{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T17:48:04Z","timestamp":1782755284084,"version":"3.54.5"},"reference-count":98,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T00:00:00Z","timestamp":1782691200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Building on prior development work, the objective of this study of health care utilization of all Weill Cornell Medicine health insurance beneficiaries over a six-year period was to demonstrate the validity of the Charlson Comorbidity Health Analytics (CCHA), a summed weighted measure of 38 chronic conditions in adults and children, that prospectively predict longitudinal risk of hospital admissions, repeated admissions and resultant high cost in populations. The objective of the Charlson Comorbidity Health Analytics (CCHA) is to provide a new foundational framework for population management strategies by identifying the highest risk patients who can then be the focus for interventions designed to reduce unplanned hospitalizations and resultant high costs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>All 27,190 Weill Cornell Medicine beneficiaries in the years 2016\u20132021, that is, employees and their dependents, including spouses\/partners and their children, were linked across the years in a de-identified way, and CCHA was calculated from claims data. In addition to basic demographics, data included all outpatient and inpatient claims, including payments for each service over each year, excluding pharmacy. While two pharmaceuticals are part of the CCHA (anticoagulants and anti-psychotics), no data about pharmaceuticals was available for this analysis. First, CCHA from each year 2016\u20132021 was evaluated cross-sectionally as a predictor of that year\u2019s hospitalizations and costs. Second, the CCHA from 2016 beneficiaries who were followed for five years were used to predict longitudinal risk of hospitalizations, repeated hospitalizations, and costs in each of the next five years. Then the CCHA was compared to the CMS Chronic Conditions Warehouse 30 (CCW30) measure. Finally, the CCHA from any given year (2016\u20132021) was analyzed for its predictive ability over the remaining one to five years of follow-up to predict hospitalizations, repeated hospitalizations, and costs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of the total 27,190 beneficiaries over the six years, 55.8% were employees (66.2% women with an average age of 40.9 years), and 25.7% children (average age of 6.1 years). The Charlson Comorbidity Health Analytics (CCHA) score from an index year longitudinally predicts the risk of hospitalizations--including repeated hospitalizations--which drive healthcare costsover six years (p\u2009&lt;\u2009.01), providing the foundation for interventions in the highest risk patients. Moreover, the 2016 CCHA was a more significant predictor of readmission in 2017\u20132021 than a 2016 admission. In addition, comorbidity from any index year can be used to predict subsequent admissions and costs; therefore, it works in dynamic populations, like employers and unions that have changes in beneficiaries over time.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusions and Relevance<\/jats:title>\n                    <jats:p>The Charlson Comorbidity Health Analytics is a method for prospectively identifying the small percent of patients who are at high longitudinal risk for unplanned hospitalizations and high costs. Intervention efforts can then be focused on high-comorbidity patients at high risk [1], with the goal of preventing health deterioration leading to health crises. Comorbidity Health Analytics provides a new foundational framework for population management strategies and specifically for interventions designed to reduce unplanned hospitalizations and thereby reduce costs.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0351956","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T17:29:55Z","timestamp":1782754195000},"page":"e0351956","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Charlson comorbidity health analytics: A population management strategy to identify risk of hospitalizations, repeated hospitalizations, and resultant high cost"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6487-1014","authenticated-orcid":true,"given":"Mary","family":"Charlson","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin T.","family":"Wells","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James P.","family":"Hollenberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,6,29]]},"reference":[{"key":"pone.0351956.ref001","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/0021-9681(70)90054-8","article-title":"The pre-therapeutic classification of comorbidity in chronic illness","author":"AR Feinstein","year":"1970","journal-title":"J Chron Dis"},{"issue":"3","key":"pone.0351956.ref002","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1159\/000521952","article-title":"Comorbidity: From a Confounder in Longitudinal Clinical Research to the Main Issue in Population Management","volume":"91","author":"ME Charlson","year":"2022","journal-title":"Psychother Psychosom"},{"issue":"12","key":"pone.0351956.ref003","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0112479","article-title":"The Charlson comorbidity index can be used prospectively to identify patients who will incur high future costs","volume":"9","author":"M Charlson","year":"2014","journal-title":"PLoS One"},{"key":"pone.0351956.ref004","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1186\/1472-6963-14-461","article-title":"Medicaid managed care: how to target efforts to reduce costs","volume":"14","author":"ME Charlson","year":"2014","journal-title":"BMC Health Serv Res"},{"issue":"11","key":"pone.0351956.ref005","doi-asserted-by":"crossref","first-page":"1985","DOI":"10.1377\/hlthaff.2014.0408","article-title":"The NewYork-Presbyterian Regional Health Collaborative: a three-year progress report","volume":"33","author":"JE Carrillo","year":"2014","journal-title":"Health Aff (Millwood)"},{"key":"pone.0351956.ref006","doi-asserted-by":"crossref","first-page":"20","DOI":"10.25302\/08.2021.HSD.160334987","article-title":"Measuring the effects of social risks on patient health outcomes - 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