SynthesisBMC health services research2025
Predictors of rural hospital closures in the United States: a systematic review and call for AI-driven early warning systems.
Synthesis in BMC health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
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.
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Who cites it
2 citing papers in PubMed.
- Why are rural hospitals closing in the U.S.? Predictors identified using explainable machine learning.BMC health services research · 2026Article
- Evaluating non-linear thresholds and synergistic interactions in the comorbidity of preoperative insomnia and emergence agitation: a SHAP-based machine learning study.Frontiers in neurology · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRural hospitals in the U.S. are closing at an alarming rate, threatening healthcare access and economic stability in underserved communities. While numerous studies have explored the causes and impacts of these closures, a predictive framework to proactively identify at-risk hospitals early remains underdeveloped.
objectiveThis systematic review examines the financial, workforce, policy-related, economic, and demographic pressures, as well as market dynamics that contribute to rural hospital closures and evaluates the extent to which current research supports the use of AI-driven early warning systems to predict and prevent future closures.
methodsA systematic literature search was conducted across PubMed, Embase, CINAHL Plus, and Scopus. Studies published between 2013 and 2024 were screened based on predefined inclusion and exclusion criteria. A total of 15 studies were included and analyzed using a narrative synthesis framework following PRISMA guidelines.
resultsThe review identified five primary categories contributing to rural hospital closures: financial distress, workforce shortages, unfavorable policy environments, adverse economic and demographic conditions, and market competition. Most studies employed traditional statistical models and used retrospective data to analyze these issues. None of the studies applied ML models to identify these factors. However, there is a significant gap in the availability of real-time tools that can identify contributing factors, predict closures in advance, and guide timely interventions.
conclusionDespite the increasing potential of AI, no studies included in this review have applied ML techniques to predict or prevent rural hospital closures. These findings highlight a critical need for developing real-time, AI-driven early warning systems, such as the proposed Rural Health Control Tower (RHCT), to continuously monitor multi-source data, issue dynamic risk alerts, and support proactive decision-making. The predictors and data sources identified in this review offer a foundation for developing such models, which could improve the timeliness of interventions and promote the sustainability of rural healthcare. Future research should prioritize the development of interpretable, equitable, and community-informed AI tools that are accessible to all. CLINICAL TRIAL NUMBER: Not applicable
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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.