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A study was conducted to evaluated the association between length of stay and short-term outcomes among Medicare older patients hospitalized for heart failure. Results indicated that with a reduced length of hospital stay 30-day mortality decreased; however, posthospital readmission and mortality risk increased.

Cross-sectional analyses of data from Medicare administrative claims for all fee-for-service beneficiaries hospitalized between 2004 and 2006 in acute care hospitals in US was conducted to determine the association between hospital volume threshold and the death rate for patients hospitalized for acute myocardial infarction, heart failure, or pneumonia. Admission to higher-volume hospitals was found to be associated with a reduction in mortality for acute myocardial infarction, heart failure, and pneumonia, but volume threshold above which an increased condition-specific hospital was not significantly associated with reduced mortality.

The study attempts to evaluate the hospital 30-day risk-standardized mortality rates (RSMRs) for those patients that have been discharged with acute myocardial infarction (AMI). The results indicate that there was a significant decrease in hospital mortality rate for patients with AMI.

Background: 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.Methods: 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).Results: 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).Conclusions: 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 riskstandardized 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 followup hospital care, help inform patient choices, and assist in quality-improvement efforts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The article discusses a research study on a hierarchical logistic regression model developed to measure hospital risk-standardized 30-day all-cause remediation rates for patients hospitalized with acute myocardial infarction. The researchers derived the model using Medicare claims data for a 2006 cohort and validated using claims and medical record data. It describes the development of a hierarchical logistic regression model by the researchers.

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. [ABSTRACT FROM AUTHOR]

The article discusses a study on the use of hierarchical regression models in assessing the rates of risk-standardized mortality and readmission of patients with heart failure in hospitals identified in the "America's Best Hospitals" ranking by the periodical "U.S. News & World Report" and in those non-ranked facilities. The study noted there was a reduced mean rate of in-hospital mortality in ranked hospitals, compared to non-ranked ones. It is said that ranked hospitals have not shown enough evidence of superiority for readmission.

The article presents the study which examined the readmission performance and patterns of readmission of high performing hospitals with low 30 day readmission rates and lower performing hospitals with higher rates of readmission. The study was conducted to Medicare beneficiaries aged 65 and older in the U.S. The results showed that hospitals could best lessen readmissions with strategies that reduce readmission risk worldwide.

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 clusterrandomized 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 feefor- service to population-based payment. Over 74,000 patients and eightyeight 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]