Author
Listed:
- Louise B. Russell
(Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
The Leonard Davis Institute of Health Economics (LDI), University of Pennsylvania, Philadelphia, PA, USA
Center for Health Incentives and Behavioral Economics, University of Pennsylvania, PA, USA)
- Qian Huang
(Center for Health Incentives and Behavioral Economics, University of Pennsylvania, PA, USA)
- Yuqing Lin
(Center for Health Incentives and Behavioral Economics, University of Pennsylvania, PA, USA)
- Laurie A. Norton
(Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Center for Health Incentives and Behavioral Economics, University of Pennsylvania, PA, USA)
- Jingsan Zhu
(Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Center for Health Incentives and Behavioral Economics, University of Pennsylvania, PA, USA)
- L. G. Iannotte
(Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA)
- David A. Asch
(Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
The Leonard Davis Institute of Health Economics (LDI), University of Pennsylvania, Philadelphia, PA, USA
Center for Health Incentives and Behavioral Economics, University of Pennsylvania, PA, USA
Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA)
- Shivan J. Mehta
(Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
The Leonard Davis Institute of Health Economics (LDI), University of Pennsylvania, Philadelphia, PA, USA
Center for Health Incentives and Behavioral Economics, University of Pennsylvania, PA, USA
Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA)
- Monique S. Tanna
(Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA)
- Andrea B. Troxel
(Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA)
- Kevin G. Volpp
(Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
The Leonard Davis Institute of Health Economics (LDI), University of Pennsylvania, Philadelphia, PA, USA
Center for Health Incentives and Behavioral Economics, University of Pennsylvania, PA, USA
Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA)
- Lee R. Goldberg
(Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA)
Abstract
Introduction. Pragmatic clinical trials test interventions in patients representative of real-world medical practice and reduce data collection costs by using data recorded in the electronic health record (EHR) during usual care. We describe our experience using the EHR to measure the primary outcome of a pragmatic trial, hospital readmissions, and important clinical covariates. Methods. The trial enrolled patients recently discharged from the hospital for treatment of heart failure to test whether automated daily monitoring integrated into the EHR could reduce readmissions. The study team used data from the EHR and several data systems that drew on the EHR, supplemented by the hospital admissions files of three states. Results. Almost three-quarters of enrollees’ readmissions over the 12-mo trial period were captured by the EHRs of the study hospitals. State data, which took 7 mo to more than 2 y from first contact to receipt of first data, provided the remaining one-quarter. Considerable expertise was required to resolve differences between the 2 data sources. Common covariates used in trial analyses, such as weight and body mass index during the index hospital stay, were available for >97% of enrollees from the EHR. Ejection fraction, obtained from echocardiograms, was available for only 47.6% of enrollees within the 6-mo window that would likely be expected in a traditional trial . Discussion. In this trial, patient characteristics and outcomes were collected from existing EHR systems, but, as usual for EHRs, they could not be standardized for date or method of measurement and required substantial time and expertise to collect and curate. Hospital admissions, the primary trial outcome, required additional effort to locate and use supplementary sources of data. Highlights Electronic health records are not a single system but a series of overlapping and legacy systems that require time and expertise to use efficiently. Commonly measured patient characteristics such as weight and body mass index are relatively easy to locate for most trial enrollees but less common characteristics, like ejection fraction, are not. Acquiring essential supplementary data—in this trial, state data on hospital admission—can be a lengthy and difficult process.
Suggested Citation
Louise B. Russell & Qian Huang & Yuqing Lin & Laurie A. Norton & Jingsan Zhu & L. G. Iannotte & David A. Asch & Shivan J. Mehta & Monique S. Tanna & Andrea B. Troxel & Kevin G. Volpp & Lee R. Goldberg, 2022.
"The Electronic Health Record as the Primary Data Source in a Pragmatic Trial: A Case Study,"
Medical Decision Making, , vol. 42(8), pages 975-984, November.
Handle:
RePEc:sae:medema:v:42:y:2022:i:8:p:975-984
DOI: 10.1177/0272989X211069980
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