The focus of the analysis is the Clinical Resource Hub program, a regionally organized initiative designed to address one of the most persistent operational headaches in large health systems: temporary staffing gaps. Under the program, each of the VHA’s eighteen administrative regions, known as Veterans Integrated Service Networks, hosts a hub that deploys clinical staff to fill vacancies at ambulatory care sites, or spokes, throughout the region. Because each hub operates within a region with its own veteran population, facility mix, and workforce conditions, the program’s outcomes vary considerably across the country. Understanding whether those variations follow recognizable regional patterns could help leaders design and evaluate the program more effectively.

The research team set out to test whether hierarchical agglomerative clustering, a classic unsupervised learning method, could accomplish two things: reveal interpretable regional typologies, and demonstrate face validity for learning about differences in Clinical Resource Hub outcomes. The choice of method matters. Many clustering approaches used in health services research produce groupings that are statistically valid but difficult for operational leaders to interpret or act upon. The authors wanted a technique whose output would align with the intuitions of people who actually manage regional programs, making the results usable rather than merely academic.

The methodological pipeline began with feature selection. The team developed a set of regional characteristics with hypothesized links to variations in the Clinical Resource Hub program, drawing on veteran demographics, health status, and facility and staffing variables already defined in existing VHA data. Veteran-level data were aggregated to the regional level, producing sixteen features that described each region’s population and infrastructure. These drew on the VHA’s Corporate Data Warehouse, the electronic health record, and the Survey of Healthcare Experience of Patients, alongside measures such as the Care Assessment Needs score, which captures patient clinical complexity.

With sixteen standardized regional features in hand, the researchers applied principal component analysis to reduce dimensionality. PCA transforms correlated variables into a smaller set of uncorrelated principal components, each a weighted combination of the original features. By examining the feature loadings, the weights showing how strongly each original variable contributes to a component, the team could interpret what each component represented, for example a dimension capturing rurality and age versus one capturing growth and demographic change. This interpretive step is what separates the study’s approach from black-box clustering: rather than feeding raw variables into an algorithm, the researchers first distilled them into comprehensible axes of regional difference.

Hierarchical agglomerative clustering was then applied to the retained principal components. In HAC, each observation, here a VHA region, begins as its own cluster, and the algorithm iteratively merges the most similar clusters according to a linkage criterion, building a hierarchy from the bottom up. The researchers iteratively tested the face validity of the resulting groupings, checking whether the clusters made sense in light of operational knowledge. The procedure settled on four clusters, four distinct regional profiles. One profile captured regions that were older, sicker, and more rural; another described regions that were younger, growing, and with a larger share of female veterans. The other two profiles occupied intermediate or contrasting positions along these dimensions.

The payoff came when the team compared program outcomes across these clusters. They examined access before and after the Clinical Resource Hub program, using fiscal year 2019 as the pre-program baseline and fiscal year 2023 as the post-program period, and utilization in fiscal year 2023. Apparent differences emerged between clusters. Notably, one cluster displayed both the lowest variation in access scores and the lowest absolute access score, a combination the authors interpret as signaling regional access challenges: consistently low performance across the regions in that group rather than a mix of strong and weak performers. Such a pattern is exactly the kind of signal that could prompt targeted attention from program leadership.

Importantly, the regional typologies produced by the PCA-informed clustering aligned with insights from operational leadership, suggesting the method achieved the interpretability the team sought. This alignment between statistical output and managerial intuition is a meaningful validation for a proof-of-concept study. It indicates that the clustering was not detecting mathematical artifacts but was capturing real, recognizable differences in the populations and conditions that each regional hub serves. For a health system contemplating how to evaluate regionally based programs, that is a promising demonstration that unsupervised learning can support, rather than obscure, practical decision-making.

The study also carries methodological lessons for the broader field of health services research. Large integrated systems increasingly regionalize services to optimize resource allocation and improve efficiency, yet evaluations of such programs often treat regions as interchangeable units or rely on ad hoc groupings. The approach demonstrated here, aggregating patient-level data to regional features, reducing dimensionality with PCA, and clustering with HAC, offers a transparent, reproducible template that other systems could adapt. The authors followed established reporting standards, using the STROBE guidelines for observational studies and the SQUIRE guidelines for quality improvement work, and the evaluation was conducted as part of an ongoing VHA quality improvement effort rather than human subjects research.

The authors are careful to frame the work as a first step rather than a finished solution. They note that future work is needed to confirm the usefulness of this method more generally as an approach for understanding outcome variations across regionally based programs. The study was funded by the Department of Veterans Affairs Veterans Health Administration through the VA Office of Rural Health and the VA Office of Primary Care, with the funders having no role in the design, analysis, or decision to publish. Published open access on 7 October 2026, the article arrives at a moment when health systems everywhere are grappling with workforce shortages and uneven access. If simple, interpretable clustering can help leaders see which regions face structural headwinds, programs like the Clinical Resource Hub can be tuned to meet veterans where they actually are, rather than where an average assumes they are.

Subject of Research: Unsupervised clustering of regional characteristics in the Veterans Health Administration's Clinical Resource Hub program

Article Title: Understanding regional variations in the veterans health administration’s clinical resource hub program using hierarchical agglomerative clustering

Article References: Mayfield, B., Wheat, C., Stockdale, S. E., Rose, D. E., Kath, S., Ford, G., Curtis, I., Nelson, K. M., & Rubenstein, L. V. (2026). Understanding regional variations in the veterans health administration’s clinical resource hub program using hierarchical agglomerative clustering. BMC Health Services Research. https://doi.org/10.1186/s12913-026-15781-8

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15781-8

Keywords: Veterans Health Administration, Clinical Resource Hub, hierarchical agglomerative clustering, principal component analysis, unsupervised machine learning, health services research, regional variation, access to care, program evaluation, veterans health, dimensionality reduction, staffing gaps