TY - JOUR
T1 - Connecting community-delivered evidence-based programs and the healthcare system
T2 - Piloting a learning “wellcare” system
AU - Redmond, Sarah
AU - Leppin, Aaron L.
AU - Fischer, Karen
AU - Hanson, Gregory
AU - Doubeni, Chyke
AU - Takahashi, Paul
N1 - Funding Information:
National Center for Advancing Translational Sciences (NCATS), Grant/Award Number: CTSA Grant Number UL1 TR002377; Mayo Clinic Division of Community Internal Medicine Funding information
Funding Information:
This project was supported by the Mayo Clinic Division of Community Internal Medicine and by CTSA Grant Number UL1 TR002377 from the National Center for Advancing Translational Sciences (NCATS). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
Funding Information:
This project was supported by the Mayo Clinic Division of Community Internal Medicine and by CTSA Grant Number UL1 TR002377 from the National Center for Advancing Translational Sciences (NCATS). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
Publisher Copyright:
© 2020 The Authors. Learning Health Systems published by Wiley Periodicals LLC on behalf of University of Michigan.
PY - 2021/10
Y1 - 2021/10
N2 - Introduction: Many evidence-based programs (EBPs) have been determined in randomized controlled trials to be effective, but few studies explore the real-world effectiveness of EBPs implemented in the natural community setting. Our study evaluated whether a novel linked infrastructure would enable such insights and continuous improvement as part of a learning healthcare-community bridged “wellcare” ecosystem. Methods: We created a secure, web-based data entry and storage platform with a network of Minnesota community-based organizations to record EBP participants' demographics and attendance, and program details. We then linked participant's information to their Rochester Epidemiology Project (REP) medical records. With this infrastructure, we conducted a proof of concept, retrospective cohort study by matching EBP participants to REP controls and comparing medical record-documented outcomes over 1 year follow-up. Results: We successfully linked EBP participant records with medical records in 77.6% of cases, and the infrastructure proved feasible and scalable. Still, key challenges remain in obtaining participant consent for data sharing. Upfront resource investments and the availability of REP-like warehouses limit generalizability. Optimal learning will be improved by enhancements that better track program fidelity. Our pilot study established a proof-of-concept, but sample sizes (n = 99 for falls prevention and n = 97 chronic disease/pain management EBP completers) were too small to detect significant differences in hospital admittance as compared to matched controls for either EBP group, (OR = 0.66[0.36, 1.19]) and (OR = 0.81[0.43, 1.54]), respectively. Events were too rare to gather meaningful information about effects on fall rates. Conclusions: Our pilot demonstrates the feasibility of developing an online infrastructure that connects information from community leaders with medical record documented health outcomes, bridging the knowledge gap between community programs and the health care system. Insights gleaned from our infrastructure can be used to continuously shape community program delivery to reduce the need for formal health care services.
AB - Introduction: Many evidence-based programs (EBPs) have been determined in randomized controlled trials to be effective, but few studies explore the real-world effectiveness of EBPs implemented in the natural community setting. Our study evaluated whether a novel linked infrastructure would enable such insights and continuous improvement as part of a learning healthcare-community bridged “wellcare” ecosystem. Methods: We created a secure, web-based data entry and storage platform with a network of Minnesota community-based organizations to record EBP participants' demographics and attendance, and program details. We then linked participant's information to their Rochester Epidemiology Project (REP) medical records. With this infrastructure, we conducted a proof of concept, retrospective cohort study by matching EBP participants to REP controls and comparing medical record-documented outcomes over 1 year follow-up. Results: We successfully linked EBP participant records with medical records in 77.6% of cases, and the infrastructure proved feasible and scalable. Still, key challenges remain in obtaining participant consent for data sharing. Upfront resource investments and the availability of REP-like warehouses limit generalizability. Optimal learning will be improved by enhancements that better track program fidelity. Our pilot study established a proof-of-concept, but sample sizes (n = 99 for falls prevention and n = 97 chronic disease/pain management EBP completers) were too small to detect significant differences in hospital admittance as compared to matched controls for either EBP group, (OR = 0.66[0.36, 1.19]) and (OR = 0.81[0.43, 1.54]), respectively. Events were too rare to gather meaningful information about effects on fall rates. Conclusions: Our pilot demonstrates the feasibility of developing an online infrastructure that connects information from community leaders with medical record documented health outcomes, bridging the knowledge gap between community programs and the health care system. Insights gleaned from our infrastructure can be used to continuously shape community program delivery to reduce the need for formal health care services.
KW - community
KW - evidence-based program
KW - learning healthcare
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U2 - 10.1002/lrh2.10240
DO - 10.1002/lrh2.10240
M3 - Article
AN - SCOPUS:85089490914
SN - 2379-6146
VL - 5
JO - Learning Health Systems
JF - Learning Health Systems
IS - 4
M1 - e10240
ER -