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Objectives The objectives of this study were to compare risk-standardized hospital visit ratios of the predicted to expected number of unplanned hospital visits within 7 days of same-day surgeries performed at US hospital outpatient departments (HOPDs) and to describe the causes of hospital visits. Summary of background data More than half of procedures in the US are performed in outpatient settings, yet little is known about facility-level variation in short-term safety outcomes. Methods The study cohort included 1,135,441 outpatient surgeries performed at 4058 hospitals between October 1, 2015 and September 30, 2016 among Medicare Fee-for-Service beneficiaries aged ≥65 years. Hospital-level, risk-standardized measure scores of unplanned hospital visits (emergency department visits, observation stays, and unplanned inpatient admissions) within 7 days of hospital outpatient surgery were calculated using hierarchical logistic regression modeling that adjusted for age, clinical comorbidities, and surgical procedural complexity. Results Overall, 7.8% of hospital outpatient surgeries were followed by an unplanned hospital visit within 7 days. Many of the leading reasons for unplanned visits were for potentially preventable conditions, such as urinary retention, infection, and pain. We found considerable variation in the risk-standardized ratio score across hospitals. The 203 best-performing HOPDs, at or below the 5th percentile, had at least 22% fewer unplanned hospital visits than expected, whereas the 202 worst-performing HOPDs, at or above the 95th percentile, had at least 29% more post-surgical visits than expected, given their case and surgical procedure mix. Conclusions Many patients experience an unplanned hospital visit within 7 days of hospital outpatient surgery, often for potentially preventable reasons. The observed variation in performance across hospitals suggests opportunities for quality improvement.

Policy makers are increasingly using performance feedback that compares physicians to their peers as part of payment policy reforms. However, it is not known whether peer comparisons can improve br...

BackgroundDisparities in multimorbidity prevalence indicate health inequalities. This study sought to determine if gaps in multimorbidity prevalence by race and ethnicity are decreasing over time.Methods and findingsSerial cross-sectional analysis of the National Health Interview Survey from years 1999 to 2018. The study included individuals ≥18 years old, categorized by self-reported race, ethnicity, age, and income. The main outcomes were temporal trends in multimorbidity prevalence based on the self-reported presence of ≥2 of 9 chronic conditions. The study sample included 596,355 individuals (4.7% Asian, 11.8% Black, 13.8% Latino/Hispanic, and 69.7% White). In 1999, the estimated prevalence of multimorbidity was 5.9% among Asian, 17.4% among Black, 10.7% among Latino/Hispanic, and 13.5% among White individuals. Prevalence increased for all racial/ethnic groups during the study period (PConclusionsFrom 1999 to 2018, a period of increasing multimorbidity prevalence for all the groups studied, there has not been any significant progress in eliminating disparities between Black and White people. The disparity began among young adults. Public health interventions that prevent the onset of chronic conditions in early life may be needed to eliminate these disparities.

BACKGROUND: The Merit-based Incentive Payment System (MIPS) incorporates financial incentives and penalties intended to drive clinicians towards value-based purchasing, including alternative payment models (APMs). Newly available Medicare-approved qualified clinical data registries (QCDRs) offer specialty-specific quality measures for clinician reporting, yet their impact on clinician performance and payment adjustments remains unknown. OBJECTIVES: We sought to characterize clinician participation, performance, and payment adjustments in the MIPS program across specialties, with a focus on clinician use of QCDRs. RESEARCH DESIGN: We performed a cross-sectional analysis of the 2018 MIPS program. RESULTS: During the 2018 performance year, 558,296 clinicians participated in the MIPS program across the 35 specialties assessed. Clinicians reporting as individuals had lower overall MIPS performance scores (median [interquartile range (IQR)], 80.0 [39.4–98.4] points) than those reporting as groups (median [IQR], 96.3 [76.9–100.0] points), who in turn had lower adjustments than clinicians reporting within MIPS APMs (median [IQR], 100.0 [100.0–100.0] points) (P

ImportanceAdjusting quality measures used in pay-for-performance programs for social risk factors remains controversial.ObjectiveTo illustrate a structured, transparent approach to decision-making about adjustment for social risk factors for a measure of clinician quality that assesses acute admissions for patients with multiple chronic conditions (MCCs).Design, Setting, and ParticipantsThis retrospective cohort study used 2017 and 2018 Medicare administrative claims and enrollment data, 2013 to 2017 American Community Survey data, and 2018 and 2019 Area Health Resource Files. Patients were Medicare fee-for-service beneficiaries 65 years or older with at least 2 of 9 chronic conditions (acute myocardial infarction, Alzheimer disease/dementia, atrial fibrillation, chronic kidney disease, chronic obstructive pulmonary disease or asthma, depression, diabetes, heart failure, and stroke/transient ischemic attack). Patients were attributed to clinicians in the Merit-Based Incentive Payment System (MIPS; primary health care professionals or specialists) using a visit-based attribution algorithm. Analyses were conducted between September 30, 2017, and August 30, 2020.ExposuresSocial risk factors included low Agency for Healthcare Research and Quality Socioeconomic Status Index, low physician-specialist density, and Medicare-Medicaid dual eligibility.Main Outcomes and MeasuresNumber of acute unplanned hospital admissions per 100 person-years at risk for admission. Measure scores were calculated for MIPS clinicians with at least 18 patients with MCCs assigned to them.ResultsThere were 4 659 922 patients with MCCs (mean [SD] age, 79.0 [8.0] years; 42.5% male) assigned to 58 435 MIPS clinicians. The median (IQR) risk-standardized measure score was 38.9 (34.9-43.6) per 100 person-years. Social risk factors of low Agency for Healthcare Research and Quality Socioeconomic Status Index, low physician-specialist density, and Medicare-Medicaid dual eligibility were significantly associated with the risk of hospitalization in the univariate models (relative risk [RR], 1.14 [95% CI, 1.13-1.14], RR, 1.05 [95% CI, 1.04-1.06], and RR, 1.44 [95% CI, 1.43-1.45], respectively), but the association was attenuated in adjusted models (RR, 1.11 [95% CI 1.11-1.12] for dual eligibility). Across MIPS clinicians caring for variable proportions of dual-eligible patients with MCCs (quartile 1, 0%-3.1%; quartile 2, >3.1%-9.5%; quartile 3, >9.5%-24.5%, and quartile 4, >24.5%-100%), median measure scores per quartile were 37.4, 38.6, 40.0, and 39.8 per 100 person-years, respectively. Balancing conceptual considerations, empirical findings, programmatic structure, and stakeholder input, the Centers for Medicare & Medicaid Services decided to adjust the final model for the 2 area-level social risk factors but not dual Medicare-Medicaid eligibility.Conclusions and RelevanceThis cohort study demonstrated that adjustment for social risk factors in outcome measures requires weighing high-stake, competing concerns. A structured approach that includes evaluation of conceptual and contextual factors, as well as empirical findings, with active engagement of stakeholders can be used to make decisions about social risk factor adjustment.

To evaluate whether the implementation of a new population-based primary care payment system, Population-Based Payments for Primary Care (3PC), initiated by Hawaii Medical Service Association (HMSA; the Blue Cross Blue Shield of Hawaii), was associated with changes in spending and utilization for outpatient imaging in its first year.In this observational study, we used claims data from January 1, 2012, to December 31, 2016. We used a propensity-weighted difference-in-differences design to compare 70,284 HMSA patients in Hawaii attributed to 107 primary care physicians (PCPs) and 4 physician organizations participating in 3PC in its first year of implementation (2016) and 195,902 patients attributed to 312 PCPs and 14 physician organizations that used a fee-for-service model during the study period. The primary outcome was total spending on outpatient imaging tests, and secondary outcomes included spending and utilization by modality.The study included 266,186 HMSA patients (mean age of 43.3 years; 51.7% women) and 419 PCPs (mean age of 54.9 years; 34.8% women). The 3PC system was not significantly associated with changes in total spending for outpatient imaging. Of 12 secondary outcomes, only 3 were statistically significant, including changes in nuclear medicine spending (adjusted differential change = -20.1% [95% confidence interval = -27.5% to -12.1%]; P.001) and utilization (adjusted differential change = -18.1% [95% confidence interval = -23.8 to -11.9%]; P.001).The HMSA 3PC system was not associated with significant changes in total spending for outpatient imaging, though spending and utilization on nuclear medicine tests decreased.

Policy makers are increasingly using performance feedback that compares physicians to their peers as part of payment policy reforms. However, it is not known whether peer comparisons can improve broad outcomes, beyond changing specific individual behaviors such as reducing inappropriate prescribing of antibiotics. We conducted a cluster-randomized controlled trial with Blue Cross Blue Shield of Hawaii to examine the impact of providing peer comparisons feedback on the quality of care to primary care providers in the setting of a shift from fee-for-service to population-based payment. Over 74,000 patients and eighty-eight primary care providers across sixty-three sites were included over a period of nine months in 2016. Patients in the peer comparisons intervention group experienced a 3.1-percentage-point increase in quality scores compared to the control group-whose members received individual feedback only. This result underscores the effectiveness of peer comparisons as a way to improve health care quality, and it supports Medicare's decisions to provide comparative feedback as part of recently implemented primary care and specialty payment reform programs.

Safety-net and teaching hospitals are somewhat more likely to be penalized for excess readmissions, but the association of other hospital characteristics with readmission rates is uncertain and may have relevance for hospital-centered interventions.To examine the independent association of 8 hospital characteristics with hospital-wide 30-day risk-standardized readmission rate (RSRR).This is a retrospective cross-sectional multivariable analysis.US hospitals.Centers for Medicare and Medicaid Services specification of hospital-wide RSRR from July 1, 2013 through June 30, 2014 with race and Medicaid dual-eligibility added.We included 6,789,839 admissions to 4474 hospitals of Medicare fee-for-service beneficiaries aged over 64 years. In multivariable analyses, there was regional variation: hospitals in the mid-Atlantic region had the highest RSRRs [0.98 percentage points higher than hospitals in the Mountain region; 95% confidence interval (CI), 0.84-1.12]. For-profit hospitals had an average RSRR 0.38 percentage points (95% CI, 0.24-0.53) higher than public hospitals. Both urban and rural hospitals had higher RSRRs than those in medium metropolitan areas. Hospitals without advanced cardiac surgery capability had an average RSRR 0.27 percentage points (95% CI, 0.18-0.36) higher than those with. The ratio of registered nurses per hospital bed was not associated with RSRR. Variability in RSRRs among hospitals of similar type was much larger than aggregate differences between types of hospitals.Overall, larger, urban, academic facilities had modestly higher RSRRs than smaller, suburban, community hospitals, although there was a wide range of performance. The strong regional effect suggests that local practice patterns are an important influence. Disproportionately high readmission rates at for-profit hospitals may highlight the role of financial incentives favoring utilization.

There is an active public debate about whether patients’ socioeconomic status should be included in the readmission measures used to determine penalties in Medicare’s Hospital Readmissions Reduction Program (HRRP). Using the current Centers for Medicare and Medicaid Services methodology, we compared risk-standardized readmission rates for hospitals caring for high and low proportions of patients of low socioeconomic status (as defined by their Medicaid status or neighborhood income). We then calculated risk-standardized readmission rates after additionally adjusting for patients’ socioeconomic status. Our results demonstrate that hospitals caring for large proportions of patients of low socioeconomic status have readmission rates similar to those of other hospitals. Moreover, readmission rates calculated with and without adjustment for patients’ socioeconomic status are highly correlated. Readmission rates of hospitals caring for patients of low socioeconomic status changed by approximately 0.1 percent with adjustment for patients’ socioeconomic status, and only 3–4 percent fewer such hospitals reached the threshold for payment penalty in Medicare’s HRRP. Overall, adjustment for socioeconomic status does not change hospital results in meaningful ways.

Programs from the Centers for Medicare and Medicaid Services simultaneously promote strategies to lower hospital admissions and readmissions. However, there is concern that hospitals in communities that successfully reduce admissions may be penalized, as patients that are ultimately hospitalized may be sicker and at higher risk of readmission. We therefore examined the relationship between changes from 2010 to 2013 in admission rates and thirty-day readmission rates for elderly Medicare beneficiaries. We found that communities with the greatest decline in admission rates also had the greatest decline in thirty-day readmission rates, even though hospitalized patients did grow sicker as admission rates declined. The relationship between changing admission and readmission rates persisted in models that measured observed readmission rates, risk-standardized readmission rates, and the combined rate of readmission and death. Our findings suggest that communities can reduce admission rates and readmission rates in parallel, and that federal policy incentivizing reductions in both outcomes does not create contradictory incentives.

Background Population-based measures of admissions among patients with chronic conditions are important quality indicators of Accountable Care Organizations (ACOs), yet there are challenges in developing measures that enable fair comparisons among providers. Methods On the basis of consensus standards for outcome measure development and with expert and stakeholder input on methods decisions, we developed and tested 2 models of risk-standardized acute admission rates (RSAARs) for patients with diabetes and heart failure using 2010-2012 Medicare claims data. Model performance was assessed with deviance R; score reliability was tested with intraclass correlation coefficient. We estimated RSAARs for 114 Shared Savings Program ACOs in 2012 and we assigned ACOs to 3 performance categories: no different, worse than, and better than the national rate. Results The diabetes and heart failure cohorts included 6.5 and 2.6 million Medicare Fee-For-Service beneficiaries aged 65 years and above, respectively. Risk-adjustment variables were age, comorbidities, and condition-specific severity variables, but not socioeconomic status or other contextual factors. We selected hierarchical negative binomial models with the outcome of acute, unplanned hospital admissions per 100 person-years. For the diabetes and heart failure measures, respectively, the models accounted for 22% and 12% of the deviance in outcomes and score reliability was 0.89 and 0.81. For the diabetes measure, 51 (44.7%) ACOs were no different, 45 (39.5%) were better, and 18 (15.8%) were worse than the national rate. The distribution of performance for the heart failure measure was 61 (53.5%), 37 (32.5%), and 16 (14.0%), respectively. Conclusion Measures of RSAARs for patients with diabetes and heart failure meet criteria for scientific soundness and reveal important variation in quality across ACOs.

A key quality metric for Accountable Care Organizations (ACOs) is the rate of hospitalization among patients with heart failure (HF). Among this patient population, non-HF-related hospitalizations account for a substantial proportion of admissions. Understanding the types of admissions and the distribution of admission types across ACOs of varying performance may provide important insights for lowering admission rates.We examined admission diagnoses among 220 Medicare Shared Savings Program ACOs in 2013. ACOs were stratified into quartiles by their performance on a measure of unplanned risk-standardized acute admission rates (RSAARs) among patients with HF. Using a previously validated algorithm, we categorized admissions by principal discharge diagnosis into: HF, cardiovascular/non-HF, and noncardiovascular. We compared the mean admission rates by admission type as well as the proportion of admission types across RSAAR quartiles (Q1-Q4).Among 220 ACOs caring for 227,356 patients with HF, the median (IQR) RSAARs per 100 person-years ranged from 64.5 (61.7-67.7) in Q1 (best performers) to 94.0 (90.1-99.9) in Q4 (worst performers). The mean admission rates by admission types for ACOs in Q1 compared with Q4 were as follows: HF admissions: 9.8 (2.2) vs 14.6 (2.8) per 100 person years (P.0001); cardiovascular/non-HF admissions: 11.1 (1.6) vs 15.9 (2.6) per 100 person-years (P.0001); and noncardiovascular admissions: 42.7 (5.4) vs 69.6 (11.3) per 100 person-years (P.0001). The proportion of admission due to HF, cardiovascular/non-HF, and noncardiovascular conditions was 15.4%, 17.5%, and 67.1% in Q1 compared with 14.6%, 15.9%, and 69.4% in Q4 (P.007).Although ACOs with the best performance on a measure of all-cause admission rates among people with HF tended to have fewer admissions for HF, cardiovascular/non-HF, and noncardiovascular conditions compared with ACOs with the worst performance (highest admission rates), the largest difference in admission rates were for noncardiovascular admission types. Across all ACOs, two-thirds of admissions of patients with HF were for noncardiovascular causes. These findings suggest that comprehensive approaches are needed to reduce the diverse admission types for which HF patients are at risk.

BACKGROUND: Temporal changes in the readmission rates for patient groups and conditions that were not directly under the purview of Hospital Readmissions Reduction Program (HRRP) can help assess whether efforts to lower readmissions extended beyond targeted patients and conditions. METHODS: Using Nationwide Readmissions Database (2010–2015), we assessed trends in all-cause readmission rates for one of the 3 HRRP conditions (acute myocardial infarction, heart failure, pneumonia) or conditions not targeted by HRRP in 6 age-insurance groups defined by age-groups (≥65 or

Background & Aims Colonoscopy is a common procedure, yet little is known about variations in colonoscopy quality among outpatient facilities. We developed an outcome measure to profile outpatient facilities by estimating risk-standardized rates of unplanned hospital visits within 7 days of colonoscopy. Methods We used a 20% sample of 2010 Medicare outpatient colonoscopy claims (331,880 colonoscopies performed at 8140 facilities) from patients ≥65 years or older, and developed a patient-level logistic regression model to estimate the risk of unplanned hospital visits (ie, emergency department visits, observation stays, and inpatient admissions) within 7 days of colonoscopy. We then used the patient-level risk model variables and hierarchical logistic regression to estimate facility rates of risk-standardized unplanned hospital visits using data from the Healthcare Cost and Utilization Project (325,811 colonoscopies at 992 facilities), from 4 states containing 100% of colonoscopies per facility. Results Outpatient colonoscopies were followed by 5412 unplanned hospital visits within 7 days (16.3/1000 colonoscopies). Hemorrhage, abdominal pain, and perforation were the most common causes of unplanned hospital visits. Fifteen variables were independently associated with unplanned hospital visits (c = 0.67). A history of fluid and electrolyte imbalance (odds ratio [OR] = 1.43; 95% confidence interval [CI]: 1.29−1.58), psychiatric disorders (OR = 1.34; 95% CI: 1.22−1.46), and, in the absence of prior arrhythmia, increasing age past 65 years (aged >85 years vs 65−69 years: OR = 1.87; 95% CI: 1.54−2.28) were most strongly associated. The facility risk-standardized unplanned hospital visits calculated using Healthcare Cost and Utilization Project data showed significant variation (median 12.3/1000; 5th−95th percentile, 10.5−14.6/1000). Median risk-standardized unplanned hospital visits were comparable between ambulatory surgery centers and hospital outpatient departments (each was 10.2/1000), and ranged from 16.1/1000 in the Northeast to 17.2/1000 in the Midwest. Conclusions We calculated a risk-adjusted measure of outpatient colonoscopy quality, which shows important variation in quality among outpatient facilities. This measure can make transparent the extent to which patients require follow-up hospital care, help inform patient choices, and assist in quality-improvement efforts.

BACKGROUND It is desirable not to include planned readmissions in readmission measures because they represent deliberate, scheduled care. OBJECTIVES To develop an algorithm to identify planned readmissions, describe its performance characteristics, and identify improvements. DESIGN Consensus-driven algorithm development and chart review validation study at 7 acute-care hospitals in 2 health systems. PATIENTS For development, all discharges qualifying for the publicly reported hospital-wide readmission measure. For validation, all qualifying same-hospital readmissions that were characterized by the algorithm as planned, and a random sampling of same-hospital readmissions that were characterized as unplanned. MEASUREMENTS We calculated weighted sensitivity and specificity, and positive and negative predictive values of the algorithm (version 2.1), compared to gold standard chart review. RESULTS In consultation with 27 experts, we developed an algorithm that characterizes 7.8% of readmissions as planned. For validation we reviewed 634 readmissions. The weighted sensitivity of the algorithm was 45.1% overall, 50.9% in large teaching centers and 40.2% in smaller community hospitals. The weighted specificity was 95.9%, positive predictive value was 51.6%, and negative predictive value was 94.7%. We identified 4 minor changes to improve algorithm performance. The revised algorithm had a weighted sensitivity 49.8% (57.1% at large hospitals), weighted specificity 96.5%, positive predictive value 58.7%, and negative predictive value 94.5%. Positive predictive value was poor for the 2 most common potentially planned procedures: diagnostic cardiac catheterization (25%) and procedures involving cardiac devices (33%). CONCLUSIONS An administrative claims-based algorithm to identify planned readmissions is feasible and can facilitate public reporting of primarily unplanned readmissions. Journal of Hospital Medicine 2015;10:670–677. © 2015 Society of Hospital Medicine.

Background/objective Patients with multiple chronic conditions (MCCs) are a critical but undefined group for quality measurement. We present a generally applicable systematic approach to defining an MCC cohort of Medicare fee-for-service beneficiaries that we developed for a national quality measure, risk-standardized rates of unplanned admissions for Accountable Care Organizations. Research design To define the MCC cohort we: (1) identified potential chronic conditions; (2) set criteria for cohort conditions based on MCC framework and measure concept; (3) applied the criteria informed by empirical analysis, experts, and the public; (4) described "broader" and "narrower" cohorts; and (5) selected final cohort with stakeholder input. Subjects Subjects were patients with chronic conditions. Participants included 21.8 million Medicare fee-for-service beneficiaries in 2012 aged 65 years and above with ≥1 of 27 Medicare Chronic Condition Warehouse condition(s). Results In total, 10 chronic conditions were identified based on our criteria; 8 of these 10 were associated with notably increased admission risk when co-occurring. A broader cohort (2+ of the 8 conditions) included 4.9 million beneficiaries (23% of total cohort) with an admission rate of 70 per 100 person-years. It captured 53% of total admissions. The narrower cohort (3+ conditions) had 2.2 million beneficiaries (10%) with 100 admissions per 100 person-years and captured 32% of admissions. Most stakeholders viewed the broader cohort as best aligned with the measure concept. Conclusions By systematically narrowing chronic conditions to those most relevant to the outcome and incorporating stakeholder input, we defined an MCC admission measure cohort supported by stakeholders. This approach can be used as a model for other MCC outcome measures.

Public reporting of measures of hospital performance is an important component of quality improvement efforts in many countries. However, it can be challenging to provide an overall characterization of hospital performance because there are many measures of quality. In the United States, the Centers for Medicare and Medicaid Services reports over 100 measures that describe various domains of hospital quality, such as outcomes, the patient experience and whether established processes of care are followed. Although individual quality measures provide important insight, it is challenging to understand hospital performance as characterized by multiple quality measures. Accordingly, we developed a novel approach for characterizing hospital performance that highlights the similarities and differences between hospitals and identifies common patterns of hospital performance. Specifically, we built a semi-supervised machine learning algorithm and applied it to the publicly-available quality measures for 1,614 U.S. hospitals to graphically and quantitatively characterize hospital performance. In the resulting visualization, the varying density of hospitals demonstrates that there are key clusters of hospitals that share specific performance profiles, while there are other performance profiles that are rare. Several popular hospital rating systems aggregate some of the quality measures included in our study to produce a composite score; however, hospitals that were top-ranked by such systems were scattered across our visualization, indicating that these top-ranked hospitals actually excel in many different ways. Our application of a novel graph analytics method to data describing U.S. hospitals revealed nuanced differences in performance that are obscured in existing hospital rating systems.

Objectives To describe the process of developing a new physician payment system based on value and transitioning away from a fee-for-service payment system Study design Descriptive. This paper describes a recent initiative involving redesign of primary care provider payment in the State of Hawaii. While there has been extensive discussion about switching payment from volume to value in recent years, much of this change has happened at the organizational level and this initiative focused on changing the incentives for individual providers. Methods Descriptive paper. In this paper we discuss the approach taken to shift incentives from fee-for-service towards value using behavioral economics as a conceptual framework for program design. We summarize the new payment system, challenges in its design, and our approach to piloting of different behavioral economic strategies to improve performance. Results None. Conclusions This paper will provide useful guidance to health plans or health delivery systems considering shifting primary care payment away from fee-for-service towards value highlighting some of the design challenges and necessary compromises in implementing such a system at scale.

Background: It is unknown if financial pressures to reduce hospital readmission rates following passage of the Affordable Care Act (ACA) have had the unintended effect of increasing mortality rates after hospitalization. We therefore examined correlations between paired changes in hospital 30-day readmission rates and 30-day mortality rates among Medicare fee-for-service beneficiaries hospitalized with heart failure (HF), acute myocardial infarction (AMI), or pneumonia from 2008 to 2014. Methods: We used linear regression to calculate monthly changes in hospitals’ 30-day risk-adjusted readmission rates (RARRs) and 30-day risk-adjusted mortality rates (RAMRs) after discharge for HF, AMI, and pneumonia from 2008 to 2014. Adjustment was made for patient age, sex, comorbidities, hospital length of stay, and season. We then examined the correlation of hospitals’ paired monthly changes in 30-day RARRs and monthly changes in 30-day RAMRs after discharge. Results: From 2008 to 2014, we identified 2,962,554, 1,229,939, and 2,544,530 hospitalizations for HF, AMI, and pneumonia at 5,016, 4,772, and 5,057 hospitals, respectively. Hospital 30-day RARRs declined for all three conditions from 2008 to 2014; the monthly change in RARRs was -0.053 (95% CI -0.055, -0.051) for HF, -0.044 (95% CI -0.047, -0.041) for AMI, and -0.033 (95% CI -0.035, -0.031) for pneumonia. In contrast, the monthly change in hospital 30-day RAMRs after discharge varied by admitting condition and was 0.008 (95% CI 0.007, 0.010) for HF, -0.003 (95% CI -0.006, -0.001) for AMI, and 0.001 (95% CI -0.001, 0.003) for pneumonia. The correlation between monthly changes in hospitals’ 30-day RARRs and 30-day RAMRs after discharge was 0.060 for HF (p Conclusion: Changes in hospital readmission rates for HF, AMI, and pneumonia were poorly correlated with changes in mortality rates after hospitalization between 2008 and 2014. These findings suggest that financial incentives to improve hospitals’ readmission performance have not increased mortality after hospitalization.

Background— Reducing readmissions is a major healthcare reform goal, and reimbursement penalties are imposed for higher-than-expected readmission rates. Most readmission risk models and performance measures are based on administrative rather than clinical data. Methods and Results— We examined rates and predictors of 30-day all-cause readmission following coronary artery bypass grafting surgery by using nationally representative clinical data (2008–2010) from the Society of Thoracic Surgeons National Database linked to Medicare claims records. Among 265 434 eligible Medicare records, 226 960 (86%) were successfully linked to Society of Thoracic Surgeons records; 162 572 (61%) isolated coronary artery bypass grafting admissions constituted the study cohort. Logistic regression was used to identify readmission risk factors; hierarchical regression models were then estimated. Risk-standardized readmission rates ranged from 12.6% to 23.6% (median, 16.8%) among 846 US hospitals with ≥30 eligible cases and ≥90% of eligible Centers for Medicare and Medicaid Services records linked to the Society of Thoracic Surgeons database. Readmission predictors (odds ratios [95% confidence interval]) included dialysis (2.02 [1.87–2.19]), severe chronic lung disease (1.58 [1.49–1.68]), creatinine (2.5 versus 1.0 or lower:1.49 [1.41–1.57]; 2.0 versus 1.0 or lower: 1.37 [1.32–1.43]), insulin-dependent diabetes mellitus (1.45 [1.39–1.51]), obesity in women (body surface area 2.2 versus 1.8: 1.44 [1.35–1.53]), female sex (1.38 [1.33–1.43]), immunosuppression (1.38 [1.28–1.49]), preoperative atrial fibrillation (1.36 [1.30–1.42]), age per 10-year increase (1.36 [1.33–1.39]), recent myocardial infarction (1.24 [1.08–1.42]), and low body surface area in men (1.22 [1.14–1.30]). C-statistic was 0.648. Fifty-two hospitals (6.1%) had readmission rates statistically better or worse than expected. Conclusions— A coronary artery bypass grafting surgery readmission measure suitable for public reporting was developed by using the national Society of Thoracic Surgeons clinical data linked to Medicare readmission claims.

Background: Little is known about the variation in complication rates among U.S. hospitals that perform elective total hip arthroplasty (THA) and total knee arthroplasty (TKA) procedures. The purpose of this study was to use National Quality Forum (NQF)-endorsed hospital-level risk-standardized complication rates to describe variations in, and disparities related to, hospital quality for elective primary THA and TKA procedures performed in U.S. hospitals. Methods: We conducted a cross-sectional analysis of national Medicare Fee-for-Service data. The study cohort included 878,098 Medicare fee-for-service beneficiaries, sixty-five years or older, who underwent elective THA or TKA from 2008 to 2010 at 3479 hospitals. Both medical and surgical complications were included in the composite measure. Hospital-specific complication rates were calculated from Medicare claims with use of hierarchical logistic regression to account for patient clustering and were risk-adjusted for age, sex, and patient comorbidities. We determined whether hospitals with higher proportions of Medicaid patients and black patients had higher risk-standardized complication rates. Results: The crude rate of measured complications was 3.6%. The most common complications were pneumonia (0.86%), pulmonary embolism (0.75%), and periprosthetic joint infection or wound infection (0.67%). The median risk-standardized complication rate was 3.6% (range, 1.8% to 9.0%). Among hospitals with at least twenty-five THA and TKA patients in the study cohort, 103 (3.6%) were better and seventy-five (2.6%) were worse than expected. Hospitals with the highest proportion of Medicaid patients had slightly higher but similar risk-standardized complication rates (median, 3.6%; range, 2.0% to 7.1%) compared with hospitals in the lowest decile (3.4%; 1.7% to 6.2%). Findings were similar for the analysis involving the proportion of black patients. Conclusions: There was more than a fourfold difference in risk-standardized complication rates across U.S. hospitals in which elective THA and TKA are performed. Although hospitals with higher proportions of Medicaid and black patients had rates similar to those of hospitals with lower proportions, there is a continued need to monitor for disparities in outcomes. These findings suggest there are opportunities for quality improvement among hospitals in which elective THA and TKA procedures are performed. Level of Evidence: Therapeutic Level III. See Instruction to Authors for a complete description of levels of evidence.

IMPORTANCE: Hawaii Medical Service Association (HMSA), the Blue Cross Blue Shield of Hawaii, introduced Population-based Payments for Primary Care (3PC), a new capitation-based primary care payment system, in 2016. The effect of this system on quality measures has not been evaluated. OBJECTIVE: To evaluate whether the 3PC system was associated with changes in quality, utilization, or spending in its first year. DESIGN, SETTING, AND PARTICIPANTS: Observational study using HMSA claims and clinical registry data from January 1, 2012, to December 31, 2016, and a propensity-weighted difference-in-differences method to compare 77 225 HMSA members in Hawaii attributed to 107 primary care physicians (PCPs) and 4 physician organizations participating in the first wave of the 3PC and 222 233 members attributed to 312 PCPs and 14 physician organizations that continued in a fee-for-service model in 2016 but had 3PC start dates thereafter. EXPOSURES: Participation in the 3PC system. MAIN OUTCOMES AND MEASURES: The primary outcome was the change in a composite measure score reflecting the probability that a member achieved an eligible measure out of 13 pooled Healthcare Effectiveness Data and Information Set quality measures. Primary care visits and total cost of care were among 15 secondary outcomes. RESULTS: In total, the study included 299 458 HMSA members (mean age, 42.1 years; 51.5% women) and 419 primary care physicians (mean age, 54.9 years; 34.8% women). The risk-standardized composite measure scores for 2012 to 2016 changed from 75.1% to 86.6% (+11.5 percentage points) in the 3PC group and 74.3% to 83.5% (+9.2 percentage points) in the non-3PC group (differential change, 2.3 percentage points [95% CI, 2.1 to 2.6 percentage points]; P

Background— Variation in outcomes after percutaneous coronary interventions (PCI) may reflect differences in quality of care. To date, however, we lack a methodology to monitor and improve national hospital 30-day mortality rates among patients undergoing PCI. Methods and Results— We developed hierarchical logistic regression models to calculate hospital risk-standardized 30-day all-cause PCI mortality rates. Due to differences in risk, patients were divided into 2 cohorts: those with ST-segment elevation myocardial infarction or cardiogenic shock, and those with no ST-segment elevation myocardial infarction and no cardiogenic shock. The models were derived using 2006 data from the CathPCI Registry linked with administrative claims data, and validated using comparable 2005 data. In the derivation cohort of the ST-segment elevation myocardial infarction or shock model (n=15 123), the unadjusted 30-day mortality rate was 9.2%. The final model included 13 variables with the observed mortality rates ranging from 1.4% to 40.3% across deciles of the predicted patient mortality rates. The 25th and 75th percentiles of the risk-standardized mortality rate were 8.5% and 9.7%, with 5th and 95th percentiles of 7.6% and 11.0%. In the derivation cohort of the no ST-segment elevation myocardial infarction and no shock model (n=110 529), the unadjusted 30-day mortality rate was 1.4%. The final model included 16 variables with the observed predicted mortality rates ranging from 0.1% to 7.0% across deciles of the predicted patient mortality rates. The 25th and 75th percentiles of the risk-standardized mortality rate across 612 hospitals were 1.3% and 1.6%, with 5th and 95th percentiles of 1.0% and 2.0%. Conclusions— These National Quality Forum endorsed registry-based models produce estimates of hospital risk-standardized mortality rates for patients undergoing PCI.

Safety-net hospitals, which include urban hospitals serving large numbers of low-income, uninsured, and otherwise vulnerable populations, have historically faced greater financial strains than hospitals that serve more affluent populations. These strains can affect hospitals’ quality of care, perhaps resulting in worse outcomes that are commonly used as indicators of care quality—mortality and readmission rates. We compared risk-standardized rates of both of these clinical outcomes among fee-for-service Medicare beneficiaries admitted for acute myocardial infarction, heart failure, or pneumonia. These beneficiaries were admitted to urban hospitals within Metropolitan Statistical Areas that contained at least one safety-net and at least one non-safety-net hospital. We found that outcomes varied across the urban areas for both safety-net and non-safety-net hospitals for all three conditions. However, mortality and readmission rates were broadly similar, with non-safety-net hospitals outperforming safety-net...

Background— Registry studies have suggested improvements in door-to-balloon times, but a national assessment of the trends in door-to-balloon times is lacking. Moreover, we do not know whether improvements in door-to-balloon times were shared equally among patient and hospital groups. Methods and Results— This analysis includes all patients reported by hospitals to the Centers for Medicare & Medicaid Services for inclusion in the time to percutaneous coronary intervention (acute myocardial infarction-8) inpatient measure from January 1, 2005, through September 30, 2010. For each calendar year, we summarized the characteristics of patients reported for the measure, including the number and percentage in each group, the median time to primary percutaneous coronary intervention, and the percentage with time to primary percutaneous coronary intervention within 75 minutes and within 90 minutes. Door-to-balloon time declined from a median of 96 minutes in the year ending December 31, 2005, to a median of 64 minutes in the 3 quarters ending September 30, 2010. There were corresponding increases in the percentage of patients who had times 75 years of age (median decline, 38 minutes), women (35 minutes), and blacks (42 minutes). Conclusion— National progress has been achieved in the timeliness of treatment of patients with ST-segment–elevation myocardial infarction who undergo primary percutaneous coronary intervention.

Background— National attention has increasingly focused on readmission as a target for quality improvement. We present the development and validation of a model approved by the National Quality Forum and used by the Centers for Medicare & Medicaid Services for hospital-level public reporting of risk-standardized readmission rates for patients discharged from the hospital after an acute myocardial infarction. 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 acute myocardial infarction. The model was derived using Medicare claims data for a 2006 cohort and validated using claims and medical record data. The unadjusted readmission rate was 18.9%. The final model included 31 variables and had discrimination ranging from 8% observed 30-day readmission rate in the lowest predictive decile to 32% in the highest decile and a C statistic of 0.63. The 25th and 75th percentiles of the risk-standardized readmission rates across 3890 hospitals were 18.6% and 19.1%, with fifth and 95th percentiles of 18.0% and 19.9%, respectively. The odds of all-cause readmission for a hospital 1 SD above average were 1.35 times that of a hospital 1 SD below average. Hospital-level adjusted readmission rates developed using the claims model were similar to rates produced for the same cohort using a medical record model (correlation, 0.98; median difference, 0.02 percentage points). Conclusions— This claims-based model of hospital risk-standardized readmission rates for patients with acute myocardial infarction produces estimates that are excellent surrogates for those produced from a medical record model.

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.

BACKGROUND The association between hospital volume and the death rate for patients who are hospitalized for acute myocardial infarction, heart failure, or pneumonia remains unclear. It is also not known whether a volume threshold for such an association exists. METHODS We conducted cross-sectional analyses of data from Medicare administrative claims for all fee-for-service beneficiaries who were hospitalized between 2004 and 2006 in acute care hospitals in the United States for acute myocardial infarction, heart failure, or pneumonia. Using hierarchical logistic-regression models for each condition, we estimated the change in the odds of death within 30 days associated with an increase of 100 patients in the annual hospital volume. Analyses were adjusted for patients' risk factors and hospital characteristics. Bootstrapping procedures were used to estimate 95% confidence intervals to identify the condition-specific volume thresholds above which an increased volume was not associated with reduced mortality. RESULTS There were 734,972 hospitalizations for acute myocardial infarction in 4128 hospitals, 1,324,287 for heart failure in 4679 hospitals, and 1,418,252 for pneumonia in 4673 hospitals. An increased hospital volume was associated with reduced 30-day mortality for all conditions (P

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 14 813 patients in 50 ranked hospitals and 409 806 patients in 4761 nonranked hospitals. The readmission analysis included 16 641 patients in 50 ranked hospitals and 458 473 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 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.

Background— In 2009, the Centers for Medicare & Medicaid Services is publicly reporting hospital-level risk-standardized 30-day mortality and readmission rates after acute myocardial infarction (AMI) and heart failure (HF). We provide patterns of hospital performance, based on these measures. Methods and Results— We calculated the 30-day mortality and readmission rates for all Medicare fee-for-service beneficiaries ages 65 years or older with a primary diagnosis of AMI or HF, discharged between July 2005 and June 2008. We compared weighted risk-standardized mortality and readmission rates across Hospital Referral Regions and hospital structural characteristics. The median 30-day mortality rate was 16.6% for AMI (range, 10.9% to 24.9%; 25th to 75th percentile, 15.8% to 17.4%; 10th to 90th percentile, 14.7% to 18.4%) and 11.1% for HF (range, 6.6% to 19.8%; 25th to 75th percentile, 10.3% to 12.0%; 10th to 90th percentile, 9.4% to 13.1%). The median 30-day readmission rate was 19.9% for AMI (range, 15.3% to 29.4%; 25th to 75th percentile, 19.5% to 20.4%; 10th to 90th percentile, 18.8% to 21.1%) and 24.4% for HF (range, 15.9% to 34.4%; 25th to 75th percentile, 23.4% to 25.6%; 10th to 90th percentile, 22.3% to 27.0%). We observed geographic differences in performance across the country. Although there were some differences in average performance by hospital characteristics, there were high and low hospital performers among all types of hospitals. Conclusions— In a recent 3-year period, 30-day risk-standardized mortality rates for AMI and HF varied among hospitals and across the country. The readmission rates were particularly high.

Objective To examine the association of hospital volume (a marker of quality of care) with hospital readmission rates. Design Retrospective cross-sectional study. Setting 4651US acute care hospitals. Study data 6 916 644 adult discharges, excluding patients receiving psychiatric or medical cancer treatment. Main outcome measures We used Medicare fee-for-service data from 1 July 2011 to 30 June 2012 to calculate observed-to-expected, unplanned, 30 day, standardized readmission rates for hospitals and for specialty cohorts medicine, surgery/gynecology, cardiorespiratory, cardiovascular, and neurology. We assessed the association of hospital volume by quintiles with 30 day, standardized readmission rates, with and without adjustment for hospital characteristics (safety net status, teaching status, geographic region, urban/rural status, nurse to bed ratio, ownership, and cardiac procedure capability. We also examined associations with the composite outcome of 30 day, standardized readmission or mortality rates. Results Mean 30 day, standardized readmission rate among the fifth of hospitals with the lowest volume was 14.7 (standard deviation 5.3) compared with 15.9 (1.7) among the fifth of hospitals with the highest volume (P

Importance The Affordable Care Act has led to US national reductions in hospital 30-day readmission rates for heart failure (HF), acute myocardial infarction (AMI), and pneumonia. Whether readmission reductions have had the unintended consequence of increasing mortality after hospitalization is unknown. Objective To examine the correlation of paired trends in hospital 30-day readmission rates and hospital 30-day mortality rates after discharge. Design, Setting, and Participants Retrospective study of Medicare fee-for-service beneficiaries aged 65 years or older hospitalized with HF, AMI, or pneumonia from January 1, 2008, through December 31, 2014. Exposure Thirty-day risk-adjusted readmission rate (RARR). Main Outcomes and Measures Thirty-day RARRs and 30-day risk-adjusted mortality rates (RAMRs) after discharge were calculated for each condition in each month at each hospital in 2008 through 2014. Monthly trends in each hospital’s 30-day RARRs and 30-day RAMRs after discharge were examined for each condition. The weighted Pearson correlation coefficient was calculated for hospitals’ paired monthly trends in 30-day RARRs and 30-day RAMRs after discharge for each condition. Results In 2008 through 2014, 2 962 554 hospitalizations for HF, 1 229 939 for AMI, and 2 544 530 for pneumonia were identified at 5016, 4772, and 5057 hospitals, respectively. In January 2008, mean hospital 30-day RARRs and 30-day RAMRs after discharge were 24.6% and 8.4% for HF, 19.3% and 7.6% for AMI, and 18.3% and 8.5% for pneumonia. Hospital 30-day RARRs declined in the aggregate across hospitals from 2008 through 2014; monthly changes in RARRs were −0.053% (95% CI, −0.055% to −0.051%) for HF, −0.044% (95% CI, −0.047% to −0.041%) for AMI, and −0.033% (95% CI, −0.035% to −0.031%) for pneumonia. In contrast, monthly aggregate changes across hospitals in hospital 30-day RAMRs after discharge varied by condition: HF, 0.008% (95% CI, 0.007% to 0.010%); AMI, −0.003% (95% CI, −0.005% to −0.001%); and pneumonia, 0.001% (95% CI, −0.001% to 0.003%). However, correlation coefficients in hospitals’ paired monthly changes in 30-day RARRs and 30-day RAMRs after discharge were weakly positive: HF, 0.066 (95% CI, 0.036 to 0.096); AMI, 0.067 (95% CI, 0.027 to 0.106); and pneumonia, 0.108 (95% CI, 0.079 to 0.137). Findings were similar in secondary analyses, including with alternate definitions of hospital mortality. Conclusions and Relevance Among Medicare fee-for-service beneficiaries hospitalized for heart failure, acute myocardial infarction, or pneumonia, reductions in hospital 30-day readmission rates were weakly but significantly correlated with reductions in hospital 30-day mortality rates after discharge. These findings do not support increasing postdischarge mortality related to reducing hospital readmissions.

Background: Admissions among ambulatory patients with heart failure (HF) is a key quality metric for Accountable Care Organizations (ACOs). Yet HF patients are at risk for many acute illnesses, HF and non-HF related, and it is unknown whether specific types of admissions explain lower or higher ACO

In recent years, the Centers for Medicare & Medicaid Services (CMS) has expanded its array of hospital quality measures in cardiovascular disease. In doing so, CMS has shifted its focus from process measures to outcome measures, increasingly using the measures in pay-for-reporting and performance programs.1 The emerging measures and their association with policy initiatives have changed over time and may be difficult to track for the casual observer, even as they increasingly influence payments to individuals and institutions. This Perspective provides a primer on the CMS hospital cardiovascular outcome measures included in the Hospital Inpatient Quality Reporting Program (IQR), Hospital Readmissions Reduction Program (HRRP); and Hospital Value-Based Purchasing (HVBP) Program. We begin with a brief background on past and current CMS quality measurement efforts and then provide an overview of measure specifications for the current cardiovascular outcome measures used in these programs. We then summarize how the measures are currently used in the IQR, HRRP, and HVBP to assess hospital performance. CMS has a history of quality measurement dating back to its implementation of the Medicare Utilization and Quality Control Peer Review Program in the early 1980s. Initially, the Peer Review Program conducted implicit reviews of select cases, as suggested to them via beneficiaries, providers, or sampling in order to determine if appropriate care was provided. Over time, CMS reorganized the Peer Review Program into Quality Improvement Organizations, which, in addition to their quality improvement objectives, were charged with systematically gathering and reviewing data for the purpose of quality measurement.2,3 Concerns were raised surrounding the voluntary nature of participation in Quality Improvement Organization measurement activities and the potential for bias toward high-performing hospitals, which would be more likely to participate.4 Partially in response to these concerns, CMS shifted its quality measurement activities away from individual …

Background: After hospitalization for heart failure (HF) and acute myocardial infarction (AMI), patients experience increased risk of death and hospital readmission. Defining the trajectory and timing of this period of risk may help guide interventions to improve post-discharge outcomes. Methods: We used 2008-10 Medicare data to identify patients ≥65 years discharged alive after HF or AMI hospitalization. Using hazard rates, we characterized the risks of death and first readmission on each day after discharge to describe (1) the maximum daily risks of death and readmission after discharge; (2) risks of death and readmission 1 year after discharge; (3) the time in days after discharge for the risks of death and readmission to reach their maximum daily rates and 50% of their maximum daily rates to characterize the rapidity of decline in risk. We created separate survival models for death and first readmission. Data were censored after 1 year follow up. The readmission model also censored for death prior to readmission. Results: Of 878,963 HF hospitalizations, 367,542 (41.8%) died and 618,283 (70.3%) were readmitted in 1 year. Of 350,509 AMI hospitalizations, 90,623 (25.9%) died and 177,031(50.5%) were readmitted in 1 year. The Figure shows hazard rates by time after discharge. For HF, daily risk of death was 0.0056 maximally and 0.0011 at 1 year (19% of maximum). Daily risk of readmission was 0.013 maximally and 0.002 at 1 year (16% of maximum). Daily risk of death was highest 1 day after discharge and 50% less 11 days after discharge. Daily risk of readmission was highest 4 days after discharge and 50% less 49 days after discharge. For AMI, daily risk of death was 0.010 maximally and 0.0004 at 1 year (4% of maximum). Daily risk of readmission was 0.015 maximally and 0.0011 at 1 year (7% of maximum). Daily risk of death was highest 1 day after discharge and 50% less 6 days after discharge. Daily risk of readmission was highest 2 days after discharge and 50% less 13 days after discharge. Conclusions: After hospitalization for HF and AMI, risk of death is highest on day 1 after discharge and then declines rapidly. In contrast, risk of readmission peaks later and declines more slowly. This extended period of risk for readmission may justify continued vigilance beyond the 30-day period used by Medicare to evaluate hospital readmission performance.

BACKGROUND The Centers for Medicare & Medicaid Services publicly reports risk-standardized mortality rates (RSMRs) within 30-days of admission and, in 2013, risk-standardized unplanned readmission rates (RSRRs) within 30-days of discharge for patients hospitalized with acute myocardial infarction (AMI), heart failure (HF), and pneumonia. Current publicly reported data do not focus on variation in national results or annual changes. OBJECTIVE Describe U.S. hospital performance on AMI, HF, and pneumonia mortality and updated readmission measures to provide perspective on national performance variation. DESIGN To identify recent changes and variation in national hospital-level mortality and readmission for AMI, HF, and pneumonia, we performed cross-sectional panel analyses of national hospital performance on publicly reported measures. PARTICIPANTS Fee-for-service Medicare and Veterans Health Administration beneficiaries, 65 years or older, hospitalized with principal discharge diagnoses of AMI, HF, or pneumonia between July 2009 and June 2012. RSMRs/RSRRs were calculated using hierarchical logistic models risk-adjusted for age, sex, comorbidities, and patients’ clustering among hospitals. Results Median (range) RSMRs for AMI, HF, and pneumonia were 15.1% (9.4–21.0%), 11.3% (6.4–17.9%), and 11.4% (6.5–24.5%), respectively. Median (range) RSRRs for AMI, HF, and pneumonia were 18.2% (14.4–24.3%), 22.9% (17.1–30.7%), and 17.5% (13.6–24.0%), respectively. Median RSMRs declined for AMI (15.5% in 2009–2010, 15.4% in 2010–2011, 14.7% in 2011–2012) and remained similar for HF (11.5% in 2009–2010, 11.9% in 2010–2011, 11.7% in 2011–2012) and pneumonia (11.8% in 2009–2010, 11.9% in 2010–2011, 11.6% in 2011–2012). Median hospital-level RSRRs declined: AMI (18.5% in 2009–2010, 18.5% in 2010–2011, 17.7% in 2011–2012), HF (23.3% in 2009–2010, 23.1% in 2010–2011, 22.5% in 2011–2012), and pneumonia (17.7% in 2009–2010, 17.6% in 2010–2011, 17.3% in 2011–2012). Conclusions We report the first national unplanned readmission results demonstrating declining rates for all three conditions between 2009–2012. Simultaneously, AMI mortality continued to decline, pneumonia mortality was stable, and HF mortality experienced a small increase. Electronic supplementary material The online version of this article (doi:10.1007/s11606-014-2862-5) contains supplementary material, which is available to authorized users.

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% (r 2 = 0.029), with the correlation most prominent for hospitals with RSMR 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.

Importance To better guide strategies intended to reduce high rates of 30-day readmission after hospitalization for heart failure (HF), acute myocardial infarction (MI), or pneumonia, further information is needed about readmission diagnoses, readmission timing, and the relationship of both to patient age, sex, and race. Objective To examine readmission diagnoses and timing among Medicare beneficiaries readmitted within 30 days after hospitalization for HF, acute MI, or pneumonia. Design, Setting, and Patients We analyzed 2007-2009 Medicare fee-for-service claims data to identify patterns of 30-day readmission by patient demographic characteristics and time after hospitalization for HF, acute MI, or pneumonia. Readmission diagnoses were categorized using an aggregated version of the Centers for Medicare & Medicaid Services' Condition Categories. Readmission timing was determined by day after discharge. Main Outcome Measures We examined the percentage of 30-day readmissions occurring on each day (0-30) after discharge; the most common readmission diagnoses occurring during cumulative periods (days 0-3, 0-7, 0-15, and 0-30) and consecutive periods (days 0-3, 4-7, 8-15, and 16-30) after hospitalization; median time to readmission for common readmission diagnoses; and the relationship between patient demographic characteristics and readmission diagnoses and timing. Results From 2007 through 2009, we identified 329 308 30-day readmissions after 1 330 157 HF hospitalizations (24.8% readmitted), 108 992 30-day readmissions after 548 834 acute MI hospitalizations (19.9% readmitted), and 214 239 30-day readmissions after 1 168 624 pneumonia hospitalizations (18.3% readmitted). The proportion of patients readmitted for the same condition was 35.2% after the index HF hospitalization, 10.0% after the index acute MI hospitalization, and 22.4% after the index pneumonia hospitalization. Of all readmissions within 30 days of hospitalization, the majority occurred within 15 days of hospitalization: 61.0%, HF cohort; 67.6%, acute MI cohort; and 62.6%, pneumonia cohort. The diverse spectrum of readmission diagnoses was largely similar in both cumulative and consecutive periods after discharge. Median time to 30-day readmission was 12 days for patients initially hospitalized for HF, 10 days for patients initially hospitalized for acute MI, and 12 days for patients initially hospitalized for pneumonia and was comparable across common readmission diagnoses. Neither readmission diagnoses nor timing substantively varied by age, sex, or race. Conclusion and Relevance Among Medicare fee-for-service beneficiaries hospitalized for HF, acute MI, or pneumonia, 30-day readmissions were frequent throughout the month after hospitalization and resulted from a similar spectrum of readmission diagnoses regardless of age, sex, race, or time after discharge.

BACKGROUND Risk-standardized measures of hospital outcomes reported by the Centers for Medicare and Medicaid Services include Medicare fee-for-service (FFS) patients and exclude Medicare Advantage (MA) patients due to data availability. MA penetration varies greatly nationwide and seems to be associated with increased FFS population risk. Whether variation in MA penetration affects the performance on the Centers for Medicare and Medicaid Service measures is unknown. OBJECTIVE To determine whether the MA penetration rate is associated with outcomes measures based on FFS patients. RESEARCH DESIGN In this retrospective study, 2008 MA penetration was estimated at the Hospital Referral Region (HRR) level. Risk-standardized mortality rates and risk-standardized readmission rates for heart failure, acute myocardial infarction, and pneumonia from 2006 to 2008 were estimated among HRRs, along with several markers of FFS population risk. Weighted linear regression was used to test the association between each of these variables and MA penetration among HRRs. RESULTS Among 304 HRRs, MA penetration varied greatly (median, 17.0%; range, 2.1%-56.6%). Although MA penetration was significantly (P

Hospital-level, 30-day risk-standardized mortality and readmission rates are publicly reported for Medicare patients admitted with acute myocardial infarction (AMI), heart failure (HF), and pneumonia, but the correlations among mortality rates and among readmission rates within US hospitals for these conditions are unknown. Correlation among measures within the same hospital would suggest that there are common hospital-wide quality factors.We designed a cross-sectional study of US hospital 30-day risk-standardized mortality and readmission rates for Medicare fee-for-service beneficiaries from July 2007 to June 2009. We assessed the correlation between pairs of risk-standardized mortality rates and pairs of risk-standardized readmission rates for AMI, HF, and pneumonia.The mortality cohort included 4559 hospitals, and the readmission cohort included 4468 hospitals. Every mortality measure was significantly correlated with every other mortality measure (range of correlation coefficients, 0.27-0.41, P0.0001 for all correlations). Every readmission measure was significantly correlated with every other readmission measure (range of correlation coefficients, 0.32-0.47, P0.0001 for all correlations). For each condition pair and outcome, one-third or more of hospitals were in the same quartile of performance. Correlations were highest within large, nonprofit, urban, and/or Council of Teaching Hospitals members. For any given condition pair, the correlation between readmission rates was significantly higher than the correlation between mortality rates (P0.01 for all pairs).Risk-standardized readmission rates are moderately correlated with each other within hospitals, as are risk-standardized mortality rates. This suggests that there may be common hospital-wide factors affecting hospital outcomes.

In the fall of 2013, our 86-year-old dad, Robert Drye, a heart failure patient, mentioned to us that his cardiologist was going to repair his cardiac device. Dad has an implanted dual-chamber pacemaker and defibrillator (pacemaker/implantable cardioverter-defibrillator [ICD]) that will pace his heartbeat if it beats too slowly and provide a shock if he develops a life-threatening irregular rhythm. His cardiologist could not get a recording off the device, presumably because of a failure of the wire that connects it to the heart’s rhythm system. He referred Dad to his electrophysiologist, his cardiologist specializing in heart rhythm problems. The specialist informed Dad that the failing lead might cause the pacemaker/ICD to fire inappropriately and proposed performing a procedure to disable the lead and place a working one. He told Dad he would have to stay in the hospital overnight. He did not mention any risks of the procedure other than infection. We were ambivalent about the ICD procedure. In fact, it is fair to say we were biased against it; one of us helped develop a national ICD complications quality measure and the other is a palliative care doctor. We were both well aware of the risks but not sure of the benefit. We decided we should share our reservations with Dad and make sure that having the device repaired was the right decision for him. Our dad was living at home but seemed to be increasingly frail. Seven years before, Dad had a cardiac arrest. A sudden block in the blood flow to his heart damaged the heart muscle and caused a usually fatal heart arrhythmia called …

Background Delays in treatment time are commonplace for patients with ST-segment elevation acute myocardial infarction who must be transferred to another hospital for percutaneous coronary intervention. Experts have recommended that door-in to door-out (DIDO) time (ie, time from arrival at the first hospital to transfer from that hospital to the percutaneous coronary intervention hospital) should not exceed 30 minutes. We sought to describe national performance in DIDO time using a new measure developed by the Centers for Medicare & Medicaid Services. Methods We report national median DIDO time and examine associations with patient characteristics (age, sex, race, contraindication to fibrinolytic therapy, and arrival time) and hospital characteristics (number of beds, geographic region, location [rural or urban], and number of cases reported) using a mixed effects multivariable model. Results Among 13 776 included patients from 1034 hospitals, only 1343 (9.7%) had a DIDO time within 30 minutes, and DIDO exceeded 90 minutes for 4267 patients (31.0%). Mean estimated times (95% CI) to transfer based on multivariable analysis were 8.9 (5.6-12.2) minutes longer for women, 9.1 (2.7-16.0) minutes longer for African Americans, 6.9 (1.6-11.9) minutes longer for patients with contraindication to fibrinolytic therapy, shorter for all age categories (except >75 years) relative to the category of 18 to 35 years, 15.3 (7.3-23.5) minutes longer for rural hospitals, and 14.4 (6.6-21.3) minutes longer for hospitals with 9 or fewer transfers vs 15 or more in 2009 (all P Conclusion Among patients presenting to emergency departments and requiring transfer to another facility for percutaneous coronary intervention, the DIDO time rarely met the recommended 30 minutes.

Background Substantial hospital-level variation in the risk of readmission after hospitalization for heart failure (HF) or acute myocardial infarction (AMI) has been reported. Prior studies have documented considerable state-level variation in rates of discharge to skilled nursing facilities (SNFs), but evaluation of hospital-level variation in SNF rates and its relationship to hospital-level readmission rates is limited. Methods Hospital-level 30-day all-cause risk-standardized readmission rates (RSRRs) were calculated using claims data for fee-for-service Medicare patients hospitalized with a principal diagnosis of HF or AMI from 2006-2008. Medicare claims were used to calculate rates of discharge to SNF following HF-specific or AMI-specific admissions in hospitals with ≥25 HF or AMI patients, respectively. Weighted regression was used to quantify the relationship between RSRRs and SNF rates for each condition. Results Mean RSRR following HF admission among 4101 hospitals was 24.7%, and mean RSRR after AMI admission among 2453 hospitals was 19.9%. Hospital-level SNF rates ranged from 0% to 83.8% for HF and from 0% to 77.8% for AMI. No significant relationship between RSRR after HF and SNF rate was found in adjusted regression models ( P =.15). RSRR after AMI increased by 0.03 percentage point for each 1 absolute percentage point increase in SNF rate in adjusted regression models ( P =.001). Overall, HF and AMI SNF rates explained Conclusion SNF rates after HF or AMI hospitalization vary considerably across hospitals, but explain little of the variation in 30-day all-cause readmission rates for these conditions.

Background— Patient outcomes provide a critical perspective on quality of care. The Centers for Medicare and Medicaid Services (CMS) is publicly reporting hospital 30-day risk-standardized mortality rates (RSMRs) and risk-standardized readmission rates (RSRRs) for patients hospitalized with acute myocardial infarction (AMI) and heart failure (HF). We provide a national perspective on hospital performance for the 2010 release of these measures. Methods and Results— The hospital RSMRs and RSRRs are calculated from Medicare claims data for fee-for-service Medicare beneficiaries, 65 years or older, hospitalized with AMI or HF between July 1, 2006, and June 30, 2009. The rates are calculated using hierarchical logistic modeling to account for patient clustering, and are risk-adjusted for age, sex, and patient comorbidities. The median RSMR for AMI was 16.0% and for HF was 10.8%. Both measures had a wide range of hospital performance with an absolute 5.2% difference between hospitals in the 5th versus 95th percentile for AMI and 5.0% for HF. The median RSRR for AMI was 19.9% and for HF was 24.5% (3.9% range for 5th to 95th percentile for AMI, 6.7% for HF). Distinct regional patterns were evident for both measures and both conditions. Conclusions— High RSRRs persist for AMI and HF and clinically meaningful variation exists for RSMRs and RSRRs for both conditions. Our results suggest continued opportunities for improvement in patient outcomes for HF and AMI.

BACKGROUND: Pneumonia is a leading cause of hospitalization and death in the elderly, and remains the subject of both local and national quality improvement efforts. OBJECTIVE: To describe patterns of hospital and regional performance in the outcomes of elderly patients with pneumonia. DESIGN: Cross-sectional study using hospital and outpatient Medicare claims between 2006 and 2009. SETTING: A total of 4,813 nonfederal acute care hospitals in the United States and its organized territories. PATIENTS: Hospitalized fee-for-service Medicare beneficiaries age 65 years and older who received a principal diagnosis of pneumonia. INTERVENTION: None. MEASUREMENTS: Hospital and regional level risk-standardized 30-day mortality and readmission rates. RESULTS: Of the 1,118,583 patients included in the mortality analysis 129,444 (11.6%) died within 30 days of hospital admission. The median (Q1, Q3) hospital 30-day risk-standardized mortality rate for patients with pneumonia was 11.1% (10.0%, 12.3%), and despite controlling for differences in case mix, ranged from 6.7% to 20.9%. Among the 1,161,817 patients included in the readmission analysis 212,638 (18.3%) were readmitted within 30 days of hospital discharge. The median (Q1, Q3) 30-day risk-standardized readmission rate was 18.2% (17.2%, 19.2%) and ranged from 13.6% to 26.7%. Risk-standardized mortality rates varied across hospital referral regions from a high of 14.9% to a low of 8.7%. Risk-standardized readmission rates varied across hospital referral regions from a high of 22.2% to a low of 15%. CONCLUSIONS: Risk-standardized 30-day mortality and, to a lesser extent, readmission rates for patients with pneumonia vary substantially across hospitals and regions and may present opportunities for quality improvement, especially at low performing institutions and areas. Journal of Hospital Medicine 2010. © 2010 Society of Hospital Medicine.

BACKGROUND: Readmission following hospital discharge has become an important target of quality improvement. OBJECTIVE: To describe the development, validation, and results of a risk-standardized measure of hospital readmission rates among elderly patients with pneumonia employed in federal quality measurement and efficiency initiatives. DESIGN: A retrospective cohort study using hospital and outpatient Medicare claims from 2005 and 2006. SETTING: A total of 4675 hospitals in the United States. PATIENTS: Medicare beneficiaries aged >65 years with a principal discharge diagnosis of pneumonia. INTERVENTION: None. MEASUREMENTS: Hospital-specific, risk-standardized 30-day readmission rates calculated as the ratio of predicted-to-expected readmissions, multiplied by the national unadjusted rate. Comparison of the areas under the receiver operating curve (ROC) and measurement of correlation coefficient in development and validation samples. RESULTS: The development sample consisted of 226,545 hospitalizations at 4675 hospitals, with an overall unadjusted 30-day readmission rate of 17.4%. The median risk-standardized hospital readmission rate was 17.3%, and the odds of readmission for a hospital one standard deviation above average was 1.4 times that of a hospital one standard deviation below average. Performance of the medical record and administrative models was similar (areas under the ROC curve 0.59 and 0.63, respectively) and the correlation coefficient of estimated state-specific standardized readmission rates from the administrative and medical record models was 0.96. CONCLUSIONS: Rehospitalization within 30 days of treatment for pneumonia is common, and rates vary across hospitals. A risk-standardized measure of hospital readmission rates derived from administrative claims has similar performance characteristics to one based on medical record review. Journal of Hospital Medicine 2010. © 2010 Society of Hospital Medicine

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.To describe the temporal changes in length of stay, discharge disposition, and short-term outcomes among older patients hospitalized for HF.An observational study of 6,955,461 Medicare fee-for-service hospitalizations for HF between 1993 and 2006, with a 30-day follow-up.Length of hospital stay, in-patient and 30-day mortality, and 30-day readmission rates.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).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.