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Background: The environment in which a patient lives influences their health outcomes. However, the degree to which community factors are associated with readmissions is uncertain. Objective: To estimate the influence of community factors on the Centers for Medicare & Medicaid Services risk-standardized hospital-wide readmission measure (HWR)–a quality performance measure in the U.S. Research design: We assessed 71 community variables in 6 domains related to health outcomes: clinical care; health behaviors; social and economic factors; the physical environment; demographics; and social capital. Subjects: Medicare fee-for-service patients eligible for the HWR measure between July 2014-June 2015 (n = 6,790,723). Patients were linked to community variables using their 5-digit zip code of residence. Methods: We used a random forest algorithm to rank variables for their importance in predicting HWR scores. Variables were entered into 6 domain-specific multivariable regression models in order of decreasing importance. Variables with P-values <0.10 were retained for a final model, after eliminating any that were collinear. Results: Among 71 community variables, 19 were retained in the 6 domain models and in the final model. Domains which explained the most to least variance in HWR were: physical environment (R2 = 15%); clinical care (R2 = 12%); demographics (R2 = 11%); social and economic environment (R2 = 7%); health behaviors (R2 = 9%); and social capital (R2 = 8%). In the final model, the 19 variables explained more than a quarter of the variance in readmission rates (R2 = 27%). Conclusions: Readmissions for a wide range of clinical conditions are influenced by factors relating to the communities in which patients reside. These findings can be used to target efforts to keep patients out of the hospital. [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]

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 hospitals on average by less than one percentage point across most conditions. For heart failure mortality, there was no difference between safety-net and non-safety-net hospitals. These findings suggest that safety-net hospitals are performing better than many would have expected. [ABSTRACT FROM AUTHOR]

The article presents a qualitative study on the effect of socioeconomic status (SES) in making decision related to health care. The study surveyed 18 physicians from different practices. Result suggests that SES greatly influences the health management and in accordance to this, physicians often modified their plan of care to however maximize patient outcomes.

Abstract: Objective. : Examine the association between poverty and preventive care use among older working adults. Method. : Cross-sectional analysis of the pooled 1996, 1998 and 2000 waves of the Health and Retirement Study, a nationally representative sample of older community-dwelling adults, studying self-reported use of cervical, breast, and prostate cancer screening, as well as serum cholesterol screening and influenza vaccination. Adults with incomes within 200% of the federal poverty level were defined as poor. Results. : Among 10,088 older working adults, overall preventive care use ranged from 38% (influenza vaccination) to 76% (breast cancer screening). In unadjusted analyses, the working poor were significantly less likely to receive preventive care. After adjustment for insurance coverage, education, and other socio-demographic characteristics, the working poor remained significantly less likely to receive breast cancer (RR 0.92, 95% CI, 0.86–0.96), prostate cancer (RR 0.89, 95% CI, 0.81–0.97), and cholesterol screening (RR 0.91, 95% CI, 0.86–0.96) than the working non-poor, but were not significantly less likely to receive cervical cancer screening (RR 0.96, 95% CI, 0.90–1.01) or influenza vaccination (RR 0.92, 95% CI, 0.84–1.01). Conclusion. : The older working poor are at modestly increased risk for not receiving preventive care. [Copyright &y& Elsevier]

The author discusses the relation between patient's socioeconomic status (SES) and hospital performance. It states that the Hospital Readmissions Reduction Program aimed to reduce patients readmission within 30 days of discharge which was very high for diseases such as acute myocardial infarction, pneumonia and heart failure. Topics discussed include lack of SES concern by the hospital, influence of social risk factors on readmission risk and shortage of resources in health care institutions.

A letter to the editor is presented in response to the article "Accounting For Patients' Socioeconomic Status Does Not Change Hospital Readmission Rates," by Susannah Bernheim in the August 2016 issue.

Key Points: Question: Do hospitals achieve equitable readmission rates (ie, fewer readmissions with narrow gaps in readmission rates between populations)? What characterizes hospitals with equitable readmissions? Findings: Of eligible hospitals, 17% had equitable readmissions by insurance, and 30% had equitable readmissions by race. Hospitals with and without equitable readmissions were characteristically different. Achieving equitable readmissions did not consistently correlate with quality, cost, or value. Many hospitals were not eligible for a disparities assessment due to insufficient numbers of dual-eligible and Black patients. Meaning: A minority of hospitals achieve equitable readmissions. Equity-focused outcome measures assess new dimensions of hospital performance distinct from traditional accountability measures. Importance: Equity is an essential domain of health care quality. The Centers for Medicare & Medicaid Services (CMS) developed 2 Disparity Methods that together assess equity in clinical outcomes. Objectives: To define a measure of equitable readmissions; identify hospitals with equitable readmissions by insurance (dual eligible vs non–dual eligible) or patient race (Black vs White); and compare hospitals with and without equitable readmissions by hospital characteristics and performance on accountability measures (quality, cost, and value). Design, Setting, and Participants: Cross-sectional study of US hospitals eligible for the CMS Hospital-Wide Readmission measure using Medicare data from July 2018 through June 2019. Main Outcomes and Measures: We created a definition of equitable readmissions using CMS Disparity Methods, which evaluate hospitals on 2 methods: outcomes for populations at risk for disparities (across-hospital method); and disparities in care within hospitals' patient populations (within-a-single-hospital method). Exposures: Hospital patient demographics; hospital characteristics; and 3 measures of hospital performance—quality, cost, and value (quality relative to cost). Results: Of 4638 hospitals, 74% served a sufficient number of dual-eligible patients, and 42% served a sufficient number of Black patients to apply CMS Disparity Methods by insurance and race. Of eligible hospitals, 17% had equitable readmission rates by insurance and 30% by race. Hospitals with equitable readmissions by insurance or race cared for a lower percentage of Black patients (insurance, 1.9% [IQR, 0.2%-8.8%] vs 3.3% [IQR, 0.7%-10.8%], P <.01; race, 7.6% [IQR, 3.2%-16.6%] vs 9.3% [IQR, 4.0%-19.0%], P =.01), and differed from nonequitable hospitals in multiple domains (teaching status, geography, size; P <.01). In examining equity by insurance, hospitals with low costs were more likely to have equitable readmissions (odds ratio, 1.57 [95% CI, 1.38-1.77), and there was no relationship between quality and value, and equity. In examining equity by race, hospitals with high overall quality were more likely to have equitable readmissions (odds ratio, 1.14 [95% CI, 1.03-1.26]), and there was no relationship between cost and value, and equity. Conclusion and Relevance: A minority of hospitals achieved equitable readmissions. Notably, hospitals with equitable readmissions were characteristically different from those without. For example, hospitals with equitable readmissions served fewer Black patients, reinforcing the role of structural racism in hospital-level inequities. Implementation of an equitable readmission measure must consider unequal distribution of at-risk patients among hospitals. This cross-sectional study of US hospitals compared hospital and patient characteristics to evaluate equitable rates of readmission by insurance (dual eligible [Medicare and Medicaid] vs non–dual eligible) and race (Black vs White). [ABSTRACT FROM AUTHOR]

A response from the authors of the article "Considering the role of socioeconomic status in hospital outcomes measures" that was published in a 2014 issue of the periodical, is presented.

A letter to the editor is presented in response to the article "Refusal of care: patients' well being and physicians' ethical obligations: 'but doctor, I want to go home'," by Joseph A. Carrese, published in the August 9, 2006 issue (Volume 296, Number 6).

Background: Given the inclusion of orthopaedic quality measures in the Centers for Medicare & Medicaid Services national hospital payment programs, the present study sought to assess whether the public reporting of total hip arthroplasty (THA) and total knee arthroplasty (TKA) risk-standardized readmission rates (RSRRs) and complication rates (RSCRs) was temporally associated with a decrease in the rates of these outcomes among Medicare beneficiaries.Methods: Annual trends in national observed and hospital-level RSRRs and RSCRs were evaluated for patients who underwent hospital-based inpatient hip and/or knee replacement procedures from fiscal year 2010 to fiscal year 2016. Hospital-level rates were calculated with use of the same measures and methodology that were utilized in public reporting. Annual trends in the distribution of hospital-level outcomes were then examined with use of density plots.Results: Complication and readmission rates and variation declined steadily from fiscal year 2010 to fiscal year 2016. Reductions of 33% and 25% were noted in hospital-level RSCRs and RSRRs, respectively. The interquartile range decreased by 18% (relative reduction) for RSCRs and by 34% (relative reduction) for RSRRs. The frequency of risk variables in the complication and readmission models did not systematically change over time, suggesting no evidence of widespread bias or up-coding.Conclusions: This study showed that hospital-level complication and readmission rates following THA and TKA and the variation in hospital-level performance declined during a period coinciding with the start of public reporting and financial incentives associated with measurement. The consistently decreasing trend in rates of and variation in outcomes suggests steady improvements and greater consistency among hospitals in clinical outcomes for THA and TKA patients in the 2016 fiscal year compared with the 2010 fiscal year. The interactions between public reporting, payment, and hospital coding practices are complex and require further study.Level Of Evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence. [ABSTRACT FROM AUTHOR]

Background: To estimate, prior to finalization of claims, the national monthly numbers of admissions and rates of 30-day readmissions and post-discharge observation-stays for Medicare fee-for-service beneficiaries hospitalized with acute myocardial infarction (AMI), heart failure (HF), or pneumonia.Methods: The centers for Medicare & Medicaid Services (CMS) Integrated Data Repository, including the Medicare beneficiary enrollment database, was accessed in June 2015, February 2017, and February 2018. We evaluated patterns of delay in Medicare claims accrual, and used incomplete, non-final claims data to develop and validate models for real-time estimation of admissions, readmissions, and observation stays.Results: These real-time reporting models accurately estimate, within 2 months from admission, the monthly numbers of admissions, 30-day readmission and observation-stay rates for patients with AMI, HF, or pneumonia.Conclusions: This work will allow CMS to track the impact of policy decisions in real time and enable hospitals to better monitor their performance nationally. [ABSTRACT FROM AUTHOR]

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]

Background: Concern has been raised about consequences of including patients with left ventricular assist device (LVAD) or heart transplantation in readmission and mortality measures. Methods: We calculated unadjusted and hospital-specific 30-day risk-standardized mortality (RSMR) and readmission (RSRR) rates for all Medicare fee-for-service beneficiaries with a primary diagnosis of AMI or HF discharged between July 2010 and June 2013. Hospitals were compared before and after excluding LVAD and heart transplantation patients. LVAD indication was measured. Results: In the AMI mortality (n = 506,543) and readmission (n = 526,309) cohorts, 1,166 and 1,016 patients received an LVAD while 3 and 2 had a heart transplantation, respectively. In the HF mortality (n = 1,015,335) and readmission (n = 1,254,124) cohorts, 789 and 931 received an LVAD, while 212 and 202 received a heart transplantation, respectively. Less than 2% of hospitals had either ≥6 patients who received an LVAD or, independently, had ≥1 heart transplantation. The AMI mortality and readmission cohorts used 1.8% and 2.8% of LVADs for semi-permanent/permanent indications, versus 73.8% and 78.0% for HF patients, respectively. The rest were for temporary/external indications. In the AMI cohort, RSMR for hospitals without LVAD patients versus hospitals with ≥6 LVADs was 14.8% and 14.3%, and RSRR was 17.8% and 18.3%, respectively; the HF cohort RSMR was 11.9% and 9.7% and RSRR was 22.6% and 23.4%, respectively. In the AMI cohort, RSMR for hospitals without versus with heart transplantation patients was 14.7% and 13.9% and RSRR was 17.8% and 17.7%, respectively; in the HF cohort, RSMR was 11.9% and 11.0%, and RSRR was 22.6% and 22.6%, respectively. Estimations changed ≤0.1% after excluding LVAD or heart transplantation patients. Conclusion: Hospitals caring for ≥6 patients with LVAD or ≥1 heart transplantation typically had a trend toward lower RSMRs but higher RSRRs. Rates were insignificantly changed when these patients were excluded. LVADs were primarily for acute-care in the AMI cohort and chronic support in the HF cohort. LVAD and heart transplantation patients are a distinct group with differential care requirements and outcomes, thus should be considered separately from the rest of the HF cohort. [ABSTRACT FROM AUTHOR]

Current approaches to health care quality have failed to reduce health care disparities. Despite dramatic increases in the use of quality measurement and associated payment policies, there has been no notable implementation of measurement strategies to reduce health disparities. The National Quality Forum developed a road map to demonstrate how measurement and associated policies can contribute to eliminating disparities and promote health equity. Specifically, the road map presents a four-part strategy whose components are identifying and prioritizing areas to reduce health disparities, implementing evidence-based interventions to reduce disparities, investing in the development and use of health equity performance measures, and incentivizing the reduction of health disparities and achievement of health equity. To demonstrate how the road map can be applied, we present an example of how measurement and value-based payment can be used to reduce racial disparities in hypertension among African Americans. [ABSTRACT FROM AUTHOR]

Background: Publicly reported hospital risk-standardized mortality rates (RSMRs) for acute myocardial infarction (AMI) are calculated for Medicare beneficiaries. Outcomes for older patients with AMI may not reflect general outcomes.Objective: To examine the relationship between hospital 30-day RSMRs for older patients (aged ≥65 years) and those for younger patients (aged 18 to 64 years) and all patients (aged ≥18 years) with AMI.Design: Retrospective cohort study.Setting: 986 hospitals in the ACTION (Acute Coronary Treatment and Intervention Outcomes Network) Registry-Get With the Guidelines.Participants: Adults hospitalized for AMI from 1 October 2010 to 30 September 2014.Measurements: Hospital 30-day RSMRs were calculated for older, younger, and all patients using an electronic health record measure of AMI mortality endorsed by the National Quality Forum. Hospitals were ranked by their 30-day RSMRs for these 3 age groups, and agreement in rankings was plotted. The correlation in hospital AMI achievement scores for each age group was also calculated using the Hospital Value-Based Purchasing (HVBP) Program method computed with the electronic health record measure.Results: 267 763 and 276 031 AMI hospitalizations among older and younger patients, respectively, were identified. Median hospital 30-day RSMRs were 9.4%, 3.0%, and 6.2% for older, younger, and all patients, respectively. Most top- and bottom-performing hospitals for older patients were neither top nor bottom performers for younger patients. In contrast, most top and bottom performers for older patients were also top and bottom performers for all patients. Similarly, HVBP achievement scores for older patients correlated weakly with those for younger patients (R = 0.30) and strongly with those for all patients (R = 0.92).Limitation: Minority of U.S. hospitals.Conclusion: Hospital mortality rankings for older patients with AMI inconsistently reflect rankings for younger patients. Incorporation of younger patients into assessment of hospital outcomes would permit further examination of the presence and effect of age-related quality differences.Primary Funding Source: American College of Cardiology. [ABSTRACT FROM AUTHOR]

Background: To isolate hospital effects on risk-standardized hospital-readmission rates, we examined readmission outcomes among patients who had multiple admissions for a similar diagnosis at more than one hospital within a given year.Methods: We divided the Centers for Medicare and Medicaid Services hospital-wide readmission measure cohort from July 2014 through June 2015 into two random samples. All the patients in the cohort were Medicare recipients who were at least 65 years of age. We used the first sample to calculate the risk-standardized readmission rate within 30 days for each hospital, and we classified hospitals into performance quartiles, with a lower readmission rate indicating better performance (performance-classification sample). The study sample (identified from the second sample) included patients who had two admissions for similar diagnoses at different hospitals that occurred more than 1 month and less than 1 year apart, and we compared the observed readmission rates among patients who had been admitted to hospitals in different performance quartiles.Results: In the performance-classification sample, the median risk-standardized readmission rate was 15.5% (interquartile range, 15.3 to 15.8). The study sample included 37,508 patients who had two admissions for similar diagnoses at a total of 4272 different hospitals. The observed readmission rate was consistently higher among patients admitted to hospitals in a worse-performing quartile than among those admitted to hospitals in a better-performing quartile, but the only significant difference was observed when the patients were admitted to hospitals in which one was in the best-performing quartile and the other was in the worst-performing quartile (absolute difference in readmission rate, 2.0 percentage points; 95% confidence interval, 0.4 to 3.5; P=0.001).Conclusions: When the same patients were admitted with similar diagnoses to hospitals in the best-performing quartile as compared with the worst-performing quartile of hospital readmission performance, there was a significant difference in rates of readmission within 30 days. The findings suggest that hospital quality contributes in part to readmission rates independent of factors involving patients. (Funded by Yale-New Haven Hospital Center for Outcomes Research and Evaluation and others.). [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]

Importance: Readmission rates declined after announcement of the Hospital Readmission Reduction Program (HRRP), which penalizes hospitals for excess readmissions for acute myocardial infarction (AMI), heart failure (HF), and pneumonia.Objective: To compare trends in readmission rates for target and nontarget conditions, stratified by hospital penalty status.Design, Setting, and Participants: Retrospective cohort study of Medicare fee-for-service beneficiaries older than 64 years discharged between January 1, 2008, and June 30, 2015, from 2214 penalty hospitals and 1283 nonpenalty hospitals. Difference-interrupted time-series models were used to compare trends in readmission rates by condition and penalty status.Exposure: Hospital penalty status or target condition under the HRRP.Main Outcomes and Measures: Thirty-day risk adjusted, all-cause unplanned readmission rates for target and nontarget conditions.Results: The study included 48 137 102 hospitalizations of 20 351 161 Medicare beneficiaries. In January 2008, the mean readmission rates for AMI, HF, pneumonia, and nontarget conditions were 21.9%, 27.5%, 20.1%, and 18.4%, respectively, at hospitals later subject to financial penalties and 18.7%, 24.2%, 17.4%, and 15.7% at hospitals not subject to penalties. Between January 2008 and March 2010, prior to HRRP announcement, readmission rates were stable across hospitals (except AMI at nonpenalty hospitals). Following announcement of HRRP (March 2010), readmission rates for both target and nontarget conditions declined significantly faster for patients at hospitals later subject to financial penalties compared with those at nonpenalized hospitals (for AMI, additional decrease of -1.24 [95% CI, -1.84 to -0.65] percentage points per year relative to nonpenalty discharges; for HF, -1.25 [95% CI, -1.64 to -0.86]; for pneumonia, -1.37 [95% CI, -1.80 to -0.95]; and for nontarget conditions, -0.27 [95% CI, -0.38 to -0.17]; P < .001 for all). For penalty hospitals, readmission rates for target conditions declined significantly faster compared with nontarget conditions (for AMI, additional decline of -0.49 [95% CI, -0.81 to -0.16] percentage points per year relative to nontarget conditions [P = .004]; for HF, -0.90 [95% CI, -1.18 to -0.62; P < .001]; and for pneumonia, -0.57 [95% CI, -0.92 to -0.23; P < .001]). In contrast, among nonpenalty hospitals, readmissions for target conditions declined similarly or more slowly compared with nontarget conditions (for AMI, additional increase of 0.48 [95% CI, 0.01-0.95] percentage points per year [P = .05]; for HF, 0.08 [95% CI, -0.30 to 0.46; P = .67]; for pneumonia, 0.53 [95% CI, 0.13-0.93; P = .01]). After HRRP implementation in October 2012, the rate of change for readmission rates plateaued (P < .05 for all except pneumonia at nonpenalty hospitals), with the greatest relative change observed among hospitals subject to financial penalty.Conclusions and Relevance: Medicare fee-for-service patients at hospitals subject to penalties under the HRRP had greater reductions in readmission rates compared with those at nonpenalized hospitals. Changes were greater for target vs nontarget conditions for patients at the penalized hospitals but not at the other hospitals. [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]

Importance: Little contemporary information is available about comparative performance between Veterans Affairs (VA) and non-VA hospitals, particularly related to mortality and readmission rates, 2 important outcomes of care.Objective: To assess and compare mortality and readmission rates among men in VA and non-VA hospitals.Design, Setting, and Participants: Cross-sectional analysis involving male Medicare fee-for-service beneficiaries aged 65 years or older hospitalized between 2010 and 2013 in VA and non-VA acute care hospitals for acute myocardial infarction (AMI), heart failure (HF), or pneumonia using the Medicare Standard Analytic Files and Enrollment Database together with VA administrative claims data. To avoid confounding geographic effects with health care system effects, we studied VA and non-VA hospitals within the same metropolitan statistical area (MSA).Exposures: Hospitalization in a VA or non-VA hospital in MSAs that contained at least 1 VA and non-VA hospital.Main Outcomes and Measures: For each condition, 30-day risk-standardized mortality rates and risk-standardized readmission rates for VA and non-VA hospitals. Mean aggregated within-MSA differences in mortality and readmission rates were also assessed.Results: We studied 104 VA and 1513 non-VA hospitals, with each condition-outcome analysis cohort for VA and non-VA hospitals containing at least 7900 patients (men; ≥65 years), in 92 MSAs. Mortality rates were lower in VA hospitals than non-VA hospitals for AMI (13.5% vs 13.7%, P = .02; -0.2 percentage-point difference) and HF (11.4% vs 11.9%, P = .008; -0.5 percentage-point difference), but higher for pneumonia (12.6% vs 12.2%, P = .045; 0.4 percentage-point difference). In contrast, readmission rates were higher in VA hospitals for all 3 conditions (AMI, 17.8% vs 17.2%, 0.6 percentage-point difference; HF, 24.7% vs 23.5%, 1.2 percentage-point difference; pneumonia, 19.4% vs 18.7%, 0.7 percentage-point difference, all P < .001). In within-MSA comparisons, VA hospitals had lower mortality rates for AMI (percentage-point difference, -0.22; 95% CI, -0.40 to -0.04) and HF (-0.63; 95% CI, -0.95 to -0.31), and mortality rates for pneumonia were not significantly different (-0.03; 95% CI, -0.46 to 0.40); however, VA hospitals had higher readmission rates for AMI (0.62; 95% CI, 0.48 to 0.75), HF (0.97; 95% CI, 0.59 to 1.34), or pneumonia (0.66; 95% CI, 0.41 to 0.91).Conclusions and Relevance: Among older men with AMI, HF, or pneumonia, hospitalization at VA hospitals, compared with hospitalization at non-VA hospitals, was associated with lower 30-day risk-standardized all-cause mortality rates for AMI and HF, and higher 30-day risk-standardized all-cause readmission rates for all 3 conditions, both nationally and within similar geographic areas, although absolute differences between these outcomes at VA and non-VA hospitals were small. [ABSTRACT FROM AUTHOR]

BACKGROUND: Existing publicly reported readmission measures are condition-specific, representing less than 20% of adult hospitalizations. An all-condition measure may better measure quality and promote innovation. OBJECTIVE: To develop an all-condition, hospital-wide readmission measure. DESIGN: Measure development study. SETTING: 4821 U.S. hospitals. PATIENTS: Medicare fee-for-service beneficiaries aged 65 years or older. MEASUREMENTS: Hospital-level, risk-standardized unplanned readmissions within 30 days of discharge. The measure uses Medicare fee-for-service claims and is a composite of 5 specialty-based, risk-standardized rates for medicine, surgery/gynecology, cardiorespiratory, cardiovascular, and neurology cohorts. The 2007-2008 admissions were randomly split for development and validation. Models were adjusted for age, principal diagnosis, and comorbid conditions. Calibration in Medicare and all-payer data was examined, and hospital rankings in the development and validation samples were compared. RESULTS: The development data set contained 8 018 949 admissions associated with 1 276 165 unplanned readmissions (15.9%). The median hospital risk-standardized unplanned readmission rate was 15.8 (range, 11.6 to 21.9). The 5 specialty cohort models accurately predicted readmission risk in both Medicare and all-payer data sets for average-risk patients but slightly overestimated readmission risk at the extremes. Overall hospital risk-standardized readmission rates did not differ statistically in the split samples (P = 0.71 for difference in rank), and 76% of hospitals' validation-set rankings were within 2 deciles of the development rank (24% were more than 2 deciles). Of hospitals ranking in the top or bottom deciles, 90% remained within 2 deciles (10% were more than 2 deciles) and 82% remained within 1 decile (18% were more than 1 decile). LIMITATION: Risk adjustment was limited to that available in claims data. CONCLUSION: A claims-based, hospital-wide unplanned readmission measure for profiling hospitals produced reasonably consistent results in different data sets and was similarly calibrated in both Medicare and all-payer data. [ABSTRACT FROM AUTHOR]

: Isuru Ranasinghe and colleagues compare readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia in adults aged 18 to 64 years with readmissions in those aged 65 and older. Please see later in the article for the Editors' Summary [ABSTRACT FROM AUTHOR]

Objectives To determine whether high performing hospitals with low 30 day risk standardized readmission rates have a lower proportion of readmissions from specific diagnoses and time periods after admission or instead have a similar distribution of readmission diagnoses and timing to lower performing institutions. Design Retrospective cohort study. Setting Medicare beneficiaries in the United States. Participants Patients aged 65 and older who were readmitted within 30 days after hospital admission for heart failure, acute myocardial infarction, or pneumonia in 2007-09. Main outcome measures Readmission diagnoses were classified with a modified version of the Centers for Medicare and Medicaid Services' condition categories, and readmission timing was classified by day (0-30) after hospital discharge. Hospital 30 day risk standardized readmission rates over the three years of study were calculated with public reporting methods of the US federal government, and hospitals were categorized with bootstrap analysis as having high, average, or low readmission performance for each index condition. High and low performing hospitals had ≥95% probability of having an interval estimate respectively less than or greater than the national 30 day readmission rate over the three year period of study. All remaining hospitals were considered average performers. Results For readmissions in the 30 days after the index admission, there were 320 003 after 1 291 211 admissions for heart failure (4041 hospitals), 102 536 after 517 827 admissions for acute myocardial infarction (2378 hospitals), and 208 438 after 1 135 932 admissions for pneumonia (4283 hospitals). The distribution of readmissions by diagnosis was similar across categories of hospital performance for all three conditions. High performing hospitals had fewer readmissions for all common diagnoses. Median time to readmission was similar by hospital performance for heart failure and acute myocardial infarction, though was 1.4 days longer among high versus low performing hospitals for pneumonia (P<0.001). Findings were unchanged after adjustment for other hospital characteristics potentially associated with readmission patterns. Conclusions High performing hospitals have proportionately fewer 30 day readmissions without differences in readmission diagnoses and timing, suggesting the possible benefit of strategies that lower risk of readmission globally rather than for specific diagnoses or time periods after hospital stay. [ABSTRACT FROM AUTHOR]

The article examines the association between hospital readmission and mortality rates for cardiac patients who were presented with clinical case of acute myocardial infarction, heart failure and pneumonia. It highlights the impact of quality of medical care provided by health care centers in resolving the risk associated with cardiac patients. The Centers for Medicare & Medicaid Services in the U.S. on the basis of a cohort analysis revealed that differences in performance of hospitals and diagnostic parameters implied by hospital were of immense use in determination of readmission rate and mortality rate of cardiac patients.

The article focuses on a study regarding the evaluation of readmission and timing among Medicare beneficiaries who are readmitted for heart failure (HF), acute myocardial infarction (MI) and pneumonia. It mentions that hospital readmissions report poor health care facility and states that the U.S. Centers for Medicare and Medicaid Services (CMS) has started public reporting of outcomes for improving the hospital care quality. The study reveals that 30-day readmissions were frequent throughout the month after rehospitalization for HF, MI and pneumonia. It also presents statistics which depict the readmission of patients.

A sufficient cardiology workforce is necessary to ensure access to cardiovascular care. Specifically, access to cardiologists is important in the management and treatment of chronic cardiovascular disease. Given this, we examined the supply and distribution of the cardiologist workforce. In doing so, we mapped the ratios of cardiologists, primary care physicians, and total physicians to the population age sixty-five or older within different Hospital Referral Regions from the years 1995 and 2007. We found that within the twelve-year span of our study, the cardiology workforce grew modestly compared with the primary care physician and total physician workforces. Also, despite increases in the number of cardiologists, there was a persistent geographic maldistribution of the workforce. For example, approximately 60 percent of the elderly population had access to only 38 percent of the cardiologists. Our results suggest that large segments of the US population, specifically in rural and socioeconomically disadvantaged areas, continue to have a lower concentration of cardiologists. This maldistribution could be addressed through a variety of strategies, including the use of telemedicine and economic incentives. [ABSTRACT FROM AUTHOR]

The article discusses the results of a study which used a system developed by the U.S. Centers for Medicare & Medicaid Services to examine the national performance in door-in to door-out (DIDO) time. The study examined the association of the national median DIDO time with patient and hospital characteristics. The study concluded that rarely does the DIDO time met the recommended 30 minutes among patients requiring transfer to another facility for percutaneous coronary intervention.

The article reviews the statistical models and patient predictors of readmission after heart failure(HF). It discusses that readmission after HF hospitalization is the altering focus for physicians and policy manufacturers and that the statistical models are demanded to evaluate patient risk and to equate hospital operation. It infers that the performed systematic review is used to report the models which are planned to compare hospital rates of readmission and to anticipate the patient's risk of readmission.

The article discusses about the performance of Veterans Affairs medical centers and the utilization of recommended ambulatory care services. According to the article, the Veterans Affairs medical centers are said to have presented better preventive and chronic disease care when compared to other health care organizations. A research was conducted to determine its accuracy using the nationally representative 2000 and 2004 surveys of the Behavior Risk Factor Surveillance System. It was concluded in the research that even though there is an arising emphasis on the quality of care and enhanced performance over US health care system, those who are receiving care from VAMC remains as those who are more inclined to obtain recommended ambulatory care.

The article presents a medical research study that examined self-reported financial barriers to medical care services or medication in patients with acute myocardial infarction. Data was achieved through the Prospective Registry Evaluating Myocardial Infarction: Event and Recovery study. The health status symptoms, overall health status function, and rehospitalization rates of the patients were measured. The authors found that financial barriers to health care services and medications are associated with worse recovery after acute myocardial function, as evidenced by more angina, a poorer quality of life, and a greater risk of rehospitalization.

Background: Temporal changes in the readmission rates for patient groups and conditions that were not directly under the purview of the Hospital Readmissions Reduction Program (HRRP) can help assess whether efforts to lower readmissions extended beyond targeted patients and conditions.Methods: Using the Nationwide Readmissions Database (2010-2015), we assessed trends in all-cause readmission rates for 1 of the 3 HRRP conditions (acute myocardial infarction, heart failure, pneumonia) or conditions not targeted by the HRRP in age-insurance groups defined by age group (≥65 years or <65 years) and payer (Medicare, Medicaid, or private insurance).Results: In the group aged ≥65 years, readmission rates for those covered by Medicare, Medicaid, and private insurance decreased annually for acute myocardial infarction (risk-adjusted odds ratio [OR; 95% confidence interval] among Medicare patients, 0.94 [0.94-0.95], among Medicaid patients, 0.93 [0.90-0.97], and among patients with private-insurance, 0.95 [0.93-0.97]); heart failure (ORs, 0.96 [0.96-0.97], 0.96 [0.94-0.98], and 0.97 [0.96-0.99], for the 3 payers, respectively), and pneumonia (ORs, 0.96 [0.96-0.97), 0.94 [0.92-0.96], and 0.96 [0.95-0.97], respectively). Readmission rates also decreased in the group aged <65 years for acute myocardial infarction (ORs: Medicare 0.97 [0.96-0.98], Medicaid 0.94 [0.92-0.95], and private insurance 0.93 [0.92-0.94]), heart failure (ORs, 0.98 [0.97-0.98]: 0.96 [0.96-0.97], and 0.97 [0.95-0.98], for the 3 payers, respectively), and pneumonia (ORs, 0.98 [0.97-0.99], 0.98 [0.97-0.99], and 0.98 [0.97-1.00], respectively). Further, readmission rates decreased significantly for non-target conditions.Conclusions: There appears to be a systematic improvement in readmission rates for patient groups beyond the population of fee-for-service, older, Medicare beneficiaries included in the HRRP. [ABSTRACT FROM AUTHOR]

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.

Abstract: 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 <1% and 4% of the variation for their respective RSRRs. 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. [Copyright &y& Elsevier]