Medicare & medicaid research review最新文献

{"title":"Use of hospitalists by Medicare beneficiaries: a national picture.","authors":"W Pete Welch,&nbsp;Sally C Stearns,&nbsp;Alison E Cuellar,&nbsp;Andrew B Bindman","doi":"10.5600/mmrr2014-004-02-b01","DOIUrl":"https://doi.org/10.5600/mmrr2014-004-02-b01","url":null,"abstract":"<p><strong>Objective: </strong>To describe the characteristics of hospitalists serving Medicare beneficiaries.</p><p><strong>Data sources: </strong>Medicare claims from 2009 and 2011 merged with the Provider Enrollment, Chain, and Ownership System file for physician characteristics.</p><p><strong>Study design: </strong>Our construction of the Medicare Data on Physician Practice and Specialty (MD-PPAS) enabled identification of hospitalists based on the attending physician for Medicare admissions (medical and surgical) in 2009 and 2011.</p><p><strong>Principal findings: </strong>In 2011, hospitalists constituted 13.3% of physicians who designated their specialty as primary care and 4.4% of all physicians serving Medicare beneficiaries. Compared to other physicians, hospitalists were more likely to be female, under forty, and in large practices. More than a quarter of Medicare admissions had a hospitalist as the attending physician, though the rate was substantially higher for medical than surgical admissions (31.8% versus 11.3%). Between 2009 and 2011, the percentage of medical admissions with a hospitalist as the attending physician increased by roughly a quarter (from 25.7% to 31.8%).</p><p><strong>Conclusions: </strong>This analysis provides a more current and complete estimate of the use of hospitalists by the Medicare population than is available from prior studies. The ability to identify hospitalists from claims data will facilitate research on the impact of hospitalist use on quality and cost.</p>","PeriodicalId":89601,"journal":{"name":"Medicare & medicaid research review","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4067040/pdf/mmrr2014-004-02-b01.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32456097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can increases in CHIP copayments reduce program expenditures on prescription drugs?","authors":"Bisakha Sen, Justin Blackburn, Michael Morrisey, David Becker, Meredith Kilgore, Cathy Caldwell, Nir Menachemi","doi":"10.5600/mmrr2014-004-02-a03","DOIUrl":"10.5600/mmrr2014-004-02-a03","url":null,"abstract":"<p><strong>Objective: </strong>The primary aim is to explore whether prescription drug expenditures by enrollees changed in Alabama's CHIP program, ALL Kids, after copayment increases in fiscal year 2004. The subsidiary aim is to explore whether non-pharmaceutical expenditures also changed.</p><p><strong>Data sources: </strong>Data on ALL Kids enrollees between 1999-2007, obtained from claims files and the state's administrative database.</p><p><strong>Study design: </strong>We used data on children who were enrolled between one and three years both before and after the changes to the copayment schedule, and estimate regression models with individual-level fixed effects to control for time-invariant heterogeneity at the child level. This allows an accurate estimate of how program expenditures change for the same individual following copayment changes. Primary outcomes of interest are expenditures for prescription drugs by class and brand-name and generic versions. We estimate models for the likelihood of any use of prescription drugs and expenditure level conditional on use.</p><p><strong>Principal findings: </strong>Following the copayment increase, the probability of any expenditure decline by 5.8%, brand name drugs by 6.9%, generic drugs by 7.4%. Conditional on any use, program expenditures decline by 7.9% for all drugs, by 9.6% for brand name drugs, and 6.2% for generic drugs. The largest declines are for antihistamine drugs; the least declines are for Central Nervous System agents. Declines are smaller and statistically weaker for children with chronic health conditions. Concurrent declines are also seen for non-pharmaceutical medical expenditures.</p><p><strong>Conclusions: </strong>Copayment increases appear to reduce program expenditures on prescription drugs per enrollee and may be a useful tool for controlling program costs.</p>","PeriodicalId":89601,"journal":{"name":"Medicare & medicaid research review","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4063370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32456096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Post-discharge follow-up visits and hospital utilization by Medicare patients, 2007-2010.","authors":"Derek DeLia, Jian Tong, Dorothy Gaboda, Lawrence P Casalino","doi":"10.5600/mmrr.004.02.a01","DOIUrl":"10.5600/mmrr.004.02.a01","url":null,"abstract":"<p><strong>Objective: </strong>Document trends in time to post-discharge follow-up visit for Medicare patients with an index admission for heart failure (HF), acute myocardial infarction (AMI), or community-acquired pneumonia (CAP). Determine factors predicting whether the first post-discharge utilization event is a follow-up visit, treat-and-release emergency department (ED) visit, or readmission.</p><p><strong>Methods: </strong>Using Medicare claims data from 2007-2010, we plotted annual cumulative incidence functions for the time frame post-discharge to follow-up visit, accounting for competing risks with censoring at 30 days. We used multinomial probit regression to determine factors predicting the probability of first-occurring post-discharge utilization events within 30 days.</p><p><strong>Results: </strong>For each cohort, the cumulative incidence of follow-up visits increased during the study period. For example, in 2010, 54.6% of HF patients had a follow-up visit within 10 days of discharge compared to 47.9% in 2007. Within each cohort, the largest increase in follow-up visits took place between 2008 and 2009. Follow-up visits were less likely for patients who were Black, Hispanic, and enrolled in Medicaid or Medicare Advantage, and they were more likely for patients with greater comorbidities and prior procedures as well as those with private or supplemental Medicare coverage. There were no changes in 30-day readmission rates.</p><p><strong>Discussion: </strong>Although increases in follow-up visits may have been influenced by the introduction of publicly reported readmission rates in 2009, these increases did not continue in 2010 and were not associated with a change in readmissions. Patients who were Black, Hispanic, and/or enrolled in Medicaid or Medicare Advantage were less likely to have follow-up visits.</p>","PeriodicalId":89601,"journal":{"name":"Medicare & medicaid research review","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4062381/pdf/mmrr2014-004-02-a01.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32439516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The HHS-HCC risk adjustment model for individual and small group markets under the Affordable Care Act.","authors":"John Kautter, Gregory C Pope, Melvin Ingber, Sara Freeman, Lindsey Patterson, Michael Cohen, Patricia Keenan","doi":"10.5600/mmrr2014-004-03-a03","DOIUrl":"10.5600/mmrr2014-004-03-a03","url":null,"abstract":"<p><p>Beginning in 2014, individuals and small businesses are able to purchase private health insurance through competitive Marketplaces. The Affordable Care Act (ACA) provides for a program of risk adjustment in the individual and small group markets in 2014 as Marketplaces are implemented and new market reforms take effect. The purpose of risk adjustment is to lessen or eliminate the influence of risk selection on the premiums that plans charge. The risk adjustment methodology includes the risk adjustment model and the risk transfer formula. This article is the second of three in this issue of the Review that describe the Department of Health and Human Services (HHS) risk adjustment methodology and focuses on the risk adjustment model. In our first companion article, we discuss the key issues and choices in developing the methodology. In this article, we present the risk adjustment model, which is named the HHS-Hierarchical Condition Categories (HHS-HCC) risk adjustment model. We first summarize the HHS-HCC diagnostic classification, which is the key element of the risk adjustment model. Then the data and methods, results, and evaluation of the risk adjustment model are presented. Fifteen separate models are developed. For each age group (adult, child, and infant), a model is developed for each cost sharing level (platinum, gold, silver, and bronze metal levels, as well as catastrophic plans). Evaluation of the risk adjustment models shows good predictive accuracy, both for individuals and for groups. Lastly, this article provides examples of how the model output is used to calculate risk scores, which are an input into the risk transfer formula. Our third companion paper describes the risk transfer formula. </p>","PeriodicalId":89601,"journal":{"name":"Medicare & medicaid research review","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4214270/pdf/mmrr2014-004-03-a03.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32784047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating whether changes in utilization of hospital outpatient services contributed to lower Medicare readmission rate.","authors":"Geoffrey Gerhardt,&nbsp;Alshadye Yemane,&nbsp;Keri Apostle,&nbsp;Allison Oelschlaeger,&nbsp;Eric Rollins,&nbsp;Niall Brennan","doi":"10.5600/mmrr2014-004-01-b03","DOIUrl":"https://doi.org/10.5600/mmrr2014-004-01-b03","url":null,"abstract":"<p><strong>Objective: </strong>Descriptive analysis comparing changes in hospital inpatient readmissions to emergency department visits and observation stays that occurred within 30 days of an inpatient stay.</p><p><strong>Population: </strong>Medicare fee-for-service (FFS) beneficiaries that had at least one acute hospital inpatient stay.</p><p><strong>Data source: </strong>Using 100 percent of claims in the Chronic Condition Data Warehouse, we compare growth in annual readmission stays to post-hospitalization emergency department visits and observation stays that were not accompanied by an inpatient stay. Comparisons are performed at the national level and within the Dartmouth Hospital Referral Regions (HRRs).</p><p><strong>Results: </strong>In calendar year 2012, the national, all-cause, 30-day hospital readmission rate among Medicare FFS beneficiaries was 18.5 percent, a significant decline from 19 percent in 2011, which was also the average rate over the previous five years. The number of index admission stays per-1,000 Medicare beneficiaries declined by 4.3 percent, from 283.4 in 2011 to 271.3 in 2012. On a per-1,000 beneficiary basis, the number of readmission stays declined by 6.8 percent, from 53.8 in 2011 to 50.1 in 2012. On the same per-beneficiary basis, the rate of outpatient visits to an emergency department occurring within 30 days of an index hospitalization remained similar at 23.5 in 2011 and 23.4 in 2012. Per-1,000 beneficiaries, the number of observation stays within 30 days of an index hospitalization increased by 0.3 percent, from 3.4 in 2011 to 3.7 in 2012.</p><p><strong>Discussion: </strong>The reasons behind the decline in the Medicare readmission rate in 2012 are not yet clear. When looking at utilization changes in absolute terms, our findings suggest that the reduction in the nation-wide readmission rate observed in 2012 was not primarily the result of increases in either post-index ED visits or post-index observation stays.</p>","PeriodicalId":89601,"journal":{"name":"Medicare & medicaid research review","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4086854/pdf/mmrr2014-004-01-b03.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32493066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of Medicaid peer support utilization on cost.","authors":"Glenn Landers,&nbsp;Mei Zhou","doi":"10.5600/mmrr.004.01.a04","DOIUrl":"https://doi.org/10.5600/mmrr.004.01.a04","url":null,"abstract":"<p><strong>Background: </strong>Peer support programs have proliferated over the past decade, building on recovery oriented programming, yet relationships between peer support services and the costs to public programs have not been well described in literature. The purpose of this study is to fill gaps in the literature related to peer support programs and cost: lack of comparison groups, small sample sizes, and the availability of research examining utilization of Medicaid mental health services.</p><p><strong>Methods: </strong>The study employed a retrospective design with treatment and comparison groups created from three administrative databases. Three ordinary least squares regression models were constructed to predict crisis stabilization cost, psychiatric hospitalization cost, and total Medicaid cost while controlling for other factors. The Chronic Illness and Disability Payment System + Rx was used to control for illness severity.</p><p><strong>Results: </strong>Peer support was associated with $5,991 higher total Medicaid cost (p < .01). Peer support was also associated with higher crisis stabilization cost and lower psychiatric hospitalization cost, but the relationships were not statistically significant. Peer support was associated with $2,100 higher prescription drug cost (p < .01), $5,116 higher professional services cost (p < .01), and $1,225 lower facility cost (p < .01).</p><p><strong>Conclusions: </strong>While the implementation of Medicaid financed peer support programs may not result in savings from reductions of costly crisis stabilizations and psychiatric hospitalizations, it does support the principles of self-direction and recovery from severe mental illness. State policy makers must weigh the potential higher cost associated with peer support programs with efforts to redesign the delivery of mental health services.</p>","PeriodicalId":89601,"journal":{"name":"Medicare & medicaid research review","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5600/mmrr.004.01.a04","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32420775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The policy implications of the cost structure of home health agencies.","authors":"Dana B Mukamel, Richard H Fortinsky, Alan White, Charlene Harrington, Laura M White, Quyen Ngo-Metzger","doi":"10.5600/mmrr2014-004-01-a03","DOIUrl":"10.5600/mmrr2014-004-01-a03","url":null,"abstract":"<p><strong>Purpose: </strong>To examine the cost structure of home health agencies by estimating an empirical cost function for those that are Medicare-certified, ten years following the implementation of prospective payment.</p><p><strong>Design and methods: </strong>2010 national Medicare cost report data for certified home health agencies were merged with case-mix information from the Outcome and Assessment Information Set (OASIS). We estimated a fully interacted (by tax status) hybrid cost function for 7,064 agencies and calculated marginal costs as percent of total costs for all variables.</p><p><strong>Results: </strong>The home health industry is dominated by for-profit agencies, which tend to be newer than the non-profit agencies and to have higher average costs per patient but lower costs per visit. For-profit agencies tend to have smaller scale operations and different cost structures, and are less likely to be affiliated with chains. Our estimates suggest diseconomies of scale, zero marginal cost for contracting with therapy workers, and a positive marginal cost for contracting with nurses, when controlling for quality.</p><p><strong>Implications: </strong>Our findings suggest that efficiencies may be achieved by promoting non-profit, smaller agencies, with fewer contract nursing staff. This conclusion should be tested further in future studies that address some of the limitations of our study.</p>","PeriodicalId":89601,"journal":{"name":"Medicare & medicaid research review","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4062313/pdf/mmrr2014-004-01-a03.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32439514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Medicaid medically improved group: losing disability status and growing earnings.","authors":"Kathleen C Thomas, Jean P Hall","doi":"10.5600/mmrr.004.01.a02","DOIUrl":"10.5600/mmrr.004.01.a02","url":null,"abstract":"<p><strong>Objectives: </strong>Under the Ticket to Work and Work Incentives Improvement Act (PL 106-170), states may extend Medicaid Buy-In coverage to a medically improved group. Improved group coverage allows adults with disabilities to retain Medicaid coverage even once they lose disability status due to medical improvement, as long as they retain the original medical impairment. The goal of this paper is to describe who participated, the patterns of their participation, and employment outcomes.</p><p><strong>Methods: </strong>The study population consists of all individuals (n = 315) who participated in medically improved group coverage 2002-2009 in the seven states with coverage by 2009 (Arizona, Connecticut, Kansas, New York, North Carolina, Pennsylvania, and West Virginia). Linked data from state Medicaid Buy-In finder files and Social Security Administration Ticket Research and Master Earnings Files were used to describe improved group participants and their patterns of enrollment.</p><p><strong>Results: </strong>Although enrollment has been limited, with 255 participants in 2009, it has doubled annually on average with little churning and drop-out. Participants' earnings grew nearly 200 dollars per month after two years, likely reflecting increased work hours and/or higher pay rates.</p><p><strong>Conclusions: </strong>Improved group participants represent an unusually successful group of individuals with disabilities, many of whom have recently moved off Social Security cash benefit rolls or who were diverted from them. Specifics of insurance eligibility and coverage for improved group participants are uncertain under the Affordable Care Act. The challenge remains to provide a pathway for adults with disabilities to increase work and assets without loss of adequate health insurance.</p>","PeriodicalId":89601,"journal":{"name":"Medicare & medicaid research review","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4049515/pdf/mmrr2014-004-01-a02.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32415745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medicare post-acute care episodes and payment bundling.","authors":"Melissa Morley,&nbsp;Susan Bogasky,&nbsp;Barbara Gage,&nbsp;Shannon Flood,&nbsp;Melvin J Ingber","doi":"10.5600/mmrr.004.01.b02","DOIUrl":"https://doi.org/10.5600/mmrr.004.01.b02","url":null,"abstract":"<p><strong>Background: </strong>The purpose of this paper is to examine service use in an episode of acute and post-acute care (PAC) under alternative episode definitions and to look at geographic differences in episode payments.</p><p><strong>Data and methods: </strong>The data source for these analyses was a Medicare claims file for 30 percent of beneficiaries with an acute hospital initiated episode in 2008 (N = 1,705,794, of which 38.7 percent went on to use PAC). Fixed length episodes of 30, 60, and 90 days were examined. Analyses examined differences in definitions allowing any claim within the fixed length period to be part of the episode versus prorating a claim extending past the episode endpoint. Readmissions were also examined as an episode endpoint. Payments were standardized to allow for comparison of episode payments per acute hospital discharge or PAC user across states.</p><p><strong>Results: </strong>The results of these analyses provide information on the composition of service use under different episode definitions and highlight considerations for providers and payers testing different alternatives for bundled payment.</p>","PeriodicalId":89601,"journal":{"name":"Medicare & medicaid research review","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053189/pdf/mmrr2014-004-01-b02.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32420774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medicare-Medicaid eligible beneficiaries and potentially avoidable hospitalizations.","authors":"Misha Segal, Eric Rollins, Kevin Hodges, Michelle Roozeboom","doi":"10.5600/mmrr.004.01.b01","DOIUrl":"10.5600/mmrr.004.01.b01","url":null,"abstract":"<p><strong>Objective: </strong>Potentially avoidable hospitalizations have been identified by experts as leading to poor health outcomes and costly care. Potentially avoidable hospitalizations are particularly common among full-benefit dual eligible beneficiaries. This paper examines potentially avoidable hospitalizations rates by setting, state, and medical condition, and the average cost of these events.</p><p><strong>Methods: </strong>This analysis identifies potentially avoidable hospitalizations using diagnosis codes identified by an expert panel. Settings of care are determined using a timeline file, which assigns an individual to a specific setting on a particular day.</p><p><strong>Population/data source: </strong>The analysis uses several different datasets from the Chronic Conditions Data Warehouse. The study population includes fee-for-service beneficiaries who were eligible for both Medicare and full Medicaid benefits for at least one month during the calendar year. The study years are 2007 to 2009.</p><p><strong>Results: </strong>In 2009, among our study population, 26 percent of hospitalizations were potentially avoidable; and the rate was 133 per 1,000 person-years. Potentially avoidable hospitalizations were much more likely for those beneficiaries who were in institutions--16 percent of beneficiaries in our study population were in an institution, yet comprised 45 percent of all potentially avoidable hospitalizations. The range in rates across the states was considerable, with more than a threefold difference across states. Five conditions were responsible for nearly 80 percent of potentially avoidable hospitalizations. From 2007 to 2009, the national and state rates were fairly consistent.</p><p><strong>Discussion: </strong>This analysis indicates that the potentially avoidable hospitalization rate among MME beneficiaries was consistently high from 2007 to 2009. This bears monitoring in the future to see if the Centers for Medicare & Medicaid Services' various initiatives have led to a reduction in rates.</p>","PeriodicalId":89601,"journal":{"name":"Medicare & medicaid research review","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053188/pdf/mmrr2014-004-01-b01.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32421935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}