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 imp

SUBMITTER: Owolabi T 

PROVIDER: S-EPMC11854074 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature