{"title":"减少医院再入院计划的预期和非预期后果","authors":"Engy Ziedan","doi":"10.2139/ssrn.3350492","DOIUrl":null,"url":null,"abstract":"The Hospital Readmission Reduction Program (HRRP) is a prominent Pay−for− Performance (P4P) program of the Centers for Medicare and Medicaid (CMS) intended to reduce hospital readmissions. In this article, I use a regression kink design to examine whether hospitals that were penalized under the HRRP changed the process of care for patients targeted and untrageted by the policy, as measured by the amount and composition of resource use (e.g. length of stay, and spending on radiology, pharmacy, and laboratory). Estimates indicate that hospitals penalized for excess heart attack (AMI) readmissions decreased AMI readmissions by 30% and increased spending on AMI patients by 20%. This additional care had no impact on mortality. Interestingly, I find that these hospitals also increased the quantity of care for patients with diagnoses not targeted by the HRRP. Hospitals penalized for excess readmissions for relatively more frequent conditions (pneumonia and heart failure) did not respond to the HRRP incentives. I show using a conceptual model of hospital behavior that as the number of patients in the targeted condition rises, the hospital’s marginal cost of reducing the penalty increases by relatively more than the marginal benefit. This intuitive result is novel and fundamental to the discussion on the relative incentive to reduce readmissions across medical diagnoses and how P4P programs can be optimized to reflect this differential cost.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"The Intended and Unintended Consequences of the Hospital Readmission Reduction Program\",\"authors\":\"Engy Ziedan\",\"doi\":\"10.2139/ssrn.3350492\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Hospital Readmission Reduction Program (HRRP) is a prominent Pay−for− Performance (P4P) program of the Centers for Medicare and Medicaid (CMS) intended to reduce hospital readmissions. In this article, I use a regression kink design to examine whether hospitals that were penalized under the HRRP changed the process of care for patients targeted and untrageted by the policy, as measured by the amount and composition of resource use (e.g. length of stay, and spending on radiology, pharmacy, and laboratory). Estimates indicate that hospitals penalized for excess heart attack (AMI) readmissions decreased AMI readmissions by 30% and increased spending on AMI patients by 20%. This additional care had no impact on mortality. Interestingly, I find that these hospitals also increased the quantity of care for patients with diagnoses not targeted by the HRRP. Hospitals penalized for excess readmissions for relatively more frequent conditions (pneumonia and heart failure) did not respond to the HRRP incentives. I show using a conceptual model of hospital behavior that as the number of patients in the targeted condition rises, the hospital’s marginal cost of reducing the penalty increases by relatively more than the marginal benefit. This intuitive result is novel and fundamental to the discussion on the relative incentive to reduce readmissions across medical diagnoses and how P4P programs can be optimized to reflect this differential cost.\",\"PeriodicalId\":11036,\"journal\":{\"name\":\"Demand & Supply in Health Economics eJournal\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Demand & Supply in Health Economics eJournal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3350492\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Demand & Supply in Health Economics eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3350492","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Intended and Unintended Consequences of the Hospital Readmission Reduction Program
The Hospital Readmission Reduction Program (HRRP) is a prominent Pay−for− Performance (P4P) program of the Centers for Medicare and Medicaid (CMS) intended to reduce hospital readmissions. In this article, I use a regression kink design to examine whether hospitals that were penalized under the HRRP changed the process of care for patients targeted and untrageted by the policy, as measured by the amount and composition of resource use (e.g. length of stay, and spending on radiology, pharmacy, and laboratory). Estimates indicate that hospitals penalized for excess heart attack (AMI) readmissions decreased AMI readmissions by 30% and increased spending on AMI patients by 20%. This additional care had no impact on mortality. Interestingly, I find that these hospitals also increased the quantity of care for patients with diagnoses not targeted by the HRRP. Hospitals penalized for excess readmissions for relatively more frequent conditions (pneumonia and heart failure) did not respond to the HRRP incentives. I show using a conceptual model of hospital behavior that as the number of patients in the targeted condition rises, the hospital’s marginal cost of reducing the penalty increases by relatively more than the marginal benefit. This intuitive result is novel and fundamental to the discussion on the relative incentive to reduce readmissions across medical diagnoses and how P4P programs can be optimized to reflect this differential cost.