An empirical investigation into factors affecting patient cancellations and no-shows at outpatient clinics

  • Authors:

  • John B. Norris;Chetan Kumar;Suresh Chand;Herbert Moskowitz;Steve A. Shade;Deanna R. Willis

  • Affiliations:

  • Purdue University, Krannert Graduate School of Management, 403 West State Street, West Lafayette, IN 47907-2056, USA;California State University San Marcos, College of Business Administration, Department of Information Systems and Operations Management, 333 South Twin Oaks Valley Road, San Marcos, CA 92096, USA;Purdue University, Krannert Graduate School of Management, 403 West State Street, West Lafayette, IN 47907-2056, USA;Purdue University, Krannert Graduate School of Management, 403 West State Street, West Lafayette, IN 47907-2056, USA;Purdue University, Krannert Graduate School of Management, 403 West State Street, West Lafayette, IN 47907-2056, USA;Indiana University, University Hospital, Indianapolis, IN 46202, USA

  • Venue:
  • Decision Support Systems
  • Year:
  • 2014

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Abstract

Medical facilities competing in the US Healthcare system must consider the likelihood of patient attendance when scheduling appointments. This paper analyzes a robust, panel style registration data set from 9 outpatient facilities consisting of 5years of patients' attendance outcomes. The three outcomes, arrivals, cancellations prior to the scheduled appointment and failure to arrive (no-shows), distinguish this paper from prior empirical research that typically treats patient arrivals as a dichotomous outcome by grouping cancellations and no-shows together or excluding cancellations. Distinguishing cancellations from no-shows reveal different effects from patient age and appointment slot day and time. Findings focus on the variables having the greatest impact on attendance and conclude with the difficulty in predicting individual appointment outcomes and the observation that a rather small number of patients represent a disproportionately large percentage of no-shows. Four factors that have the greatest association with patient nonattendance are lead time (call appointment interval), financial payer (typically insurance provider), patient age, and the patient's prior attendance history. Lead time has the greatest impact and is the most addressable, whereas a patient's age, insurance provider and, to some extent, patient behavior cannot be altered. Results reveal quite a paradox that scheduling systems designed to help ensure full utilization on a future date also contribute to underutilization by increasing the chance that patients will not show.