{"title":"Estimating Hospital Quality with Quasi-Experimental Data","authors":"Peter Hull","doi":"10.2139/ssrn.3118358","DOIUrl":null,"url":null,"abstract":"Non-random sorting can bias observational measures of institutional quality and distort quality-based polices. I develop alternative quasi-experimental approaches to quality estimation that accommodate nonlinear causal effects, institutional specialization, and unobserved selection-on-gains. I use this framework to compute empirical Bayes posteriors of the quality of 4,821 U.S. hospitals, combining estimates from ambulance referral quasi-experiments with predictions from observational risk-adjustment models. Higher-spending, higher-volume, and privately-owned hospitals are of higher quality, and most healthcare markets exhibit positive Roy selection-on-gains. I then simulate Medicare reimbursement and consumer guidance programs based on different hospital quality measures. Higher-spending providers tend to see moderately larger performance-linked subsidies when quality posteriors replace conventional rankings, while teaching hospitals are reimbursed relatively less. Admissions policy simulations highlight limitations of consumer guidance programs in settings with unobserved Roy selection: redirecting patients to top-ranked hospitals may worsen expected survival when based on observational rankings, while quasi-experimental rankings appear to generate modest gains.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"12 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"73","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Demand & Supply in Health Economics eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3118358","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 73
Abstract
Non-random sorting can bias observational measures of institutional quality and distort quality-based polices. I develop alternative quasi-experimental approaches to quality estimation that accommodate nonlinear causal effects, institutional specialization, and unobserved selection-on-gains. I use this framework to compute empirical Bayes posteriors of the quality of 4,821 U.S. hospitals, combining estimates from ambulance referral quasi-experiments with predictions from observational risk-adjustment models. Higher-spending, higher-volume, and privately-owned hospitals are of higher quality, and most healthcare markets exhibit positive Roy selection-on-gains. I then simulate Medicare reimbursement and consumer guidance programs based on different hospital quality measures. Higher-spending providers tend to see moderately larger performance-linked subsidies when quality posteriors replace conventional rankings, while teaching hospitals are reimbursed relatively less. Admissions policy simulations highlight limitations of consumer guidance programs in settings with unobserved Roy selection: redirecting patients to top-ranked hospitals may worsen expected survival when based on observational rankings, while quasi-experimental rankings appear to generate modest gains.