Designing risk prediction models for ambulatory no-shows across different specialties and clinics

Xiruo Ding, Ziad F. Gellad, Chad Mather, Pamela Barth, Eric G. Poon, Mark Newman, Benjamin A. Goldstein

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Objective: As available data increases, so does the opportunity to develop risk scores on more refined patient populations. In this paper we assessed the ability to derive a risk score for a patient no-showing to a clinic visit. Methods: Using data from 2 264 235 outpatient appointments we assessed the performance of models built across 14 different specialties and 55 clinics. We used regularized logistic regression models to fit and assess models built on the health system, specialty, and clinic levels. We evaluated fits based on their discrimination and calibration. Results: Overall, the results suggest that a relatively robust risk score for patient no-shows could be derived with an average C-statistic of 0.83 across clinic level models and strong calibration. Moreover, the clinic specific models, even with lower training set sizes, often performed better than the more general models. Examination of the individual models showed that risk factors had different degrees of predictability across the different specialties. Implementation of optimal modeling strategies would lead to capturing an additional 4819 no-shows per-year. Conclusion: Overall, this work highlights both the opportunity for and the importance of leveraging the available electronic health record data to develop more refined risk models.

Original languageEnglish
Pages (from-to)924-930
Number of pages7
JournalJournal of the American Medical Informatics Association
Volume25
Issue number8
DOIs
StatePublished - Aug 1 2018

Bibliographical note

Funding Information:
BAG was supported by National Institute of Diabetes and Digestive and Kidney Diseases grant K25DK097279. ZFG was funded by Veterans Affairs Health Services Research and Development Career Development Award CDA 14-158. This publication was made possible (in part) by Grant Number UL 1TR001117 from the National Center for Advancing Translational Sciences (NCATS), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NCATS or NIH.

Publisher Copyright:
© The Author(s) 2018. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Keywords

  • Clinical decision making
  • Electronic health records
  • Model comparison
  • Predictive model

ASJC Scopus subject areas

  • Health Informatics

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