{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T08:19:31Z","timestamp":1777450771745,"version":"3.51.4"},"reference-count":15,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,3,18]],"date-time":"2021-03-18T00:00:00Z","timestamp":1616025600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2021,3,18]],"date-time":"2021-03-18T00:00:00Z","timestamp":1616025600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000905","name":"Commonwealth Fund","doi-asserted-by":"publisher","award":["20140607"],"award-info":[{"award-number":["20140607"]}],"id":[{"id":"10.13039\/100000905","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Patients with complex health care needs may suffer adverse outcomes from fragmented and delayed care, reducing well-being and increasing health care costs. Health reform efforts, especially those in primary care, attempt to mitigate risk of adverse outcomes by better targeting resources to those most in need. However, predicting who is susceptible to adverse outcomes, such as unplanned hospitalizations, ED visits, or other potentially avoidable expenditures, can be difficult, and providing intensive levels of resources to all patients is neither wanted nor efficient. Our objective was to understand if primary care teams can predict patient risk better than standard risk scores.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Six primary care practices risk stratified their entire patient population over a 2-year period, and worked to mitigate risk for those at high risk through care management and coordination. Individual patient risk scores created by the practices were collected and compared to a common risk score (Hierarchical Condition Categories) in their ability to predict future expenditures, ED visits, and hospitalizations. Accuracy of predictions, sensitivity, positive predictive values (PPV), and c-statistics were calculated for each risk scoring type. Analyses were stratified by whether the practice used intuition alone, an algorithm alone, or adjudicated an algorithmic risk score.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In all, 40,342 patients were risk stratified. Practice scores had 38.6% agreement with HCC scores on identification of high-risk patients. For the 3,381 patients with reliable outcomes data, accuracy was high (0.71\u20130.88) but sensitivity and PPV were low (0.16\u20130.40). Practice-created scores had 0.02\u20130.14 lower sensitivity, specificity and PPV compared to HCC in prediction of outcomes. Practices using adjudication had, on average, .16 higher sensitivity.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Practices using simple risk stratification techniques had slightly worse accuracy in predicting common outcomes than HCC, but adjudication improved prediction.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-021-01455-4","type":"journal-article","created":{"date-parts":[[2021,3,18]],"date-time":"2021-03-18T19:12:32Z","timestamp":1616094752000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Primary care practices\u2019 ability to predict future risk of expenditures and hospitalization using risk stratification and segmentation"],"prefix":"10.1186","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2318-7261","authenticated-orcid":false,"given":"David A.","family":"Dorr","sequence":"first","affiliation":[]},{"given":"Rachel L.","family":"Ross","sequence":"additional","affiliation":[]},{"given":"Deborah","family":"Cohen","sequence":"additional","affiliation":[]},{"given":"Devan","family":"Kansagara","sequence":"additional","affiliation":[]},{"given":"Katrina","family":"Ramsey","sequence":"additional","affiliation":[]},{"given":"Bhavaya","family":"Sachdeva","sequence":"additional","affiliation":[]},{"given":"Jonathan P.","family":"Weiner","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,18]]},"reference":[{"issue":"12","key":"1455_CR1","doi-asserted-by":"publisher","first-page":"2306","DOI":"10.1111\/j.1532-5415.2009.02558.x","volume":"57","author":"DG Mosley","year":"2009","unstructured":"Mosley DG, Peterson E, Martin DC. Do hierarchical condition category model scores predict hospitalization risk in newly enrolled Medicare advantage participants as well as probability of repeated admission scores? J Am Geriatr Soc. 2009;57(12):2306\u201310.","journal-title":"J Am Geriatr Soc"},{"issue":"2","key":"1455_CR2","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1097\/MLR.0b013e3182353ceb","volume":"50","author":"KW Lemke","year":"2012","unstructured":"Lemke KW, Weiner JP, Clark JM. Development and validation of a model for predicting inpatient hospitalization. Med Care. 2012;50(2):131\u20139.","journal-title":"Med Care"},{"key":"1455_CR3","unstructured":"Abrams M, Kleiman R, Schneider E, Shah T. The better care playbook [Internet]: the commonwealth fund. 2016. http:\/\/www.bettercareplaybook.org\/resources\/overview-segmentation-high-need-high-cost-patient-population."},{"key":"1455_CR4","doi-asserted-by":"publisher","first-page":"585","DOI":"10.3122\/jabfm.2019.04.180341","volume":"32","author":"JE Wagner","year":"2019","unstructured":"Wagner JE, Hall J, Ross R, Cameron D, Sachdeva B, Cohen D, et al. Implementing risk stratification in primary care: challenges, considerations, and strategies. J Am Board Fam Med. 2019;32:585\u201395.","journal-title":"J Am Board Fam Med"},{"issue":"9","key":"1455_CR5","first-page":"725","volume":"19","author":"LR Haas","year":"2013","unstructured":"Haas LR, Takahashi PY, Shah ND, Stroebel RJ, Bernard ME, Finnie DM, et al. Risk-stratification methods for identifying patients for care coordination. Am J Manag Care. 2013;19(9):725\u201332.","journal-title":"Am J Manag Care"},{"issue":"2","key":"1455_CR6","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1097\/01.mlr.0000108743.74496.ce","volume":"42","author":"TL Wahls","year":"2004","unstructured":"Wahls TL, Barnett MJ, Rosenthal GE. Predicting resource utilization in a Veterans Health Administration primary care population: comparison of methods based on diagnoses and medications. Med Care. 2004;42(2):123\u20138.","journal-title":"Med Care"},{"key":"1455_CR7","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1177\/1460458216645149","volume":"23","author":"L Bruun Larsen","year":"2017","unstructured":"Bruun Larsen L, Soendergaard J, Halling A, Thilsing T, Thomsen JL. A novel approach to population-based risk stratification, comprising individualized lifestyle intervention in Danish general practice to prevent chronic diseases: results from a feasibility study. Health Informatics J. 2017;23:249\u201359.","journal-title":"Health Informatics J"},{"issue":"11","key":"1455_CR8","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1097\/01.MLR.0000094480.13057.75","volume":"41","author":"RT Meenan","year":"2003","unstructured":"Meenan RT, Goodman MJ, Fishman PA, Hornbrook MC, O\u2019Keeffe-Rosetti MC, Bachman DJ. Using risk-adjustment models to identify high-cost risks. Med Care. 2003;41(11):1301\u201312.","journal-title":"Med Care"},{"issue":"4","key":"1455_CR9","first-page":"78","volume":"5","author":"RL Ross","year":"2017","unstructured":"Ross RL, Sachdeva B, Wagner J, Ramsey K, Dorr DA. Perceptions of risk stratification workflows in primary care. Healthcare (Basel, Switzerland). 2017;5(4):78.","journal-title":"Healthcare (Basel, Switzerland)"},{"key":"1455_CR10","first-page":"1","volume":"19","author":"CS Hong","year":"2014","unstructured":"Hong CS, Siegel AL, Ferris TG. Caring for high-need, high-cost patients: what makes for a successful care management program? Issue Brief (Commonw Fund). 2014;19:1\u201319.","journal-title":"Issue Brief (Commonw Fund)"},{"issue":"12","key":"1455_CR11","doi-asserted-by":"publisher","first-page":"1741","DOI":"10.1007\/s11606-015-3357-8","volume":"30","author":"CS Hong","year":"2015","unstructured":"Hong CS, Atlas SJ, Ashburner JM, Chang Y, He W, Ferris TG, et al. Evaluating a model to predict primary care physician-defined complexity in a large academic primary care practice-based research network. J Gen Intern Med. 2015;30(12):1741\u20137.","journal-title":"J Gen Intern Med"},{"issue":"2","key":"1455_CR12","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1016\/j.jbi.2008.08.010","volume":"42","author":"PA Harris","year":"2009","unstructured":"Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)\u2014a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377\u201381.","journal-title":"J Biomed Inform"},{"issue":"3","key":"1455_CR13","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1191\/1740774506cn147oa","volume":"3","author":"CS Moskowitz","year":"2006","unstructured":"Moskowitz CS, Pepe MS. Comparing the predictive values of diagnostic tests: sample size and analysis for paired study designs. Clin Trials. 2006;3(3):272\u20139.","journal-title":"Clin Trials"},{"issue":"3","key":"1455_CR14","doi-asserted-by":"publisher","first-page":"794","DOI":"10.4338\/ACI-2016-12-RA-0210","volume":"8","author":"S Martin","year":"2017","unstructured":"Martin S, Wagner J, Lupulescu-Mann N, Ramsey K, Cohen A, Graven P, et al. Comparison of EHR-based diagnosis documentation locations to a gold standard for risk stratification in patients with multiple chronic conditions. Appl Clin Inform. 2017;8(3):794\u2013809.","journal-title":"Appl Clin Inform"},{"key":"1455_CR15","unstructured":"Peikes D, Ghosh A, Zutshi A, Taylor EF, Anglin G, Converse L, et al. Evaluation of the Comprehensive Primary Care Initiative: second annual report [Internet]. Princeton (NJ): Mathematica Policy Research; 2016 Apr [cited 2017 Dec 14]. https:\/\/innovation.cms.gov\/Files."}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-021-01455-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12911-021-01455-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-021-01455-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,18]],"date-time":"2021-03-18T19:20:56Z","timestamp":1616095256000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-021-01455-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,18]]},"references-count":15,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["1455"],"URL":"https:\/\/doi.org\/10.1186\/s12911-021-01455-4","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,18]]},"assertion":[{"value":"21 February 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The Institutional Review Board at Oregon Health and Science University (OHSU) approved this study, including access to the relevant data. Analysis was done under a waiver of consent, with data de-identified during analysis.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"104"}}