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Applied Methods for Estimating Transition Probabilities from Electronic Health Record Data

Author

Listed:
  • 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
    Division of Adolescent and Young Adult Medicine, Boston Children’s Hospital, Boston, MA, USA)

  • Davene R. Wright

    (Comparative Health Outcomes, Policy, and Economics Institute, University of Washington, Seattle, WA, USA
    Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA)

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.

Suggested Citation

  • Patricia J. Rodriguez & Zachary J. Ward & Michael W. Long & S. Bryn Austin & Davene R. Wright, 2021. "Applied Methods for Estimating Transition Probabilities from Electronic Health Record Data," Medical Decision Making, , vol. 41(2), pages 143-152, February.
    • Handle: RePEc:sae:medema:v:41:y:2021:i:2:p:143-152
    DOI: 10.1177/0272989X20985752
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    References listed on IDEAS

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    1. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629.
    2. Drummond, Michael F. & Sculpher, Mark J. & Claxton, Karl & Stoddart, Greg L. & Torrance, George W., 2015. "Methods for the Economic Evaluation of Health Care Programmes," OUP Catalogue, Oxford University Press, edition 4, number 9780199665884.
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    1. Fernando Alarid-Escudero & Eline Krijkamp & Eva A. Enns & Alan Yang & M. G. Myriam Hunink & Petros Pechlivanoglou & Hawre Jalal, 2023. "A Tutorial on Time-Dependent Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example," Medical Decision Making, , vol. 43(1), pages 21-41, January.

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