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Prediction of appointment no-shows using electronic health records

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  • Qiaohui Lin
  • Brenda Betancourt
  • Benjamin A. Goldstein
  • Rebecca C. Steorts

Abstract

Appointment no-shows have a negative impact on patient health and have caused substantial loss in resources and revenue for health care systems. Intervention strategies to reduce no-show rates can be more effective if targeted to the subpopulations of patients with higher risk of not showing to their appointments. We use electronic health records (EHR) from a large medical center to predict no-show patients based on demographic and health care features. We apply sparse Bayesian modeling approaches based on Lasso and automatic relevance determination to predict and identify the most relevant risk factors of no-show patients at a provider level.

Suggested Citation

  • Qiaohui Lin & Brenda Betancourt & Benjamin A. Goldstein & Rebecca C. Steorts, 2020. "Prediction of appointment no-shows using electronic health records," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(7), pages 1220-1234, May.
    • Handle: RePEc:taf:japsta:v:47:y:2020:i:7:p:1220-1234
    DOI: 10.1080/02664763.2019.1672631
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