Applied Methods for Estimating Transition Probabilities from Electronic Health Record Data

Patricia J. Rodriguez, Zachary J. Ward, Michael W. Long, S. Bryn Austin and Davene R. Wright
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Patricia J. Rodriguez: Comparative Health Outcomes, Policy, and Economics Institute, University of Washington, Seattle, WA, USA
Zachary J. Ward: Center for Health Decision Science, Harvard T. H. Chan School of Public Health, Boston, MA, USA
Michael W. Long: Department of Prevention and Community Health, Milken Institute School of Public Health, George Washington University, Washington, DC, USA
S. Bryn Austin: Department of Social and Behavioral Sciences, Harvard T. H. Chan School of Public Health, Boston, MA, USA
Davene R. Wright: Comparative Health Outcomes, Policy, and Economics Institute, University of Washington, Seattle, WA, USA

Medical Decision Making, 2021, vol. 41, issue 2, 143-152

Abstract: Background Electronic health record (EHR) data contain longitudinal patient information and standardized diagnostic codes. EHR data may be useful for estimating transition probabilities for state-transition models, but no guidelines exist on appropriate methods. We applied 3 potential methods to estimate transition probabilities from EHR data, using pediatric eating disorders (EDs) as a case study. Methods We obtained EHR data from PEDsnet, which includes 8 US children’s hospitals. Data included inpatient, outpatient, and emergency department visits for all patients with an ED. We mapped diagnoses to 3 ED health states: anorexia nervosa, bulimia nervosa, and other specified feeding or eating disorder. We estimated 1-y transition probabilities for males and females using 3 approaches: simple first-last proportions, a multistate Markov (MSM) model, and independent survival models. Results Transition probability estimates varied widely between approaches. The first-last proportion approach estimated higher probabilities of remaining in the same health state, while the MSM and independent survival approaches estimated higher probabilities of transitioning to a different health state. All estimates differed substantially from published literature. Limitations As a source of health state information, EHR data are incomplete and sometimes inaccurate. EHR data were especially challenging for EDs, limiting the estimation and interpretation of transition probabilities. Conclusions The 3 approaches produced very different transition probability estimates. Estimates varied considerably from published literature and were rescaled and calibrated for use in a microsimulation model. Estimation of transition probabilities from EHR data may be more promising for diseases that are well documented in the EHR. Furthermore, clinicians and health systems should work to improve documentation of ED in the EHR. Further research is needed on methods for using EHR data to inform transition probabilities.

Keywords: electronic health record data; Markov model; microsimulation; survival analysis; state-transition models (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:41:y:2021:i:2:p:143-152

DOI: 10.1177/0272989X20985752

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