Transforming appeal decisions: machine learning triage for hospital admission denials

Abstract

Objective: To develop and validate a machine learning model that helps physician advisors efficiently identify hospital admission denials likely to be overturned on appeal. Materials: Analysis of 2473 appealed hospital admission denials with known outcomes, split 90:10 for training and testing. Methods: Six binary classifier models were trained and evaluated using accuracy, precision, recall, and F1 score metrics. Results: An elastic net logistic regression model was selected based on computational efficiency and optimal performance with 84% accuracy, 84% precision, 98% recall, and an F1 score of 0.9. Discussion: The predictive model addresses the risk of physician advisors accepting inappropriate denials due to biased perceptions of appeal success. Model implementation improved denial screening efficiency and was a key feature of a more successful appeal strategy. Conclusions: By addressing data quality problems inherent to electronic health data, and expanding the feature space, machine learning can be an effective tool in the healthcare provider space.