{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T09:06:41Z","timestamp":1789376801723,"version":"build-2803163510"},"reference-count":50,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["P30AG097158"],"award-info":[{"award-number":["P30AG097158"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["RF1AG083175"],"award-info":[{"award-number":["RF1AG083175"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["agsjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J American Geriatrics Society"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Hospitals are increasingly adopting artificial intelligence and machine learning (AI\/ML) tools to support clinical decision making and care management. However, evidence on how hospital AI\/ML adoption relates to inpatient utilization and spending among clinically complex populations remains limited. Older adults with Alzheimer's disease and related dementias (ADRD) experience higher rates of readmissions and potentially avoidable hospitalizations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Cross\u2010sectional study was conducted using 2023 inpatient claims linked to the Medicare Beneficiary Summary File and the American Hospital Association Annual Survey Information Technology Supplement to examine associations between hospital adoption of patient\u2010related AI\/ML tools and inpatient utilization and spending among Medicare fee\u2010for\u2010service (FFS) beneficiaries with ADRD. The study included 340,509 FFS beneficiaries aged 65\u2009years or older with ADRD who experienced at least one inpatient hospitalization in 2023. Hospital adoption of patient\u2010related AI\/ML tools was measured using four indicators reflecting AI\/ML use to predict inpatient health risks, identify high\u2010risk outpatients, monitor patient health, and recommend treatments. Outcomes included frequent hospitalization, 30\u2010day readmission, preventable acute and chronic hospitalizations, total Medicare payments, and beneficiary out\u2010of\u2010pocket (OOP) spending. Multivariable regression models adjusted for beneficiary and hospital characteristics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Greater hospital adoption of patient\u2010related AI\/ML tools was associated with lower odds of frequent hospitalizations, 30\u2010day readmissions, and preventable acute hospitalizations. Inpatient risk prediction and high\u2010risk outpatient identification tools were consistently associated with lower inpatient utilization. Inpatient risk prediction was associated with lower total Medicare spending, while high\u2010risk outpatient identification was associated with higher Medicare spending. Treatment recommendation tools were associated with higher beneficiary OOP spending.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Among Medicare FFS beneficiaries with ADRD, hospital adoption of patient\u2010related AI\/ML tools, particularly those focused on risk prediction and high\u2010risk patient identification, was associated with lower inpatient utilization without increasing overall spending. Heterogeneity across AI\/ML tool types suggests the importance of evaluating AI\/ML tools based on their specific clinical functions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jgs.70704","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T08:42:27Z","timestamp":1789375347000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hospital Artificial Intelligence Tools and Inpatient Utilization and Costs in Older Adults with Alzheimer's Disease and Related Dementias"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0826-6530","authenticated-orcid":false,"given":"Seyeon","family":"Jang","sequence":"first","affiliation":[{"name":"Department of Health Policy and Management University of Maryland School of Public Health  College Park Maryland USA"},{"name":"Center on Aging University of Maryland  College Park Maryland USA"},{"name":"Center for Seniors Uniting Nationwide to Support Health, INtegrated Care, and Economics (SUNSHINE) University of Maryland  College Park Maryland USA"},{"name":"Hospital and Public Health interdisciPlinarY Research (HAPPY) Lab University of Maryland School of Public Health  College Park Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Health Policy and Management University of Maryland School of Public Health  College Park Maryland USA"},{"name":"Center on Aging University of Maryland  College Park Maryland USA"},{"name":"Center for Seniors Uniting Nationwide to Support Health, INtegrated Care, and Economics (SUNSHINE) University of Maryland  College Park Maryland USA"},{"name":"Hospital and Public Health interdisciPlinarY Research (HAPPY) Lab University of Maryland School of Public Health  College Park Maryland 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