Utilization Thresholds in Risk Adjustment Systems.
Risk adjustment systems, which reallocate funds among competing health insurers, often use risk adjustors that are based on utilization. The level of utilization that triggers an adjustor—the utilization threshold—is frequently chosen implicitly and uniformly. I study utilization thresholds empirica...
| Publicado en: | American Journal of Health Economics Vol. 10; no. 3; pp. 470 - 504 |
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| Autor principal: | |
| Formato: | equations & formulas tables/charts Journal Article |
| Publicado: |
University of Chicago Press
Summer2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178737604&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178737604 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23323493 HR86 jtl: American Journal of Health Economics issn: 23323493 maglogo: N pubinfo: dt: Summer2024 vid: 10 iid: 3 pid: 415 pub: University of Chicago Press place: Chicago, Illinois artinfo: ui: 178737604 178060823 178737604 178737604 10.1086/724791 178737604 ppf: 470 ppct: 34 formats: tig: atl: Utilization Thresholds in Risk Adjustment Systems. aug: au: Politzer, Eran sug: subj: Insurance, Health Utilization Medicaid Utilization Health Resource Utilization Risk Assessment Methods Simulations Regression Algorithms Motivation Database Management Software Financial Management Drugs, Prescription Analysis Descriptive Statistics ab: Risk adjustment systems, which reallocate funds among competing health insurers, often use risk adjustors that are based on utilization. The level of utilization that triggers an adjustor—the utilization threshold—is frequently chosen implicitly and uniformly. I study utilization thresholds empirically in the setting of the US Marketplaces. I demonstrate how an explicit choice of such thresholds, tailored to each adjustor, may improve the prediction fit of the risk adjustment system and decrease the incentives to game it. Using simulations, I find that a single alternative threshold may improve the prediction fit in some disease groups by up to 14 percent. A choice of multiple utilization thresholds, guided by a regression tree algorithm, may further improve fit while taking into account the effect on gaming incentives. pubtype: Academic Journal doctype: equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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