Clinical Risk Prediction Tools for Hospitalization-related Outcomes Based on Administrative Health Data: A Mini Narrative Review

Agnieszka Mazurek

doi:10.21697/ejhp.0112.02

Published: 2026-06-30

Vol. 6 No. 1 (2026)

Clinical Risk Prediction Tools for Hospitalization-related Outcomes Based on Administrative Health Data: A Mini Narrative Review

Agnieszka Mazurek

European Journal of Health Policy, Humanization of Care and Medical Ethics

Section: Articles

https://doi.org/10.21697/ejhp.0112.02

Abstract

Risk prediction tools for hospitalization-related outcomes can be used clinically for care planning and operationally for resource allocation and quality-of-care evaluation. Administrative health data, collected routinely for billing, claims, statistics, or quality monitoring, can serve as an inexpensive and generalizable source of data for the development of risk prediction tools. The purpose of this narrative review was to evaluate tools developed from administrative health data and designed primarily to predict hospitalization-related outcomes. PubMed, Web of Science, and Scopus were searched for studies published between January 1, 2016 and April 17, 2026. Ultimately, 10 studies met the criteria for inclusion. Most tools were developed using logistic regression and converted into risk scores. One study implemented a random forest model as a web-based calculator. In general, the tools predicting in-hospital mortality showed the best discrimination, whereas readmission tools had the worst performance. Although tools based on administrative data may be valuable for risk stratification, further prospective clinical validation and an assessment of potential bias are needed before routine implementation.

Keywords:

risk stratification, hospitalization outcomes, administrative data, logistic regression;, risk scores

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