
Katy E Trinkley, Anna M Maw, Cristina Huebner Torres, Amy G Huebschmann, Russell E Glasgow. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 07.10.2024. 2024
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Keywords information technology
chronic care
learning health systems
electronic health record
research and technology
evidence-based medicine
implementation science
Language English
License Katy E Trinkley, Anna M Maw, Cristina Huebner Torres, Amy G Huebschmann, Russell E Glasgow. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 07.10.2024.
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Snippet With the widespread implementation of electronic health records (EHRs), there has been significant progress in developing learning health systems (LHSs) aimed...
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SubjectTerms Case studies
Decision support systems
Electronic Health Records
Electronic records
Evidence-based medicine
Health care industry
Health care reform
Humans
Implementation Science
Information management
Learning Health System - methods
Medical informatics
Medical records
Methods
Quality management
Technology application
Viewpoint
Title Applying Implementation Science to Advance Electronic Health Record–Driven Learning Health Systems: Case Studies, Challenges, and Recommendations
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