ArticleHealth economics2022
Comparing risk adjustment estimation methods under data availability constraints.
Article in Health economics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Risk stratification for long-term inpatient costs in mental disorders: a dual-track machine learning approach using baseline EHRs and hospitalization trajectories.BMC health services research · 2026Article
- A Framework for the Design of Risk-Adjustment Models in Health care Provider Payment Systems.Medical care research and review : MCRR · 2025Review
- Risk adjustment for regional healthcare funding allocations with ensemble methods: an empirical study and interpretation.The European journal of health economics : HEPAC : health economics in prevention and care · 2024Article
- Comparing risk adjustment estimation methods under data availability constraints.Health economics · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Italian National Healthcare Service relies on per capita allocation for healthcare funds, despite having a highly detailed and wide range of data to potentially build a complex risk-adjustment formula. However, heterogeneity in data availability limits the development of a national model. This paper implements and ealuates machine learning (ML) and standard risk-adjustment models on different data scenarios that a Region or Country may face, to optimize information with the most predictive model. We show that ML achieves a small but generally statistically insignificant improvement of adjusted R
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Registered trials
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