Prediction of appointment no-shows using electronic health records

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.

KEYWORDS:

• Appointment no-shows
• electronic health data
• Bayessian Lasso
• automatic relevance determination
• sparse Bayesian modeling

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Qiaohui Lin  http://orcid.org/0000-0003-2792-0176

Additional information

Funding

Lin, Betancourt, Goldstein, and Steorts were supported by a seed grant from the Duke Center for Integrative Health at Duke University, Durham, North Carolina