Designing risk prediction models for ambulatory no-shows across different specialties and clinics. (9th February 2018)

• Record Type: Journal Article
• Authors: Ding, Xiruo; Gellad, Ziad F; Mather, Chad; Barth, Pamela; Poon, Eric G; Newman, Mark; Goldstein, Benjamin A
• Is Part Of: Journal of the American Medical Informatics Association. Volume 25:Number 8(2018)
• Page Start: 924
• Page End: 930
• Publication Date: 2018-02-09
• Subjects: predictive model -- clinical decision making -- model comparison -- electronic health records; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocy002
• Languages: English
• ISSNs: 1067-5027

Abstract

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.