Faculty Bibliography

OBJECTIVES/OBJECTIVE:High-value care is providing high quality care at low cost; we sought to define hospital value and identify the characteristics of hospitals which provide high-value care. DESIGN/METHODS:Retrospective observational study. SETTING/METHODS:Acute care hospitals in the USA. PARTICIPANTS/METHODS:All Medicare beneficiaries with claims included in Center for Medicare & Medicaid Services Overall Star Ratings or in publicly available Medicare spending per beneficiary data. PRIMARY AND SECONDARY OUTCOME MEASURES/METHODS:Our primary outcome was value defined as the difference between Star Ratings quality score and Medicare spending; the secondary outcome was classification as a 4 or 5 star hospital with lowest quintile Medicare spending ('high value') or 1 or 2 star hospital with highest quintile spending ('low value'). RESULTS:Two thousand nine hundred and fourteen hospitals had both quality and spending data, and were included. The value score had a mean (SD) of 0.58 (1.79). A total of 286 hospitals were classified as high value; these represented 28.6% of 999 4 and 5 star hospitals and 46.8% of 611 low cost hospitals. A total of 258 hospitals were classified as low value; these represented 26.6% of 970 1 and 2 star hospitals and 49.3% of 523 high cost hospitals. In regression models ownership, non-teaching status, beds, urbanity, nurse to bed ratio, percentage of dual eligible Medicare patients and percentage of disproportionate share hospital payments were associated with the primary value score. CONCLUSIONS:There are high quality hospitals that are not high value, and a number of factors are strongly associated with being low or high value. These findings can inform efforts of policymakers and hospitals to increase the value of care.

This cross-sectional study describes the price differences between capsule and tablet or ointment and cream forms of prescription drugs for insured patients.

OBJECTIVES:To evaluate whether hospital entry into the 340B Drug Pricing Program, which entitles eligible hospitals to discounts on drug purchases and intends for hospitals to use associated savings to devote more resources to the care of low-income populations, is associated with changes in hospital provision of uncompensated care. STUDY DESIGN:We analyzed secondary data on 340B participation and uncompensated care provision among general acute care hospitals and critical access hospitals from 2003 to 2015. We constructed an annual, hospital-level data set on hospital 340B participation from the Office of Pharmacy Information Systems and on uncompensated care provision from the Hospital Cost Reporting Information System. METHODS:Focusing on 2 periods of program expansion, we separately analyzed trends in uncompensated care costs for 340B-eligible general acute care hospitals and critical access hospitals, stratified by year of 340B program entry, including a stratum of eligible hospitals that never participated. We used a differences-in-differences approach to quantify whether there were differential changes in provision of uncompensated care after hospitals enter the 340B program relative to hospitals that did not participate or had not yet entered. RESULTS:We do not find evidence that hospitals increased provision of uncompensated care after entry into the 340B program differentially more than hospitals that never entered or had not yet entered the program. CONCLUSIONS:Relying on hospitals to invest surplus into care for the underserved without marginal incentives to do so or strong oversight may not be an effective strategy to expand safety-net care.

OBJECTIVE:To assess changes in physicians' provision of care to duals (low-income individuals with Medicare and Medicaid) in response to a policy that required Medicaid to fully pay Medicare's cost sharing for office visits with these patients. This policy-a provision of the Affordable Care Act-effectively increased payments for office visits with duals by 0%-20%, depending on the state, in 2013 and 2014. DATA SOURCES/METHODS:Fee-for-service claims for a 5% random sample of Medicare beneficiaries in 2010-2016. STUDY DESIGN/METHODS:We conducted a difference-in-differences analysis to compare changes in office visits among Qualified Medicare Beneficiaries (QMBs)-the largest subpopulation of duals for whom payment rates were affected by this policy-to changes among other low-income Medicare beneficiaries for whom payment rates were unaffected (pooled across all states). Next, we conducted a triple-differences analysis that compared changes between QMBs and other low-income beneficiaries in 33 states with payment rate increases of approximately 20% to analogous changes in 14 states without payment increases. DATA COLLECTION/METHODS:The study included administrative Medicare enrollment and claims data for QMBs and a comparison group of other low-income Medicare beneficiaries (1 914 073 beneficiary-years from 2010 to 2016). PRINCIPAL FINDINGS/RESULTS:Nationally, we did not find a differential increase in office visits among QMBs versus other low-income beneficiaries that coincided with this payment change. In the triple-differences analysis, we did not observe a greater increase in visits among QMBs vs other low-income beneficiaries in states where the policy resulted in large (approximately 20%) increases in payment rates vs states where payment rates were unaffected (triple-differences estimate: -0.12 annual visits, 95% CI: -0.28, 0.04; P = 0.15). CONCLUSIONS:Physicians' provision of care to low-income Medicare beneficiaries may not be responsive to short-run payment changes.

BACKGROUND:The Hospital Readmissions Reduction Program (HRRP) penalizes hospitals for higher-than-expected readmission rates. Almost 20% of Medicare fee-for-service (FFS) patients receive postacute care in skilled nursing facilities (SNFs) after hospitalization. SNF patients have high readmission rates. OBJECTIVE:The objective of this study was to investigate the association between changes in hospital referral patterns to SNFs and HRRP penalty pressure. DESIGN/METHODS:We examined changes in the relationship between penalty pressure and outcomes before versus after HRRP announcement among 2698 hospitals serving 6,936,393 Medicare FFS patients admitted for target conditions: acute myocardial infarction, heart failure, or pneumonia. Hospital-level penalty pressure was the expected penalty rate in the first year of the HRRP multiplied by Medicare discharge share. OUTCOMES/RESULTS:Informal integration measured by the percentage of referrals to hospitals' most referred SNF; formal integration measured by SNF acquisition; readmission-based quality index of the SNFs to which a hospital referred discharged patients; referral rate to any SNF. RESULTS:Hospitals facing the median level of penalty pressure had modest differential increases of 0.3 percentage points in the proportion of referrals to the most referred SNF and a 0.006 SD increase in the average quality index of SNFs referred to. There were no statistically significant differential increases in formal acquisition of SNFs or referral rate to SNF. CONCLUSIONS:HRRP did not prompt substantial changes in hospital referral patterns to SNFs, although readmissions for patients referred to SNF differentially decreased more than for other patients, warranting investigation of other mechanisms underlying readmissions reduction.

OBJECTIVES/OBJECTIVE:Healthcare payers are increasingly using price transparency and benefit design to encourage patients to choose lower-priced providers. We quantify potential savings from shifting patients to lower-priced providers. If there is limited price variation or if higher-priced providers command little market share, savings could be minimal. STUDY DESIGN/METHODS:Using 2013-2014 commercial claims for 697,381 enrollees in California, we characterized within-market price variation and the relationship between providers' market shares and relative prices for 3 nonemergent, shoppable outpatient services: laboratory tests, imaging services, and durable medical equipment (DME). In a stylized policy simulation that holds provider price and utilization constant, we computed potential savings if patients who visited providers with prices above the median price shifted to the median-priced provider in their geographic market for the same service. METHODS:Observational analyses. RESULTS:Of the service categories examined, laboratory tests had greatest within-market price variation (median coefficient of variation of 100% vs 87% for imaging services and 43% for DME). Roughly half of services (53%, 47%, and 54% for laboratory tests, imaging services, and DME, respectively) were billed by providers with prices above their market median. Shifting these patients to the median-priced provider in their markets could save 42%, 45%, and 15% of spending on laboratory tests, imaging services, and DME, respectively, together representing savings of 11% of total outpatient spending and 7% of the sum of inpatient and outpatient spending. CONCLUSIONS:Steering patients from higher- to lower-priced providers within geographic markets in targeted service categories could generate substantial healthcare savings.

OBJECTIVE: To assess the impact of Medicaid prescription copayment policies on anti-psychotic and other medication use among patients with schizophrenia. METHOD: The study sample included fee-for-service adult Medicaid patients with schizophrenia. Medicaid claims records from 2003-2005 from 42 states and D.C. were linked with county-level data from the Area Resource File and findings from a state Medicaid policy survey. Patient-level fixed-effects regression models examined the impact of increases in generic copayments and generic/brand copayment differentials on monthly use of anti-psychotic (overall and by generic/brand status) and other non-antipsychotic medications. Medications for hypertension, hyperlipidemia, and diabetes in sub-groups of patients with these comorbidities were also examined. RESULTS: Prescription copayment changes had a statistically significant but small impact on anti-psychotic use. For instance, for every $1 increase in the minimum or generic copayment per prescription, there was a reduction of 1.4 anti-psychotic drug fills per 100 patient months (relative reduction = 1.9%). The generic/brand copayment differential increases also had a minimal impact in changing utilization of first-generation (generic) and second-generation (brand) anti-psychotics. Effects of copayment changes on non-anti-psychotic medication use were substantially higher; for each $1 generic copayment increase, there was a reduction of 23 non-anti-psychotic drug fills per 100 patient months (relative reduction = 10.1%). Similarly, for each $1 increase in the generic/brand copayment differential, there was a reduction of 15 non-anti-psychotic drug fills (relative reduction = 5.6%). Reductions in the number of prescriptions filled for antidiabetics, antihypertensives, and lipid-lowering agents were 4-11-fold higher than corresponding reductions for anti-psychotics. LIMITATIONS: Because federal law requires pharmacists to fill medications for Medicaid patients regardless of the ability to pay, these results may under-estimate the true impact of copayment increases. CONCLUSIONS: Medicaid prescription copayment increases resulted in only a minimal decline in anti-psychotic medication use, but much larger reductions in use of other medications, particularly cardiometabolic medications.