{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T12:18:32Z","timestamp":1793276312383,"version":"4.5.0"},"reference-count":51,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T00:00:00Z","timestamp":1757462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Access to health care is a key social determinant of health, yet individual experiences of need and barriers\u2014especially in rural and racially diverse regions\u2014are often overlooked. Traditional models may miss complex sociodemographic and household patterns. This study applies machine learning (ML) to examine healthcare needs and access barriers among adults in New Mexico, a diverse state with high service needs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objectives<\/jats:title><jats:p>(1) Identify predictors of self-reported healthcare needs across medical, dental, and mental health domains; (2) determine factors and reasons linked to access barriers; (3) compare performance across seven ML algorithms; and (4) generate interpretable insights to inform interventions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We analyzed survey data from 9,099 adults across 13 New Mexico counties (2019\u20132024). Predictors included sociodemographic, geographic, and household factors. Models\u2014spanning linear, tree-based, kernel-based, and neural networks\u2014were evaluated using recall, F1-score, and area under the precision-recall curve. Interpretability tools included SHAP, partial dependence plots, and permutation importance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>(1) Predictors varied by domain. Mental health needs were linked to younger age, low income, limited family support, and being female. Dental needs were highest among higher-income White parents; medical needs were tied to larger households and parenting status. Family support consistently reduced barriers. (2) Common barriers included cost, wait times, and provider shortages. Hispanic respondents reported fewer mental health barriers. (3) Neural networks and tree-based models performed best (recall up to 0.99). (4) Interpretability methods revealed complex, nonlinear predictor patterns.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>ML models revealed complex, domain-specific patterns of need and access, highlighting the limitations of one-size-fits-all approaches. Community-based initiatives like 100% Community can leverage these insights to target structurally excluded populations and strengthen local support systems. Hyperlocal planning, state-level policy reform, and family-centered interventions are essential to addressing healthcare disparities in high-need settings.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpubh.2025.1659322","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T05:27:52Z","timestamp":1757482072000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A machine learning approach to healthcare needs and barriers using the 100% Community Survey of access to SDOH services"],"prefix":"10.3389","volume":"13","author":[{"given":"Karikarn","family":"Chansiri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julie S.","family":"McCrae","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katherine Ortega","family":"Courtney","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dominic","family":"Cappello","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,9,10]]},"reference":[{"key":"ref1","volume-title":"Mirror, mirror 2021: reflecting poorly","year":"2021"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1186\/s12939-021-01486-3","article-title":"Effects of medicaid expansion on poverty disparities in health insurance coverage","volume":"20","author":"Lin","year":"2021","journal-title":"Int J Equity Health"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"e013384","DOI":"10.1136\/bmjopen-2016-013384","article-title":"Social prescribing: less rhetoric and more reality. 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