No-Show Modeling for Adult Ambulatory Clinics

Ayten Turkcan (), Lynn Nuti (), Po-Ching DeLaurentis (), Zhiyi Tian (), Joanne Daggy (), Lingsong Zhang (), Mark Lawley () and Laura Sands ()
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Ayten Turkcan: Northeastern University
Lynn Nuti: Purdue University
Po-Ching DeLaurentis: Purdue University
Zhiyi Tian: Purdue University
Joanne Daggy: Indiana University School of Medicine
Lingsong Zhang: Purdue University
Mark Lawley: Purdue University
Laura Sands: Purdue University

Chapter Chapter 10 in Handbook of Healthcare Operations Management, 2013, pp 251-288 from Springer

Abstract: Abstract Patient no-show is a pervasive problem in outpatient clinics. This chapter provides a literature review and discussion on how to develop statistical no-show models. The literature review is a structured and representative selection of research studies from a variety of medical areas. The literature is grouped into four classes. The first class covers self-reported reasons for no-show. The most common self-reported reasons are forgetting, conflicts, transportation, scheduling system problems, and physical or mental illness. The second class discusses the effect of no-show interventions such as appointment reminders, patient education, and changes in scheduling systems on no-show behavior. The third class develops statistical models of no-show behavior in a variety of settings. Several patient, provider, and clinic characteristics are considered in developing these models. The last class of literature considers the impact of no-shows on health outcomes, which illustrates the importance of no-show modeling. The second part of the chapter explains how statistical no-show models can be developed. The data requirements, determination of significant factors, development of logistic regression models, and model validation are explained in detail. An example no-show model is provided to illustrate the modeling and validation process. The chapter concludes with summarizing thoughts and a discussion of future research opportunities.

Keywords: Human Immunodeficiency Virus; Primary Care Patient; Human Immunodeficiency Virus Patient; Poor Glycemic Control; Health Information Exchange (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/978-1-4614-5885-2_10

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