A Data-driven Approach to Revamp the ACO Risk Adjustment Model

A Data-driven Approach to Revamp the ACO Risk Adjustment Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Data-driven Approach to Revamp the ACO Risk Adjustment Model Yubin Park, Kevin Buchan, Jr., Jason Piconne, Brandon Sim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2827550/v3 This work is licensed under a CC BY 4.0 License Status: Posted Version 3 posted You are reading this latest preprint version Show more versions Abstract Accountable Care Organizations (ACOs) involve groups of healthcare providers who voluntarily come together to deliver coordinated, high-quality care to aligned beneficiaries. Many ACOs, such as the Medicare Shared Savings Program and the ACO REACH program, can participate in alternative payment models that differ from the prevalent Fee-for-Service model. In these alternative payment models, providers and payers share financial risk to align the ACOs’ financial incentives with the dual aims of reducing the total cost of care and improving the quality of care. In other words, ACOs could profit by keeping their patients healthy and preventing unnecessary hospitalization. However, to make this financial structure work as intended, there needs to be a Risk Adjustment (RA) model to change reimbursement proportional to a beneficiary’s risk; otherwise, ACOs may enroll only healthy patients, i.e., adverse selection. While most ACOs adopt RA models for this reason, the original RA methodology has mostly stayed the same over the last several decades. As a result, some ACO participants have found ways to “game” the system: to receive disproportional payments for the risk they bear. To mitigate the waste, the federal government has added various post-adjustment mechanisms, such as mixing the risk-adjusted benchmark with historical spending, adjusting by a coding intensity factor, capping risk score growth rate, and incorporating health equity incentives. Unfortunately, those mechanisms build on top of each other in nonlinear and discontinuous ways, causing their actual effects - and efficacy - to be difficult to disentangle and evaluate. In this paper, we will summarize our lessons from operating one of the most successful ACOs in the nation to help rebuild the RA model based on a data-driven approach. Next, we outline the characteristics of an ideal RA model. Then, we propose a new one that addresses such requirements, eliminating the need for a multi-step process involving nonlinear and discontinuous staging. Finally, we provide experimental 1 results by applying this model to our ACO data and comparing them with the current RA implementation. Our experimental results show that our data-driven approaches can achieve better predictive performances measured in R-squared, Cumming’s Prediction Measure, and Mean Absolute Prediction Error. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 3 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2827550","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":196048995,"identity":"6dba7450-ce62-456e-a99a-51e3f01735fa","order_by":0,"name":"Yubin Park","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYJACxoYKIMnO3AAkmYnVcgakmBGmhQhtjI1tpGgxuN387OPMeXZ5/MyMjY9uVFgzmLP3H8Cv5c4x45kbtyUXSzYzNhvnnElnsOw5TMCWGwnGjA+3MSduOMzYJp3bdhgokkxIS/pnxodz6qFa/gG13H9MSEuOMePGhsNQLQ0gWwh4X/LOmWLGGceOQ/1yLJ3H4EyyAV4tfLfbNzP21FTn8bM3H3ycU2MtZ3D84AO8WhRuQOgEmAAPflcBgfwMNC2jYBSMglEwCjAAADl7SqQiH6OrAAAAAElFTkSuQmCC","orcid":"","institution":"Apollo Medical Holdings, Inc.","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yubin","middleName":"","lastName":"Park","suffix":""},{"id":196048996,"identity":"e217715e-0dd8-4e75-9633-9af442c3c924","order_by":1,"name":"Kevin Buchan, Jr.","email":"","orcid":"","institution":"Apollo Medical Holdings, Inc.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kevin","middleName":"","lastName":"Buchan","suffix":"Jr."},{"id":196048997,"identity":"95ebb2b2-b281-41eb-9680-09a413b37f77","order_by":2,"name":"Jason Piconne","email":"","orcid":"","institution":"Apollo Medical Holdings, Inc.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jason","middleName":"","lastName":"Piconne","suffix":""},{"id":196048998,"identity":"481024cd-84ea-4754-a4f6-aba0f813a616","order_by":3,"name":"Brandon Sim","email":"","orcid":"","institution":"Apollo Medical Holdings, Inc.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Brandon","middleName":"","lastName":"Sim","suffix":""}],"badges":[],"createdAt":"2023-04-17 13:44:32","currentVersionCode":3,"declarations":"","doi":"10.21203/rs.3.rs-2827550/v3","doiUrl":"https://doi.org/10.21203/rs.3.rs-2827550/v3","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":37105342,"identity":"90fb63d9-ce19-413f-9f65-393760707a5b","added_by":"auto","created_at":"2023-05-16 18:07:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1162389,"visible":true,"origin":"","legend":"","description":"","filename":"RevampingACORiskAdjustmentjournal3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2827550/v3_covered_68f84ce0-e799-47ab-b43c-086e7e4e134a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Data-driven Approach to Revamp the ACO Risk Adjustment Model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2827550/v3","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2827550/v3","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccountable Care Organizations (ACOs) involve groups of healthcare providers who voluntarily come together to deliver coordinated, high-quality care to aligned beneficiaries. Many ACOs, such as the Medicare Shared Savings Program and the ACO REACH program, can participate in alternative payment models that differ from the prevalent Fee-for-Service model. In these alternative payment models, providers and payers share financial risk to align the ACOs’ financial incentives with the dual aims of reducing the total cost of care and improving the quality of care. In other words, ACOs could profit by keeping their patients healthy and preventing unnecessary hospitalization. However, to make this financial structure work as intended, there needs to be a Risk Adjustment (RA) model to change reimbursement proportional to a beneficiary’s risk; otherwise, ACOs may enroll only healthy patients, i.e., adverse selection. While most ACOs adopt RA models for this reason, the original RA methodology has mostly stayed the same over the last several decades. As a result, some ACO participants have found ways to “game” the system: to receive disproportional payments for the risk they bear. To mitigate the waste, the federal government has added various post-adjustment mechanisms, such as mixing the risk-adjusted benchmark with historical spending, adjusting by a coding intensity factor, capping risk score growth rate, and incorporating health equity incentives. Unfortunately, those mechanisms build on top of each other in nonlinear and discontinuous ways, causing their actual effects - and efficacy - to be difficult to disentangle and evaluate. In this paper, we will summarize our lessons from operating one of the most successful ACOs in the nation to help rebuild the RA model based on a data-driven approach. Next, we outline the characteristics of an ideal RA model. Then, we propose a new one that addresses such requirements, eliminating the need for a multi-step process involving nonlinear and discontinuous staging. Finally, we provide experimental 1 results by applying this model to our ACO data and comparing them with the current RA implementation. Our experimental results show that our data-driven approaches can achieve better predictive performances measured in R-squared, Cumming’s Prediction Measure, and Mean Absolute Prediction Error.\u003c/p\u003e","manuscriptTitle":"A Data-driven Approach to Revamp the ACO Risk Adjustment Model","msid":"","msnumber":"","nonDraftVersions":[{"code":3,"date":"2023-05-16 17:59:24","doi":"10.21203/rs.3.rs-2827550/v3","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":2,"date":"2023-04-28 15:40:40","doi":"10.21203/rs.3.rs-2827550/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2023-04-21 10:04:33","doi":"10.21203/rs.3.rs-2827550/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3df9ccba-8630-4089-935e-f0b177a0b38f","owner":[],"postedDate":"May 16th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-05-15T16:30:12+00:00","versionOfRecord":[],"versionCreatedAt":"2023-05-16 17:59:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v3","identity":"rs-2827550","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2827550","identity":"rs-2827550","version":["v3"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}