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

Publication ,  Journal Article
Ding, X; Gellad, ZF; Mather, C; Barth, P; Poon, EG; Newman, M; Goldstein, BA
Published in: J Am Med Inform Assoc
August 1, 2018

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.

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Published In

J Am Med Inform Assoc

DOI

EISSN

1527-974X

Publication Date

August 1, 2018

Volume

25

Issue

8

Start / End Page

924 / 930

Location

England

Related Subject Headings

  • Risk Assessment
  • Risk
  • Office Visits
  • No-Show Patients
  • Models, Statistical
  • Medicine
  • Medical Informatics
  • Humans
  • Electronic Health Records
  • Ambulatory Care
 

Citation

APA
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ICMJE
MLA
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Ding, X., Gellad, Z. F., Mather, C., Barth, P., Poon, E. G., Newman, M., & Goldstein, B. A. (2018). Designing risk prediction models for ambulatory no-shows across different specialties and clinics. J Am Med Inform Assoc, 25(8), 924–930. https://doi.org/10.1093/jamia/ocy002
Ding, Xiruo, Ziad F. Gellad, Chad Mather, Pamela Barth, Eric G. Poon, Mark Newman, and Benjamin A. Goldstein. “Designing risk prediction models for ambulatory no-shows across different specialties and clinics.J Am Med Inform Assoc 25, no. 8 (August 1, 2018): 924–30. https://doi.org/10.1093/jamia/ocy002.
Ding X, Gellad ZF, Mather C, Barth P, Poon EG, Newman M, et al. Designing risk prediction models for ambulatory no-shows across different specialties and clinics. J Am Med Inform Assoc. 2018 Aug 1;25(8):924–30.
Ding, Xiruo, et al. “Designing risk prediction models for ambulatory no-shows across different specialties and clinics.J Am Med Inform Assoc, vol. 25, no. 8, Aug. 2018, pp. 924–30. Pubmed, doi:10.1093/jamia/ocy002.
Ding X, Gellad ZF, Mather C, Barth P, Poon EG, Newman M, Goldstein BA. Designing risk prediction models for ambulatory no-shows across different specialties and clinics. J Am Med Inform Assoc. 2018 Aug 1;25(8):924–930.
Journal cover image

Published In

J Am Med Inform Assoc

DOI

EISSN

1527-974X

Publication Date

August 1, 2018

Volume

25

Issue

8

Start / End Page

924 / 930

Location

England

Related Subject Headings

  • Risk Assessment
  • Risk
  • Office Visits
  • No-Show Patients
  • Models, Statistical
  • Medicine
  • Medical Informatics
  • Humans
  • Electronic Health Records
  • Ambulatory Care