Patricia S. Keenan

Trends in length of stay and short-term outcomes among Medicare patients hospitalized for heart failure, 1993-2006.

CONTEXT Whether decreases in the length of stay during the past decade for patients with heart failure (HF) may be associated with changes in outcomes is unknown. OBJECTIVE To describe the temporal changes in length of stay, discharge disposition, and short-term outcomes among older patients hospitalized for HF. DESIGN, SETTING, AND PARTICIPANTS An observational study of 6,955,461 Medicare fee-for-service hospitalizations for HF between 1993 and 2006, with a 30-day follow-up. MAIN OUTCOME MEASURES Length of hospital stay, in-patient and 30-day mortality, and 30-day readmission rates. RESULTS Between 1993 and 2006, mean length of stay decreased from 8.81 days (95% confidence interval [CI], 8.79-8.83 days) to 6.33 days (95% CI, 6.32-6.34 days). In-hospital mortality decreased from 8.5% (95% CI, 8.4%-8.6%) in 1993 to 4.3% (95% CI, 4.2%-4.4%) in 2006, whereas 30-day mortality decreased from 12.8% (95% CI, 12.8%-12.9%) to 10.7% (95% CI, 10.7%-10.8%). Discharges to home or under home care service decreased from 74.0% to 66.9% and discharges to skilled nursing facilities increased from 13.0% to 19.9%. Thirty-day readmission rates increased from 17.2% (95% CI, 17.1%-17.3%) to 20.1% (95% CI, 20.0%-20.2%; all P < .001). Consistent with the unadjusted analyses, the 2005-2006 risk-adjusted 30-day mortality risk ratio was 0.92 (95% CI, 0.91-0.93) compared with 1993-1994, and the 30-day readmission risk ratio was 1.11 (95% CI, 1.10-1.11). CONCLUSION For patients admitted with HF during the past 14 years, reductions in length of stay and in-hospital mortality, less marked reductions in 30-day mortality, and changes in discharge disposition accompanied by increases in 30-day readmission rates were observed.

An Administrative Claims Measure Suitable for Profiling Hospital Performance on the Basis of 30-Day All-Cause Readmission Rates Among Patients With Heart Failure

Background—Readmission soon after hospital discharge is an expensive and often preventable event for patients with heart failure. We present a model approved by the National Quality Forum for the purpose of public reporting of hospital-level readmission rates by the Centers for Medicare & Medicaid Services. Methods and Results—We developed a hierarchical logistic regression model to calculate hospital risk-standardized 30-day all-cause readmission rates for patients hospitalized with heart failure. The model was derived with the use of Medicare claims data for a 2004 cohort and validated with the use of claims and medical record data. The unadjusted readmission rate was 23.6%. The final model included 37 variables, had discrimination ranging from 15% observed 30-day readmission rate in the lowest predictive decile to 37% in the upper decile, and had a c statistic of 0.60. The 25th and 75th percentiles of the risk-standardized readmission rates across 4669 hospitals were 23.1% and 24.0%, with 5th and 95th percentiles of 22.2% and 25.1%, respectively. The odds of all-cause readmission for a hospital 1 standard deviation above average was 1.30 times that of a hospital 1 standard deviation below average. State-level adjusted readmission rates developed with the use of the claims model are similar to rates produced for the same cohort with the use of a medical record model (correlation, 0.97; median difference, 0.06 percentage points). Conclusions—This claims-based model of hospital risk-standardized readmission rates for heart failure patients produces estimates that may serve as surrogates for those derived from a medical record model.

Relationship Between Hospital Readmission and Mortality Rates for Patients Hospitalized With Acute Myocardial Infarction, Heart Failure, or Pneumonia

IMPORTANCE The Centers for Medicare & Medicaid Services publicly reports hospital 30-day, all-cause, risk-standardized mortality rates (RSMRs) and 30-day, all-cause, risk-standardized readmission rates (RSRRs) for acute myocardial infarction, heart failure, and pneumonia. The evaluation of hospital performance as measured by RSMRs and RSRRs has not been well characterized. OBJECTIVE To determine the relationship between hospital RSMRs and RSRRs overall and within subgroups defined by hospital characteristics. DESIGN, SETTING, AND PARTICIPANTS We studied Medicare fee-for-service beneficiaries discharged with acute myocardial infarction, heart failure, or pneumonia between July 1, 2005, and June 30, 2008 (4506 hospitals for acute myocardial infarction, 4767 hospitals for heart failure, and 4811 hospitals for pneumonia). We quantified the correlation between hospital RSMRs and RSRRs using weighted linear correlation; evaluated correlations in groups defined by hospital characteristics; and determined the proportion of hospitals with better and worse performance on both measures. MAIN OUTCOME MEASURES Hospital 30-day RSMRs and RSRRs. RESULTS Mean RSMRs and RSRRs, respectively, were 16.60% and 19.94% for acute myocardial infarction, 11.17% and 24.56% for heart failure, and 11.64% and 18.22% for pneumonia. The correlations between RSMRs and RSRRs were 0.03 (95% CI, -0.002 to 0.06) for acute myocardial infarction, -0.17 (95% CI, -0.20 to -0.14) for heart failure, and 0.002 (95% CI, -0.03 to 0.03) for pneumonia. The results were similar for subgroups defined by hospital characteristics. Although there was a significant negative linear relationship between RSMRs and RSRRs for heart failure, the shared variance between them was only 2.9% (r2 = 0.029), with the correlation most prominent for hospitals with RSMR <11%. CONCLUSION AND RELEVANCE Risk-standardized mortality rates and readmission rates were not associated for patients admitted with an acute myocardial infarction or pneumonia and were only weakly associated, within a certain range, for patients admitted with heart failure.

Is same-hospital readmission rate a good surrogate for all-hospital readmission rate?

Background:The Centers for Medicare & Medicaid Services (CMS) readmission measure is based on all-cause readmissions to any hospital within 30 days of discharge. Whether a measure based on same-hospital readmission, an outcome that is easier for hospitals and some systems to track, could serve as a proxy for the all-hospital measure is not known. Objectives:Evaluate whether same-hospital readmission rate is a good surrogate for all-hospital readmission rate. Research Design:The study population was derived from the Medicare inpatient, outpatient, and carrier (physician) Standard Analytic Files. Thirty-day risk-standardized readmission rates (RSRRs) for heart failure (HF) for both all-hospital readmission and same-hospital readmission were assessed by using hierarchical logistic regression models. Subjects:The sample consisted of 501,234 hospitalizations in 4674 hospitals with at least 1 hospitalization. Measures:Thirty-day readmission was defined as occurrence of at least 1 hospitalization in any US acute care hospital for any cause within 30 days of discharge after an index hospitalization. Same-hospital readmission was considered if the patient was admitted to the hospital that produced the original discharge within 30 days. Results:Overall, 80.9% of all HF readmissions occurred in the same- hospital, whereas 19.1% of readmissions occurred in a different hospital. The mean difference between all- versus same-hospital RSRR was 4.7 ± 1.0%, ranging from 0.9% to 10.5% across these hospitals with 25th, 50th, and 75th percentiles of 4.1%, 4.7%, and 5.2%, respectively, and was variable across the range of average RSRR. Conclusion:Same-hospital readmission rate is an unreliable and biased indicator of all-hospital readmission rate with limited value as a benchmark for quality of care processes.

Smoking and Weight Change After New Health Diagnoses in Older Adults

BACKGROUND Smoking and patterns of diet and activity are the 2 leading underlying causes of death in the United States, yet the factors that prompt individuals to adopt healthier habits are not well understood. METHODS This study was undertaken to determine whether individuals who have experienced recent adverse health events are more likely to quit smoking or to lose weight than those without recent events using Health and Retirement Study panel survey data for 20 221 overweight or obese individuals younger than 75 years and 7764 smokers from 1992 to 2000. RESULTS In multivariate analyses, adults with recent diagnoses of stroke, cancer, lung disease, heart disease, or diabetes mellitus were 3.2 times more likely to quit smoking than were individuals without new diagnoses (P < .001). Among overweight or obese individuals younger than 75 years, those with recent diagnoses of lung disease, heart disease, or diabetes mellitus lost -0.35 U of body mass index (calculated as weight in kilograms divided by height in meters squared) compared with those without these new diagnoses (P < .001). Smokers with multiple new diagnoses were 6 times more likely to quit smoking compared with those with no new diagnoses. The odds of quitting smoking were 5 times greater in individuals with a new diagnosis of heart disease, and body mass index declined by 0.6 U in overweight or obese individuals with a new diagnosis of diabetes mellitus (P < .001). CONCLUSIONS Across a range of health conditions, new diagnoses can serve as a window of opportunity that prompts older adults to change health habits, in particular, to quit smoking. Quality improvement efforts targeting secondary as well as primary prevention through the health care system are likely well founded.

Quality assessments by sick and healthy beneficiaries in traditional Medicare and Medicare managed care.

Background:The Centers for Medicare & Medicaid Services pays for services provided through traditional fee-for-service (FFS) Medicare and managed care plans (Medicare Advantage [MA]). It is important to understand how financing and organizational arrangements relate to quality of care. Objectives:To compare care experiences and preventive services receipt in traditional Medicare and MA for healthy and sick beneficiaries. Methods:Randomly selected beneficiaries responded to the 2003 and 2004 Consumer Assessments of Healthcare Providers and Systems (CAHPS®) surveys. We analyzed 237,221 MA responses (80% response rate) and 153,535 from FFS (68% response rate). We compared case-mix-adjusted CAHPS scores between FFS and MA for healthy and sick beneficiaries on 7 CAHPS measures of care experiences and 3 preventive service measures. Results:CAHPS scores were lower in MA than FFS for all care experience measures except office wait time. The sick had less favorable care experiences than the healthy for all measures, but were more likely to receive each preventive service (P < 0.001). FFS-MA differences were larger for the sick than the healthy for 5 of 7 experience measures (P < 0.05), and were twice as large for physician ratings and interactions. Office wait time and rates of immunization were better in MA than FFS (P < 0.001), with no differences between healthy and sick groups. Conclusions:Beneficiaries in health plans report less favorable care experiences than those in FFS, particularly among the sick, but preventive service measures are higher in MA. The Centers for Medicare and Medicaid Services should strengthen efforts to improve care experiences of the sick, particularly in MA, and preventive service receipt in FFS.

Early experiences with consumer engagement initiatives to improve chronic care.

Engaging consumers to be more active participants in their health and health care is an appealing strategy for reforming the U.S. health care system, but little is known about how to mount and sustain communitywide consumer engagement initiatives. The Robert Wood Johnson Foundation launched a program in 2006 in fourteen communities to align forces around improving quality and efficiency by promoting public reporting and expanding the involvement of consumers in all facets of their care. These multistakeholder organizations provide an early glimpse into the opportunities and challenges that lie ahead as policymakers attempt to integrate consumers more completely in their reform strategies.

Dual use of Veterans Affairs services and use of recommended ambulatory care.

Background:Use of more than one health care system to obtain care is common among adults receiving care within the Veterans Affairs (VA) medical system. It is not known what effect using care from multiple sources has on the quality of care patients receive. Objectives:To examine whether use of recommended ambulatory care services differs between exclusive and dual VA users. Methods:Cross-sectional analysis of the 2004 Behavior Risk Factor Surveillance System, a nationally-representative survey of community-dwelling adults aged 18 years or older. Our outcome measures were self-reported use of 18 recommended services for cancer prevention, cardiovascular risk reduction, diabetes management, and infectious disease prevention. We used multivariable logistic regression to examine the association between exclusive and dual VA use and use of recommended ambulatory services. Results:There were 3470 exclusive VA users and 4523 dual VA users. Dual users were significantly more likely to be older and white, have higher incomes, have graduated from college, and be insured when compared with exclusive VA users. In unadjusted analyses, dual users received higher rates of recommended services. After adjustment for patient characteristics, use of recommended services was largely similar among exclusive and dual VA users. Exclusive VA users reported 14% greater use of breast cancer screening and 10% greater use of cholesterol monitoring among patients with hypercholesterolemia, and 6% lower use of prostate cancer screening and 7% lower use of influenza vaccination. Conclusions:After adjustment for patient characteristics, exclusive and dual VA users reported similar rates of recommended ambulatory service use.

Mortality and Readmission for Patients With Heart Failure Among U.S. News & World Report's Top Heart Hospitals

Background—The rankings of “America’s Best Hospitals” by U.S. News & World Report are influential, but the performance of ranked hospitals in caring for patients with routine cardiac conditions such as heart failure is not known. Methods and Results—Using hierarchical regression models based on medical administrative data from the period July 1, 2005, to June 30, 2006, we calculated risk-standardized mortality rates and risk-standardized readmission rates for ranked and nonranked hospitals in the treatment of heart failure. The mortality analysis examined 14813 patients in 50 ranked hospitals and 409806 patients in 4761 nonranked hospitals. The readmission analysis included 16641 patients in 50 ranked hospitals and 458473 patients in 4627 nonranked hospitals. Mean 30-day risk-standardized mortality rates were lower in ranked versus nonranked hospitals (10.1% versus 11.2%, P<0.01), whereas mean 30-day risk-standardized readmission rates were no different between ranked and nonranked hospitals (23.6% versus 23.8%, P=0.40). The 30-day risk-standardized mortality rates varied widely for both ranked and nonranked hospitals, ranging from 7.9% to 12.4% for ranked hospitals and from 7.1% to 17.5% for nonranked hospitals. The 30-day risk-standardized readmission rates also spanned a large range, from 18.7% to 29.3% for ranked hospitals and from 19.2% to 29.8% for nonranked hospitals. Conclusions—Hospitals ranked by U.S. News & World Report as “America’s Best Hospitals” in “Heart & Heart Surgery” are more likely than nonranked hospitals to have a significantly lower than expected 30-day mortality rate, but there was much overlap in performance. For readmission, the rates were similar in ranked and nonranked hospitals.

Charity Care, Risk Pooling, and the Decline in Private Health Insurance

Over the past several decades health-care costs have increased substantially, and the share of the population with insurance coverage has decreased. Relative to GDP, medical care today accounts for 75 percent more of the economy than it did in 1980. At the same time, the share of the non-elderly population that is uninsured has increased by roughly 4 percentage points since 1987. To what extent is the increase in the cost of health insurance responsible for the decline in coverage? Individuals will purchase coverage if the utility of being insured exceeds that of being uninsured. Textbook economic theory suggests that rising medical expenditures ought to increase the utility of coverage because insurance mitigates risk. If the variability of spending rises as medical care costs increase, insurance becomes more valuable (Charles Phelps, 1997). The rising demand for pharmaceutical coverage following the rise in spending on pharmaceuticals is consistent with this model. In the textbook model, rising costs would be associated with falling coverage only if the cost increases were driven by increased administrative loads. Empirically, though, most medical spending is a result of increased quantities of care received, owing to technological changes in medicine, not greater administrative burden (Cutler and Mark McClellan, 2001). There are two extensions to the textbook model that can help explain the inverse relationship between premiums and insurance coverage. The first is the hypothesis that the value of new services is not sufficiently high to justify their costs, and thus some people rationally decline coverage when costs increase. Of course, if consumers had the option, they would exclude unvalued care from the insurance policy, but determining which care is valuable and which is not may be difficult, and contracting imperfections may make it difficult for individuals to purchase a plan that limits access to particular services. One type of unvalued care is traditional moral hazard: services are provided because of insurance but are worth less than they cost. Cutler (2004) shows that increased medical spending overall has bought care that is more than worth its value. But that does not imply that the differential growth of service use in some areas † Discussants: Amitabh Chandra, Dartmouth College; Ellen Meara, Harvard Medical School; Helen Levy, University of Chicago; Darius Lakdawalla, RAND Corporation.