ArticleJAMIA open2025
Transforming appeal decisions: machine learning triage for hospital admission denials.
Article in JAMIA open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in prior authorization and coverage decisions: A systematic review of methods, evidence gaps, and future implications for patient access.Journal of managed care & specialty pharmacy · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.