Man vs. Machine: Comparing Physician vs. Electronic Health Record–Based Model Predictions for 30-Day Hospital Readmissions

Background Electronic health record (EHR)-based readmission risk prediction models can be automated in real-time but have modest discrimination and may be missing important readmission risk factors. Clinician predictions of readmissions may incorporate information unavailable in the EHR, but the com...

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Bibliographic Details
Published in:Journal of general internal medicine : JGIM 2021-09, Vol.36 (9), p.2555-2562
Main Authors: Nguyen, Oanh Kieu, Washington, Colin, Clark, Christopher R., Miller, Michael E., Patel, Vivek A., Halm, Ethan A., Makam, Anil N.
Format: Article
Language:English
Subjects:
Electronic health records
Electronic medical records
Internal Medicine
Medicine
Medicine & Public Health
Original Research
Prediction models
Predictions
Reclassification
Regional development
Regression models
Risk analysis
Risk factors
Sensitivity
Statistical analysis
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Summary:Background Electronic health record (EHR)-based readmission risk prediction models can be automated in real-time but have modest discrimination and may be missing important readmission risk factors. Clinician predictions of readmissions may incorporate information unavailable in the EHR, but the comparative usefulness is unknown. We sought to compare clinicians versus a validated EHR-based prediction model in predicting 30-day hospital readmissions. Methods We conducted a prospective survey of internal medicine clinicians in an urban safety-net hospital. Clinicians prospectively predicted patients’ 30-day readmission risk on 5-point Likert scales, subsequently dichotomized into low- vs. high-risk. We compared human with machine predictions using discrimination, net reclassification, and diagnostic test characteristics. Observed readmissions were ascertained from a regional hospitalization database. We also developed and assessed a “human-plus-machine” logistic regression model incorporating both human and machine predictions. Results We included 1183 hospitalizations from 106 clinicians, with a readmission rate of 20.8%. Both clinicians and the EHR model had similar discrimination (C-statistic 0.66 vs. 0.66, p  = 0.91). Clinicians had higher specificity (79.0% vs. 48.9%, p  
ISSN:0884-8734
1525-1497
DOI:10.1007/s11606-020-06355-3