Meaningful Use and Hospital Performance on Post-Acute Utilization Indicators.

OBJECTIVES: To examine trends in hospital post-acute utilization indicators and to determine whether improvement in these indicators is associated with attesting to meaningful use (MU). DATA SOURCES: Medicare claims-based, repeated measures on 30-day hospital-wide all-cause readmission and emergency department (ED) utilization rates for 160 short-stay hospitals (2009-2012); Medicare EHR Incentive Program Payments files (2011-2012); and other hospital and market data. STUDY DESIGN: Interrupted time series with concurrent comparison group. PRINCIPAL FINDINGS: Propensity score-weighted multilevel models for change demonstrate that 30-day readmission rates (unadjusted) fell from 13.4 percent in 2009 to 12.1 percent in 2012. Similarly, 30-day ED utilization declined from 18.9 percent to 17.3 percent during the same period. However, MU and non-MU hospitals were indistinguishable vis-à-vis performance. Controlling for hospital and market characteristics, MU was unrelated to 30-day readmission. In contrast, 30-day ED utilization deteriorated. CONCLUSIONS: Hospitals with MU Stage 1 designation did not show significantly higher improvement on post-acute utilization compared to their counterparts without. To achieve gains in quality and safety, potentially associated with EHRs, and to advance care coordination and patient engagement, the regulators should strengthen accountability by linking comprehensive, outcomes-based performance measures to specific MU objectives.

Examining Measures of Income and Poverty in Medicare Administrative Data.

Disparities by economic status are observed in the health status and health outcomes of Medicare beneficiaries. For health services and health policy researchers, one barrier to addressing these disparities is the ability to use Medicare data to ascertain information about an individual's income level or poverty, because Medicare administrative data contains limited information about individual economic status. Information gleaned from other sources-such as the Medicaid and Supplemental Security Income programs-can be used in some cases to approximate the income of Medicare beneficiaries. However, such information is limited in its availability and applicability to all beneficiaries. Neighborhood-level measures of income can be used to infer individual-level income, but level of neighborhood aggregation impacts accuracy and usability of the data. Community-level composite measures of economic status have been shown to be associated with health and health outcomes of Medicare beneficiaries and may capture neighborhood effects that are separate from individual effects, but are not readily available in Medicare data and do not serve to replace information about individual economic status. There is no single best method of obtaining income data from Medicare files, but understanding strengths and limitations of different approaches to identifying economic status will help researchers choose the best method for their particular purpose, and help policymakers interpret studies using measures of income.

Examining Race and Ethnicity Information in Medicare Administrative Data.

Racial and ethnic disparities are observed in the health status and health outcomes of Medicare beneficiaries. Reducing these disparities is a national priority, and having high-quality data on individuals' race and ethnicity is critical for researchers working to do so. However, using Medicare data to identify race and ethnicity is not straightforward. Currently, Medicare largely relies on Social Security Administration data for information about Medicare beneficiary race and ethnicity. Directly self-reported race and ethnicity information is collected for subsets of Medicare beneficiaries but is not explicitly collected for the purpose of populating race/ethnicity information in the Medicare administrative record. As a consequence of historical data collection practices, the quality of Medicare's administrative data on race and ethnicity varies substantially by racial/ethnic group; the data are generally much more accurate for whites and blacks than for other racial/ethnic groups. Identification of Hispanic and Asian/Pacific Islander beneficiaries has improved through use of an imputation algorithm recently applied to the Medicare administrative database. To improve the accuracy of race/ethnicity data for Medicare beneficiaries, researchers have developed techniques such as geocoding and surname analysis that indirectly assign Medicare beneficiary race and ethnicity. However, these techniques are relatively new and data may not be widely available. Understanding the strengths and limitations of different approaches to identifying race and ethnicity will help researchers choose the best method for their particular purpose, and help policymakers interpret studies using these measures.

Defining Rurality in Medicare Administrative Data.

Rural beneficiaries make up nearly one quarter of the Medicare population, yet rural providers and patients face specific challenges with health and health care delivery that remain inadequately understood. Health disparities between rural and urban residents are widespread, barriers to health care in rural communities persist, and the rural health care workforce is limited. To better understand and track the relationship between rurality and performance under Medicare's payment programs, researchers must be able to identify rural beneficiaries, providers, and hospitals. Although numerous definitions of rurality are applied across the Medicare program, empirical research is lacking comparing the different definitions of rurality and the impact of their application to quality, outcome, or costs. Definitions that recognize rurality as a graded concept, rather than a dichotomous one, hold promise. Understanding the strengths and limitations of different approaches to identifying rurality will help researchers choose the best method for their particular purpose, and help policymakers interpret studies using these approaches.

Concentration of high-cost patients in hospitals and markets.

OBJECTIVES: Although we know that healthcare costs are concentrated among a small number of patients, we know much less about the concentration of these costs among providers or markets. This is important because it could help us to understand why some patients are higher-cost compared with others and enable us to develop interventions to reduce costs for these patients. STUDY DESIGN: Observational study. METHODS: We used a 20% sample of Medicare fee-for-service claims data from 2011 and 2012, and defined high-cost patients as those in the top 10% of standardized costs. We then characterized high-concentration hospitals as those with the highest proportion of high-cost patient claims, and high-concentration markets as the Hospital Referral Regions (HRRs) with the highest proportion of high-cost patients. We compared the characteristics and outcomes of each. RESULTS: High-concentration hospitals had 69% of their inpatient Medicare claims from high-cost Medicare beneficiaries compared with 51% for the remaining 90% of hospitals. These hospitals were more likely to be for-profit and major teaching hospitals, located in urban settings, and have higher readmission rates. High-concentration HRRs had 13% high-cost patients compared with 9.5% for the remaining 90% of HRRs. These HRRs had a smaller supply of total physicians, a greater supply of cardiologists, higher rates of emergency department visits, and significantly higher expenditures on care in the last 6 months of life. CONCLUSIONS: High-cost beneficiaries are only modestly concentrated in specific hospitals and healthcare markets.

Association of US Centers for Medicare and Medicaid Services Hospital 30-Day Risk-Standardized Readmission Metric With Care Quality and Outcomes After Acute Myocardial Infarction: Findings From the National Cardiovascular Data Registry/Acute Coronary Treatment and Intervention Outcomes Network Registry-Get With the Guidelines.

Importance: The US Centers for Medicare and Medicaid Services Hospital Readmissions Reduction Program penalizes hospitals with higher-than-expected risk-adjusted 30-day readmission rates (excess readmission ratio [ERR] > 1) after acute myocardial infarction (MI). However, the association of ERR with MI care processes and outcomes are not well established. Objective: To evaluate the association between ERR for MI with in-hospital process of care measures and 1-year clinical outcomes. Design, Setting, and Participants: Observational analysis of hospitalized patients with MI from National Cardiovascular Data Registry/Acute Coronary Treatment and Intervention Outcomes Network Registry-Get With the Guidelines centers subject to the first cycle of the Hospital Readmissions Reduction Program between July 1, 2008, and June 30, 2011. Exposures: The ERR for MI (MI-ERR) in 2011. Main Outcomes and Measures: Adherence to process of care measures during index hospitalization in the overall study population and risk of the composite outcome of mortality or all-cause readmission within 1 year of discharge and its individual components among participants with available Centers for Medicare and Medicaid Services-linked data. Results: The median ages of patients in the MI-ERR greater than 1 and tertiles 1, 2, and 3 of the MI-ERR greater than 1 groups were 64, 63, 64, and 63 years, respectively. Among 380 hospitals that treated a total of 176 644 patients with MI during the study period, 43% had MI-ERR greater than 1. The proportions of patients of black race, those with heart failure signs at admission, and bleeding complications increased with higher MI-ERR. There was no significant association between adherence to MI performance measures and MI-ERR (adjusted odds ratio, 0.94; 95% CI, 0.81-1.08, per 0.1-unit increase in MI-ERR for overall defect-free care). Among the 51 453 patients with 1-year outcomes data available, higher MI-ERR was associated with higher adjusted risk of the composite outcome and all-cause readmission within 1 year of discharge. This association was largely driven by readmissions early after discharge and was not significant in landmark analyses beginning 30 days after discharge. The MI-ERR was not associated with risk for mortality within 1 year of discharge in the overall and 30-day landmark analyses. Conclusions and Relevance: During the first cycle of the Hospital Readmissions Reduction Program, participating hospitals' risk-adjusted 30-day readmission rates following MI were not associated with in-hospital quality of MI care or clinical outcomes occurring after the first 30 days after discharge.

Safety-net Hospitals Face More Barriers Yet Use Fewer Strategies to Reduce Readmissions.

OBJECTIVE: US hospitals that care for vulnerable populations, "safety-net hospitals" (SNHs), are more likely to incur penalties under the Hospital Readmissions Reduction Program, which penalizes hospitals with higher-than-expected readmissions. Understanding whether SNHs face unique barriers to reducing readmissions or whether they underuse readmission-prevention strategies is important. DESIGN: We surveyed leadership at 1600 US acute care hospitals, of whom 980 participated, between June 2013 and January 2014. Responses on 28 questions on readmission-related barriers and strategies were compared between SNHs and non-SNHs, adjusting for nonresponse and sampling strategy. We further compared responses between high-performing SNHs and low-performing SNHs. RESULTS: We achieved a 62% response rate. SNHs were more likely to report patient-related barriers, including lack of transportation, homelessness, and language barriers compared with non-SNHs (P-values<0.001). Despite reporting more barriers, SNHs were less likely to use e-tools to share discharge summaries (70.1% vs. 73.7%, P<0.04) or verbally communicate (31.5% vs. 39.8%, P<0.001) with outpatient providers, track readmissions by race/ethnicity (23.9% vs. 28.6%, P<0.001), or enroll patients in postdischarge programs (13.3% vs. 17.2%, P<0.001). SNHs were also less likely to use discharge coordinators, pharmacists, and postdischarge programs. When we examined the use of strategies within SNHs, we found trends to suggest that high-performing SNHs were more likely to use several readmission strategies. CONCLUSIONS: Despite reporting more barriers to reducing readmissions, SNHs were less likely to use readmission-reduction strategies. This combination of higher barriers and lower use of strategies may explain why SNHs have higher rates of readmissions and penalties under the Hospital Readmissions Reduction Program.

Segmenting high-cost Medicare patients into potentially actionable cohorts.

BACKGROUND: Providers are assuming growing responsibility for healthcare spending, and prior studies have shown that spending is concentrated in a small proportion of patients. Using simple methods to segment these patients into clinically meaningful subgroups may be a useful and accessible strategy for targeting interventions to control costs. METHODS: Using Medicare fee-for-service claims from 2011 (baseline year, used to determine comorbidities and subgroups) and 2012 (spending year), we used basic demographics and comorbidities to group beneficiaries into 6 cohorts, defined by expert opinion and consultation: under-65 disabled/ESRD, frail elderly, major complex chronic, minor complex chronic, simple chronic, and relatively healthy. We considered patients in the highest 10% of spending to be "high-cost." RESULTS: 611,245 beneficiaries were high-cost; these patients were less often white (76.2% versus 80.9%) and more often dually-eligible (37.0% versus 18.3%). By segment, frail patients were the most likely (46.2%) to be high-cost followed by the under-65 (14.3%) and major complex chronic groups (11.1%); fewer than 5% of the beneficiaries in the other cohorts were high-cost in the spending year. The frail elderly ($70,196) and under-65 disabled/ESRD ($71,210) high-cost groups had the highest spending; spending in the frail high-cost group was driven by inpatient ($23,704) and post-acute care ($24,080), while the under 65-disabled/ESRD spent more through part D costs ($23,003). CONCLUSIONS: Simple criteria can segment Medicare beneficiaries into clinically meaningful subgroups with different spending profiles. IMPLICATIONS: Under delivery system reform, interventions that focus on frail or disabled patients may have particularly high value as providers seek to reduce spending. LEVEL OF EVIDENCE: IV.

Opinions on the Hospital Readmission Reduction Program: results of a national survey of hospital leaders.

OBJECTIVES: To determine the opinions of US hospital leadership on the Hospital Readmissions Reduction Program (HRRP), a national mandatory penalty-for-performance program. STUDY DESIGN: We developed a survey about federal readmission policies. We used a stratified sampling design to oversample hospitals in the highest and lowest quintile of performance on readmissions, and hospitals serving a high proportion of minority patients. METHODS: We surveyed leadership at 1600 US acute care hospitals that were subject to the HRRP, and achieved a 62% response rate. Results were stratified by the size of the HRRP penalty that hospitals received in 2013, and adjusted for nonresponse and sampling strategy. RESULTS: Compared with 36.1% for public reporting of readmission rates and 23.7% for public reporting of discharge processes, 65.8% of respondents reported that the HRRP had a "great impact" on efforts to reduce readmissions. The most common critique of the HRRP penalty was that it did not adequately account for differences in socioeconomic status between hospitals (75.8% "agree" or "agree strongly"); other concerns included that the penalties were "much too large" (67.7%), and hospitals' inability to impact patient adherence (64.1%). These sentiments were each more common in leaders of hospitals with higher HRRP penalties. CONCLUSIONS: The HRRP has had a major impact on hospital leaders' efforts to reduce readmission rates, which has implications for the design of future quality improvement programs. However, leaders are concerned about the size of the penalties, lack of adjustment for socioeconomic and clinical factors, and hospitals' inability to impact patient adherence and postacute care. These concerns may have implications as policy makers consider changes to the HRRP, as well as to other Medicare value-based payment programs that contain similar readmission metrics.