ArticleAnnals of GIS2022
GIS-Automated Delineation of Hospital Service Areas in Florida: From Dartmouth Method to Network Community Detection Methods.
Article in Annals of GIS, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed.
- Physics-Informed Graph Learning for Spatially Contiguous and Capacity-Constrained Hospital Service Area Delineation.Computers, environment and urban systems · 2026Article
- Exploring cross-city hospital visiting trips for medical service evaluation in urban agglomeration areas based on automobile navigation data.BMC health services research · 2026Article
- Study on edge villages based on cross-region healthcare-seeking behavior.International journal of health geographics · 2025Article
- Application of Community Detection Methods to Identify Emergency General Surgery-Specific Regional Networks.JAMA network open · 2024Article
- The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?Entropy (Basel, Switzerland) · 2022Article
- Delineation of Cancer Service Areas Anchored by Major Cancer Centers in the United States.Cancer research communications · 2022Article
- GIS-Automated Delineation of Hospital Service Areas in Florida: From Dartmouth Method to Network Community Detection Methods.Annals of GIS · 2022Article
- Understanding Factors Influencing Geographic Variation in Healthcare Expenditures: A Small Areas Analysis Study.Inquiry : a journal of medical care organization, provision and financingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Since the Dartmouth hospital service areas (HSAs) were proposed three decades ago, there has been a large body of work using the unit in examining the geographic variation in health care in the U.S. for evaluating health care system performance and informing health policy. However, many studies question the replicability and reliability of the Dartmouth HSAs in meeting the challenges of ever-changing and a diverse set of health care services. This research develops a reproducible, automated, and efficient GIS tool to implement Dartmouth method for defining HSAs. Moreover, the research adapts two popular network community detection methods to account for spatial constraints for defining HSAs that are scale flexible and optimize an important property such as maximum service flows within HSAs. A case study based on the state inpatient database in Florida from the Healthcare Cost and Utilization Project is used to evaluate the efficiency and effectiveness of the methods. The study represents a major step toward developing HSA delineation methods that are computationally efficient, adaptable for various scales (from a local region to as large as a national market), and automated without a steep learning curve for public health professionals.
Indexed as
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What OpenQuestion holds
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.