ArticleScientific reports2024
Prediction of non emergent acute care utilization and cost among patients receiving Medicaid.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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
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Who cites it
7 citing papers in PubMed.
- Comparing Alternative Approaches to Care Management Prioritization: A Prospective Comparative Cohort Study of Acute Care Utilization and Equity Among Medicaid Beneficiaries.Health services research · 2026Article
- Article
- Enhancing Medicaid Social Risk Identification: Comparing Linear and Non-Linear Machine Learning Models with Expert Heuristics.IEEE International Conference on Digital Health. IEEE International Conference on Digital Health · 2026Article
- Reinforcement Learning to Prevent Acute Care Events Among Medicaid Populations: Mixed Methods Study.JMIR AI · 2025Article
- Optimizing AI solutions for population health in primary care.NPJ digital medicine · 2025Article
- Predicting quality measure completion among 14 million low-income patients enrolled in medicaid.NPJ digital medicine · 2025Article
- Procedural Prescription Denials and Risk of Acute Care Utilization and Spending Among Medicaid Patients.JAMA network open · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Patients receiving Medicaid often experience social risk factors for poor health and limited access to primary care, leading to high utilization of emergency departments and hospitals (acute care) for non-emergent conditions. As programs proactively outreach Medicaid patients to offer primary care, they rely on risk models historically limited by poor-quality data. Following initiatives to improve data quality and collect data on social risk, we tested alternative widely-debated strategies to improve Medicaid risk models. Among a sample of 10 million patients receiving Medicaid from 26 states and Washington DC, the best-performing model tripled the probability of prospectively identifying at-risk patients versus a standard model (sensitivity 11.3% [95% CI 10.5, 12.1%] vs 3.4% [95% CI 3.0, 4.0%]), without increasing "false positives" that reduce efficiency of outreach (specificity 99.8% [95% CI 99.6, 99.9%] vs 99.5% [95% CI 99.4, 99.7%]), and with a ~ tenfold improved coefficient of determination when predicting costs (R
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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.