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Long-term sequelae after symptomatic SARS-CoV-2 infection may impact well-being, yet existing data primarily focus on discrete symptoms and/or health care use.To compare patient-reported outcomes of physical, mental, and social well-being among adults with symptomatic illness who received a positive vs negative test result for SARS-CoV-2 infection.This cohort study was a planned interim analysis of an ongoing multicenter prospective longitudinal registry study (the Innovative Support for Patients With SARS-CoV-2 Infections Registry [INSPIRE]). Participants were enrolled from December 11, 2020, to September 10, 2021, and comprised adults (aged ≥18 years) with acute symptoms suggestive of SARS-CoV-2 infection at the time of receipt of a SARS-CoV-2 test approved by the US Food and Drug Administration. The analysis included the first 1000 participants who completed baseline and 3-month follow-up surveys consisting of questions from the 29-item Patient-Reported Outcomes Measurement Information System (PROMIS-29; 7 subscales, including physical function, anxiety, depression, fatigue, social participation, sleep disturbance, and pain interference) and the PROMIS Short Form-Cognitive Function 8a scale, for which population-normed T scores were reported.SARS-CoV-2 status (positive or negative test result) at enrollment.Mean PROMIS scores for participants with positive COVID-19 tests vs negative COVID-19 tests were compared descriptively and using multivariable regression analysis.Among 1000 participants, 722 (72.2%) received a positive COVID-19 result and 278 (27.8%) received a negative result; 406 of 998 participants (40.7%) were aged 18 to 34 years, 644 of 972 (66.3%) were female, 833 of 984 (84.7%) were non-Hispanic, and 685 of 974 (70.3%) were White. A total of 282 of 712 participants (39.6%) in the COVID-19-positive group and 147 of 275 participants (53.5%) in the COVID-19-negative group reported persistently poor physical, mental, or social well-being at 3-month follow-up. After adjustment, improvements in well-being were statistically and clinically greater for participants in the COVID-19-positive group vs the COVID-19-negative group only for social participation (β = 3.32; 95% CI, 1.84-4.80; P .001); changes in other well-being domains were not clinically different between groups. Improvements in well-being in the COVID-19-positive group were concentrated among participants aged 18 to 34 years (eg, social participation: β = 3.90; 95% CI, 1.75-6.05; P .001) and those who presented for COVID-19 testing in an ambulatory setting (eg, social participation: β = 4.16; 95% CI, 2.12-6.20; P .001).In this study, participants in both the COVID-19-positive and COVID-19-negative groups reported persistently poor physical, mental, or social well-being at 3-month follow-up. Although some individuals had clinically meaningful improvements over time, many reported moderate to severe impairments in well-being 3 months later. These results highlight the importance of including a control group of participants with negative COVID-19 results for comparison when examining the sequelae of COVID-19.

The COVID-19 pandemic has disproportionately affected Hispanic or Latino, non-Hispanic Black (Black), non-Hispanic American Indian or Alaska Native (AI/AN), and non-Hispanic Native Hawaiian or Other Pacific Islander (NH/PI) populations in the United States. These populations have experienced higher rates of infection and mortality compared with the non-Hispanic White (White) population (1-5) and greater excess mortality (i.e., the percentage increase in the number of persons who have died relative to the expected number of deaths for a given place and time) (6). A limitation of existing research on excess mortality among racial/ethnic minority groups has been the lack of adjustment for age and population change over time. This study assessed excess mortality incidence rates (IRs) (e.g., the number of excess deaths per 100,000 person-years) in the United States during December 29, 2019-January 2, 2021, by race/ethnicity and age group using data from the National Vital Statistics System. Among all assessed racial/ethnic groups (non-Hispanic Asian [Asian], AI/AN, Black, Hispanic, NH/PI, and White populations), excess mortality IRs were higher among persons aged ≥65 years (426.4 to 1033.5 excess deaths per 100,000 person-years) than among those aged 25-64 years (30.2 to 221.1) and those aged25 years (-2.9 to 14.1). Among persons aged65 years, Black and AI/AN populations had the highest excess mortality IRs. Among adults aged ≥65 years, Black and Hispanic persons experienced the highest excess mortality IRs of1,000 excess deaths per 100,000 person-years. These findings could help guide more tailored public health messaging and mitigation efforts to reduce disparities in mortality associated with the COVID-19 pandemic in the United States,* by identifying the racial/ethnic groups and age groups with the highest excess mortality rates.

BACKGROUND: Rural communities face challenges in accessing healthcare services due to physician shortages and limited unscheduled care capabilities in office settings. As a result, rural hospital-based Emergency Departments (ED) may disproportionately provide acute, unscheduled care needs. We sought to examine differences in ED utilization and the relative role of the ED in providing access to unscheduled care between rural and urban communities. METHODS: Using a 20% sample of the 2012 Medicare Chronic Condition Warehouse, we studied the overall ED visit rate and the unscheduled care rate by geography using the Dartmouth Atlas’ hospital referral regions (HRR). We calculated HRR urbanicity as the proportion of beneficiaries residing in an urban zip code within each HRR. We report descriptive statistics and utilize K-means clustering based on the ED visit rates and unscheduled care rates. RESULTS: We found rural ED use is more common and disproportionately the site of unscheduled care delivery when compared to urban communities. The ED visit and unscheduled care proportions were negatively correlated with increased urbanicity (r = −0.48, p < 0.001; r = −0.58, p < 0.001). CONCLUSION: he use and role of EDs by Medicare beneficiaries appears to be substantially different between urban and rural areas. This suggests that the ED may play a distinct role within the healthcare delivery system of rural communities that face disproportionate barriers to care access.

ObjectivesHigh-value care is providing high quality care at low cost; we sought to define hospital value and identify the characteristics of hospitals which provide high-value care.DesignRetrospective observational study.SettingAcute care hospitals in the USA.ParticipantsAll Medicare beneficiaries with claims included in Center for Medicare & Medicaid Services Overall Star Ratings or in publicly available Medicare spending per beneficiary data.Primary and secondary outcome measuresOur primary outcome was value defined as the difference between Star Ratings quality score and Medicare spending; the secondary outcome was classification as a 4 or 5 star hospital with lowest quintile Medicare spending (‘high value’) or 1 or 2 star hospital with highest quintile spending (‘low value’).ResultsTwo thousand nine hundred and fourteen hospitals had both quality and spending data, and were included. The value score had a mean (SD) of 0.58 (1.79). A total of 286 hospitals were classified as high value; these represented 28.6% of 999 4 and 5 star hospitals and 46.8% of 611 low cost hospitals. A total of 258 hospitals were classified as low value; these represented 26.6% of 970 1 and 2 star hospitals and 49.3% of 523 high cost hospitals. In regression models ownership, non-teaching status, beds, urbanity, nurse to bed ratio, percentage of dual eligible Medicare patients and percentage of disproportionate share hospital payments were associated with the primary value score.ConclusionsThere are high quality hospitals that are not high value, and a number of factors are strongly associated with being low or high value. These findings can inform efforts of policymakers and hospitals to increase the value of care.

Disease clinical treatment measures, such as inpatient length of stay (LOS), have been examined for most if not all diseases. Such analysis has important implications for the management and planning of health care, financial, and human resources. In addition, clinical treatment measures can also informatively reflect intrinsic disease properties such as severity. The existing studies mostly focus on either a single disease (or a few pre-selected and closely related diseases) or all diseases combined. In this study, we take a new and innovative perspective, examine the interconnections in length of stay (LOS) among diseases, and construct the very first disease clinical treatment network on LOS. To accommodate uniquely challenging data distributions, a new conditional network construction approach is developed. Based on the constructed network, the analysis of important network properties is conducted. The Medicare data on 100 000 randomly selected subjects for the period of January 2008 to December 2018 is analyzed. The network structure and key properties are found to have sensible biomedical interpretations. Being the very first of its kind, this study can be informative to disease clinical management, advance our understanding of disease interconnections, and foster complex network analysis.

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.

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.

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.

This cohort study uses Medicare prescription claims data to evaluate patterns of direct oral anticoagulant prescribing by US clinicians between 2013 and 2018., Key Points Question How have patterns of direct-acting oral anticoagulant (DOAC) use changed among US clinicians between 2013 and 2018? Findings In this cohort study of Medicare prescription claim data encompassing 325 666 clinicians from 2013 to 2018, most clinicians continued to use warfarin as their predominant or only anticoagulant instead of DOACs, including 1 in 5 general medicine practitioners exclusively using warfarin in 2018. Despite an increase in DOAC prescribing, those prescribing only warfarin in 2013 had lower proportionate DOAC use throughout the study than 2013 DOAC prescribers. Meaning In this study, many clinicians did not prescribe any DOACs in 2018, suggesting a need to address barriers to DOAC use., Importance Contemporary national clinical practice guidelines recommend direct-acting oral anticoagulants (DOACs) as the first-line anticoagulant strategy over warfarin for most indications, especially among older individuals with an elevated bleeding risk. Objective To evaluate anticoagulant prescribing and DOAC uptake by US clinicians in the Medicare population. Design, Setting, and Participants This retrospective cohort study included all US clinicians with more than 10 Medicare oral anticoagulant prescription claims, who were included in the national Medicare Provider Utilization and Payment Data (2013-2018). Data analyses were conducted between October 2020 and October 2021. Exposures DOAC prescription in 2013. Main Outcomes and Measures Clinicians were categorized based on 2013 prescribing as solely prescribing warfarin, DOAC, or both, and their temporal trajectories of proportionate DOAC use were examined. Results The analysis included 325 666 unique clinicians with more than 10 oral anticoagulant prescriptions between 2013 and 2018 (26 620 [8.2%] cardiologists, 85 563 [26.3%] internal medicine physicians, 84 369 [25.9%] family medicine physicians, and 81 161 [24.9%] advanced practice clinicians, including nurse practitioners and physician assistants). In 2013, among 91 837 prescribers, 54 501 (59.3%) prescribed only warfarin, 1918 (2.1%) prescribed only a DOAC, and 35 418 (38.6%) prescribed both. During the study period, the number of clinicians prescribing DOACs increased, but 19% continued to prescribe only warfarin in 2018. While 359 cardiologists prescribing anticoagulants (1.6%) were warfarin-only prescribers, 10 414 (20.0%) and 6296 (12.6%) of family and internal medicine physicians also prescribed only warfarin, respectively. Clinicians prescribing only warfarin in 2013 had lower proportionate DOAC use throughout the study compared with 2013 DOAC prescribers, which represents a median (IQR) of 41.9% (20.3%-61.9%) of their anticoagulant prescriptions in 2018 vs 67.0% (49.9%-82.8%) for DOAC prescribers. Conclusions and Relevance Despite the increase in DOAC use among Medicare beneficiaries, many clinicians in this study continued to use warfarin as their predominant or only anticoagulant instead of DOACs. There is a need to address barriers to the uptake of these medications to realize their potential benefits for patients.

Background: Evidence from large randomized clinical trials supports the benefit of SGLT2i (sodium-glucose cotransporter-2 inhibitors) to improve cardiovascular and kidney outcomes in patients with type 2 diabetes with or at high risk for atherosclerotic cardiovascular disease or chronic kidney disease. Considering this evidence, which has been expanding since the product label indication for empagliflozin to reduce risk of cardiovascular death in 2016, clinician-level variation in the prescription of SGLT2i among US Medicare beneficiaries was evaluated. Methods: Antihyperglycemic medication prescribers were identified as those physicians and advanced practice providers prescribing metformin in Medicare part D prescriber data. In this cross-sectional study, the proportion prescribing SGLT2i was assessed overall and across specialties in 2018, with changes assessed from 2014 to 2018. SGLT2i use was compared with other second-line antihyperglycemic medication classes, sulfonylureas and DPP4is (dipeptidyl peptidase-4 inhibitors). Results: Among 232 523 unique clinicians who prescribed metformin for Medicare beneficiaries in 2018 (diabetes-treating clinicians), 45 255 (19.5%) prescribed SGLT2i. There was substantial variation across specialties—from 72% of endocrinologists to 14% of cardiologists who prescribed metformin also prescribed SGLT2i. Between 2014 and 2018, the number prescribing SGLT2i increased 5-fold from 9048 in 2014 to 45 255 in 2018. Among clinicians who prescribed both sulfonylureas and SGLT2i in 2018, SGLT2i was prescribed to a median 33 beneficiaries for every 100 prescribed sulfonylureas (interquartile range, 18–67). SGLT2i use relative to sulfonylureas increased from 19 (interquartile range, 11–34) per 100 in 2014 to 33 (interquartile range, 18–67) per 100 in 2018 ( P trend Conclusions: Eighty percent of clinicians prescribing metformin to Medicare beneficiaries did not prescribe SGLT2i in 2018. Moreover, sulfonylureas prescriptions were 3 times more frequent than those of SGLT2is, although a pattern of increasing uptake may portend future trends. These findings highlight a baseline opportunity to improve care and outcomes for patients with type 2 diabetes.

This study uses national death certificate data to characterize trends in death and excess mortality from drug overdoses, homicides, unintentional injuries, motor vehicle crashes, and suicide during the first 6 months of the pandemic in the US.

Background: Coronary artery bypass graft (CABG) surgery is a focus of bundled and alternate payment models that capture outcomes up to 90 days postsurgery. While clinical registry risk models perform well, measures encompassing mortality beyond 30 days do not currently exist. We aimed to develop a risk-adjusted hospital-level 90-day all-cause mortality measure intended for assessing hospital performance in payment models of CABG surgery using administrative data. Methods: Building upon Centers for Medicare and Medicaid Services hospital-level 30-day all-cause CABG mortality measure specifications, we extended the mortality timeframe to 90 days after surgery and developed a new hierarchical logistic regression model to calculate hospital risk-standardized 90-day all-cause mortality rates for patients hospitalized for isolated CABG. The model was derived from Medicare claims data for a 3-year cohort between July 2014 to June 2017. The data set was randomly split into 50:50 development and validation samples. The model performance was evaluated with C statistics, overfitting indices, and calibration plot. The empirical validity of the measure result at the hospital level was evaluated against the Society of Thoracic Surgeons composite star rating. Results: Among 137 819 CABG procedures performed in 1183 hospitals, the unadjusted mortality rate within 30 and 90 days were 3.1% and 4.7%, respectively. The final model included 27 variables. Hospital-level 90-day risk-standardized mortality rates ranged between 2.04% and 11.26%, with a median of 4.67%. C statistics in the development and validation samples were 0.766 and 0.772, respectively. We identified a strong positive correlation between 30- and 90-day risk-standardized mortality rates, with a regression slope of 1.09. Risk-standardized mortality rates also showed a stepwise trend of lower 90-day mortality with higher Society of Thoracic Surgeons composite star ratings. Conclusions: We present a measure of hospital-level 90-day risk-standardized mortality rates following isolated CABG. This measure complements Centers for Medicare and Medicaid Services’ existing 30-day CABG mortality measure by providing greater insight into the postacute recovery period. It offers a balancing measure to ensure efforts to reduce costs associated with CABG recovery and rehabilitation do not result in unintended consequences.

Low-income older adults who are dually eligible (DE) for Medicare and Medicaid often experience worse outcomes following hospitalization. Among other federal policies aimed at improving health for DE patients, Medicare has recently begun reporting disparities in within-hospital readmissions. The degree to which disparities for DE patients are owing to differences in community-level factors or, conversely, are amenable to hospital quality improvement, remains heavily debated.To examine the extent to which within-hospital disparities in 30-day readmission rates for DE patients are ameliorated by state- and community-level factors.In this retrospective cohort study, Centers for MedicareMedicaid Services (CMS) Disparity Methods were used to calculate within-hospital disparities in 30-day risk-adjusted readmission rates for DE vs non-DE patients in US hospitals participating in Medicare. All analyses were performed in February and March 2019. The study included Medicare patients (aged ≥65 years) hospitalized for acute myocardial infarction (AMI), heart failure (HF), or pneumonia in 2014 to 2017.Within-hospital disparities, as measured by the rate difference (RD) in 30-day readmission between DE vs non-DE patients following admission for AMI, HF, or pneumonia; variance across hospitals; and correlation of hospital RDs with and without adjustment for state Medicaid eligibility policies and community-level factors.The final sample included 475 444 patients admitted for AMI, 898 395 for HF, and 1 214 282 for pneumonia, of whom 13.2%, 17.4%, and 23.0% were DE patients, respectively. Dually eligible patients had higher 30-day readmission rates relative to non-DE patients (RD0) in 99.0% (AMI), 99.4% (HF), and 97.5% (pneumonia) of US hospitals. Across hospitals, the mean (IQR) RD between DE vs non-DE was 1.00% (0.87%-1.10%) for AMI, 0.82% (0.73%-0.96%) for HF, and 0.53% (0.37%-0.71%) for pneumonia. The mean (IQR) RD after adjustment for community-level factors was 0.87% (0.73%-0.97%) for AMI, 0.67% (0.57%-0.80%) for HF, and 0.42% (0.29%-0.57%) for pneumonia. Relative hospital rankings of corresponding within-hospital disparities before and after community-level adjustment were highly correlated (Pearson coefficient, 0.98).In this cohort study, within-hospital disparities in 30-day readmission for DE patients were modestly associated with differences in state Medicaid policies and community-level factors. This suggests that remaining variation in these disparities should be the focus of hospital efforts to improve the quality of care transitions at discharge for DE patients in efforts to advance equity.

Objectives: This study aimed to characterize the distribution of acute care visits among Medicare beneficiaries receiving skilled nursing facility (SNF) services. Methods: We conducted a cross-sectional analysis of a 20% sample of continuously enrolled Medicare beneficiaries in the 2012 Chronic Condition Warehouse data set. Beneficiaries were grouped by the number of days of SNF services, and acute care visits were categorized as “before SNF,” “during SNF,” or “after SNF.” Results: Among the 10,717,786 Medicare beneficiaries analyzed, 384,312 (3.6%) had at least one SNF stay. Discussion: Beneficiaries who received SNF services had a higher proportion of acute care visits made to emergency departments (EDs) than beneficiaries who did not receive SNF services. Also, a higher proportion of acute care visits were made to EDs by beneficiaries after a SNF stay in comparison to residents actively residing in a SNF. The acute care capabilities of SNFs and post-SNF transitions of care to the community setting are discussed.

While the impact of coronavirus disease 2019 (COVID-19) has varied greatly across the United States, there has been little assessment of hospital resources and mortality. We examine hospital resources and death counts among hospital referral regions (HRRs) from March 1 to July 26, 2020. This was an analysis of American Hospital Association data with COVID-19 data from the New York Times. Hospital-based resource availabilities were characterized per COVID-19 case. Death count was defined by monthly confirmed COVID-19 deaths. Geographic areas with fewer intensive care unit (ICU) beds (incident rate ratio [IRR], 0.194; 95% CI, 0.076-0.491), nurses (IRR, 0.927; 95% CI, 0.888-0.967), and general medicine/surgical beds (IRR, 0.800; 95% CI, 0.696-0.920) per COVID-19 case were statistically significantly associated with greater deaths in April. This underscores the potential impact of innovative hospital capacity protocols and care models to create resource flexibility to limit system overload early in a pandemic.

ObjectiveTo determine whether informed consent for surgical procedures performed in US hospitals meet a minimum standard of quality, we developed and tested a quality measure of informed consent documents.DesignRetrospective observational study of informed consent documents.Setting25 US hospitals, diverse in size and geographical region.CohortAmong Medicare fee-for-service patients undergoing elective procedures in participating hospitals, we assessed the informed consent documents associated with these procedures. We aimed to review 100 qualifying procedures per hospital; the selected sample was representative of the procedure types performed at each hospital.Primary outcomeThe outcome was hospital quality of informed consent documents, assessed by two independent raters using an eight-item instrument previously developed for this measure and scored on a scale of 0–20, with 20 representing the highest quality. The outcome was reported as the mean hospital document score and the proportion of documents meeting a quality threshold of 10. Reliability of the hospital score was determined based on subsets of randomly selected documents; face validity was assessed using stakeholder feedback.ResultsAmong 2480 informed consent documents from 25 hospitals, mean hospital scores ranged from 0.6 (95% CI 0.3 to 0.9) to 10.8 (95% CI 10.0 to 11.6). Most hospitals had at least one document score at least 10 out of 20 points, but only two hospitals had >50% of their documents score above a 10-point threshold. The Spearman correlation of the measures score was 0.92. Stakeholders reported that the measure was important, though some felt it did not go far enough to assess informed consent quality.ConclusionAll hospitals performed poorly on a measure of informed consent document quality, though there was some variation across hospitals. Measuring the quality of hospital’s informed consent documents can serve as a first step in driving attention to gaps in quality.

Objectives To determine whether patients discharged after hospital admissions for conditions covered by national readmission programs who received care in emergency departments or observation units but were not readmitted within 30 days had an increased risk of death and to evaluate temporal trends in post-discharge acute care utilization in inpatient units, emergency departments, and observation units for these patients. Design Retrospective cohort study. Setting Medicare claims data for 2008-16 in the United States. Participants Patients aged 65 or older admitted to hospital with heart failure, acute myocardial infarction, or pneumonia—conditions included in the US Hospital Readmissions Reduction Program. Main outcome measures Post-discharge 30 day mortality according to patients’ 30 day acute care utilization; acute care utilization in inpatient and observation units and the emergency department during the 30 day and 31-90 day post-discharge period. Results 3 772 924 hospital admissions for heart failure, 1 570 113 for acute myocardial infarction, and 3 131 162 for pneumonia occurred. The overall post-discharge 30 day mortality was 8.7% for heart failure, 7.3% for acute myocardial infarction, and 8.4% for pneumonia. Risk adjusted mortality increased annually by 0.05% (95% confidence interval 0.02% to 0.08%) for heart failure, decreased by 0.06% (−0.09% to −0.04%) for acute myocardial infarction, and did not significantly change for pneumonia. Specifically, mortality increased for patients with heart failure who did not utilize any post-discharge acute care, increasing at a rate of 0.08% (0.05% to 0.12%) per year, exceeding the overall absolute annual increase in post-discharge mortality in heart failure, without an increase in mortality in observation units or the emergency department. Concurrent with a reduction in 30 day readmission rates, stays for observation and visits to the emergency department increased across all three conditions during and beyond the 30 day post-discharge period. Overall 30 day post-acute care utilization did not change significantly. Conclusions The only condition with increasing mortality through the study period was heart failure; the increase preceded the policy and was not present among patients who received emergency department or observation unit care without admission to hospital. During this period, the overall acute care utilization in the 30 days after discharge significantly decreased for heart failure and pneumonia, but not for acute myocardial infarction.

Background We sought to develop a claims-based definition of unscheduled care to describe the use and role of the emergency department (ED) in providing unscheduled care to vulnerable older adult populations. Methods This study was a cross-sectional analysis of national 20% sample of Medicare beneficiaries included in the 2012 Chronic Condition Warehouse data set. We measured three outcomes: the number of ED visits per 1,000 Medicare beneficiaries, the proportion of all unscheduled ED and office-based visits occurring in the ED and the number of ED and non-ED unscheduled visits adjusting for risk factors. Each outcome was estimated for vulnerable subpopulations of Medicare beneficiaries with multiple chronic conditions (MCCs), dual eligibility, hospice enrollment, and skilled nursing facility use. Results A total of 10,717,786 Medicare beneficiaries were included with 33,696,461 potentially unscheduled care visits of which 5,192,235 (15%) occurred in the ED, 364,334 (1.1%) in facility-based urgent care, and 31,570,113 (84%) in ambulatory office settings. In regression analyses each subpopulation was more likely to visit the ED for unscheduled care services than the reference population of Medicare beneficiaries ages 65 to 80. Dual-eligible beneficiaries demonstrated higher ED visit rates and lower non-ED visit rates for unscheduled care. The subpopulation with MCCs uses both the ED and the non-ED setting for unscheduled care more so than any other group. Conclusions Medicare beneficiaries, particularly vulnerable subpopulations, disproportionately visit the ED in comparison to physician offices for unscheduled care. Efforts to improve care coordination, measure quality, or reform payment to influence ED visitation should acknowledge these patterns and the unique availability of acute care services in the ED.

Background Current guidelines for small bowel obstruction (SBO) recommend a limited trial of nonoperative management of no more than 3-5 d. For patients requiring surgery, it is uncertain if sociodemographic factors are associated with disparities in the duration of the trial of nonoperative therapy. Methods The Healthcare Cost and Utilization Project National Inpatient Sample from 2012 to 2014 was queried for discharges with a primary diagnosis of SBO. Primary outcomes of interest were the effects of sociodemographic factors, including race, insurance status, and income on the rate of receiving any operative management for SBO, and subsequently, among patients managed surgically, the risk of operative delay, defined as operative management ≥ 5 d after admission. We did this by using logistic hierarchical generalized linear models, accounting for hospital clustering and adjusted for sex, age, comorbidity, and hospital factors. Results Of the 589,850 admissions for SBO between 2012 and 2014, 22.0% underwent operations. Overall, 26.2% were non-White, including 12.2% Black and 8.6% Hispanic patients, and the majority (56.0%) had Medicare insurance coverage. Income quartiles were evenly distributed across the overall study population. In adjusted logistic regression, operative delay was associated with increased odds of in-hospital mortality (odds ratio 1.30 95% confidence interval [1.10, 1.54]). Adjusted for patient and hospital factors, Black patients were significantly more likely to receive operations for SBO, whereas Medicaid and Medicare patients were significantly less likely. However, Black, Medicaid, and Medicare patients who were managed operatively were significantly more likely to have an operative delay of 5 or more d. There was no significant association between income and operative management in adjusted regression models. Conclusions Significant disparities in the operative management were based on race and insurance status. Further research is warranted to understand the causes of, and solutions to, these sociodemographic disparities in care.

Characterization of the dynamic nature of posthospital risk in chronic obstructive pulmonary disease (COPD) is needed to provide counseling and plan clinical services.To analyze risk of readmission and death after discharge for COPD among Medicare beneficiaries aged 65 years and older and to determine the association between ventilator support and risk trajectory.We computed daily absolute risks of hospital readmission and death for 1 year after discharge for COPD, stratified by ventilator support. We determined the time required for risks to decline by 50% from maximum daily values after discharge and for daily risks to plateau. We compared risks with those found in the general elderly population.Among 2,340,637 hospitalizations, the readmission rate at 1 year was 64.2%, including 63.5%, 66.0%, and 64.1% among those receiving invasive, noninvasive, and no ventilation, respectively. Among 1,283,069 hospitalizations, mortality at 1 year was 26.2%, including 45.7%, 41.8%, and 24.4% among those same respective groups. Daily risk of readmission declined by 50% within 28, 39, and 43 days and plateaued at 46, 54, and 61 days among those receiving invasive, noninvasive, and no ventilation, respectively. Risk of death declined by 50% by 3, 4, and 17 days and plateaued by 21, 18, and 24 days in the same respective groups. Risks of hospitalization and death were significantly higher after discharge for COPD than among the general Medicare population.Discharge from the hospital is associated with prolonged risks of readmission and death that vary with need for ventilator support. Interventions limited to the first month after discharge may be insufficient to improve longitudinal outcomes.

Background Whether the volume of coronavirus disease 2019 (COVID-19) hospitalizations is associated with outcomes has important implications for the organization of hospital care both during this pandemic and future novel and rapidly evolving high-volume conditions. Methods We identified COVID-19 hospitalizations at US hospitals in the American Heart Association COVID-19 Cardiovascular Disease Registry with ≥10 cases between January and August 2020. We evaluated the association of COVID-19 hospitalization volume and weekly case growth indexed to hospital bed capacity, with hospital risk-standardized in-hospital case-fatality rate (rsCFR). Results There were 85 hospitals with 15,329 COVID-19 hospitalizations, with a median hospital case volume was 118 (interquartile range, 57, 252) and median growth rate of 2 cases per 100 beds per week but varied widely (interquartile range: 0.9 to 4.5). There was no significant association between overall hospital COVID-19 case volume and rsCFR (rho, 0.18, P = .09). However, hospitals with more rapid COVID-19 case-growth had higher rsCFR (rho, 0.22, P = 0.047), increasing across case growth quartiles (P trend = .03). Although there were no differences in medical treatments or intensive care unit therapies (mechanical ventilation, vasopressors), the highest case growth quartile had 4-fold higher odds of above median rsCFR, compared with the lowest quartile (odds ratio, 4.00; 1.15 to 13.8, P = .03). Conclusions An accelerated case growth trajectory is a marker of hospitals at risk of poor COVID-19 outcomes, identifying sites that may be targets for influx of additional resources or triage strategies. Early identification of such hospital signatures is essential as our health system prepares for future health challenges.

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.

This comparative effectiveness research study assesses whether using present on admission codes and single, rather than grouped, diagnostic codes can enhance Centers for Medicare & Medicaid (CMS) models to predict payment for hospitalization for acute myocardial infarction, heart failure, and pneumonia., Key Points Question Does leveraging present on admission codes and using single, rather than grouped, diagnostic codes enhance risk models for acute myocardial infarction, heart failure, and pneumonia payment measures? Findings In this comparative effectiveness research study of risk models on 1 667 983 patients with 1 943 049 Medicare fee-for-service hospitalizations, use of present on admission codes and single diagnosis codes and separation of index admission codes from codes in the previous year improved models predicting payment that were compared with models based on Centers for Medicare & Medicaid Services grouped codes. The patient-level pseudo R2 improved from 0.077 to 0.129 for acute myocardial infarction, from 0.042 to 0.129 for heart failure, and from 0.114 to 0.237 for pneumonia. Meaning Changing candidate variables from the current standard improved models predicting payments, which has implications for research, benchmarking, public reporting, and calculations for population-based programs., Importance Predicting payments for particular conditions or populations is essential for research, benchmarking, public reporting, and calculations for population-based programs. Centers for Medicare & Medicaid Services (CMS) models often group codes into disease categories, but using single, rather than grouped, diagnostic codes and leveraging present on admission (POA) codes may enhance these models. Objective To determine whether changes to the candidate variables in CMS models would improve risk models predicting patient total payment within 30 days of hospitalization for acute myocardial infarction (AMI), heart failure (HF), and pneumonia. Design, Setting, and Participants This comparative effectiveness research study used data from Medicare fee-for-service hospitalizations for AMI, HF, and pneumonia at acute care hospitals from July 1, 2013, through September 30, 2015. Payments across multiple care settings, services, and supplies were included and adjusted for geographic and policy variations, corrected for inflation, and winsorized. The same data source was used but varied for the candidate variables and their selection, and the method used by CMS for public reporting that used grouped codes was compared with variations that used POA codes and single diagnostic codes. Combinations of use of POA codes, separation of index admission diagnoses from those in the previous 12 months, and use of individual International Classification of Diseases, Ninth Revision, Clinical Modification codes instead of grouped diagnostic categories were tested. Data analysis was performed from December 4, 2017, to June 10, 2019. Main Outcomes and Measures The models’ goodness of fit was compared using root mean square error (RMSE) and the McFadden pseudo R2. Results Among the 1 943 049 total hospitalizations of the study participants, 343 116 admissions were for AMI (52.5% male; 37.4% aged ≤74 years), 677 044 for HF (45.5% male; 25.9% aged ≤74 years), and 922 889 for pneumonia (46.4% male; 28.2% aged ≤74 years). The mean (SD) 30-day payment was $23 103 ($18 221) for AMI, $16 365 ($12 527) for HF, and $17 097 ($12 087) for pneumonia. Each incremental model change improved the pseudo R2 and RMSE. Incorporating all 3 changes improved the pseudo R2 of the patient-level models from 0.077 to 0.129 for AMI, from 0.042 to 0.129 for HF, and from 0.114 to 0.237 for pneumonia. Parallel improvements in RMSE were found for all 3 conditions. Conclusions and Relevance Leveraging POA codes, separating index from previous diagnoses, and using single diagnostic codes improved payment models. Better models can potentially improve research, benchmarking, public reporting, and calculations for population-based programs.

Objective To propose and evaluate a metric for quantifying hospital-specific disparities in health outcomes that can be used by patients and hospitals. Data sources/study setting Inpatient admissions for Medicare patients with acute myocardial infarction, heart failure, or pneumonia to all non-federal, short-term, acute care hospitals during 2012-2015. Study design Building on the current Centers for Medicare and Medicaid Services methodology for calculating risk-standardized readmission rates, we developed models that include a hospital-specific random coefficient for either patient dual eligibility status or African American race. These coefficients quantify the difference in risk-standardized outcomes by dual eligibility and race at a given hospital after accounting for the hospital's patient case mix and proportion of dual eligible or African American patients. We demonstrate this approach and report variation and performance in hospital-specific disparities. Principal findings Dual eligibility and African American race were associated with higher readmission rates within hospitals for all three conditions. However, this disparity effect varied substantially across hospitals. Conclusion Our models isolate a hospital-specific disparity effect and demonstrate variation in quality of care for different groups of patients across conditions and hospitals. Illuminating within-hospital disparities can incentivize hospitals to reduce inequities in health care quality.

Key Points Question Was the announcement or implementation of the Hospital Readmissions Reduction Program (HRRP) associated with an increase in mortality following hospitalization for acute myocardial infarction, heart failure, or pneumonia among Medicare beneficiaries? Findings In this cohort study, between 2006 and 2014, in-hospital mortality decreased for the 3 conditions while 30-day postdischarge mortality decreased for acute myocardial infarction but increased for heart failure and pneumonia. Before the announcement of the HRRP, postdischarge mortality was stable for acute myocardial infarction and increasing for heart failure and pneumonia, and there were no inflections in slope around the announcement or implementation of the HRRP. Meaning There was no evidence for increase in in-hospital or postdischarge mortality associated with the HRRP announcement or implementation—a period with substantial reductions in readmissions., Importance The US Hospital Readmissions Reduction Program (HRRP) was associated with reduced readmissions among Medicare beneficiaries hospitalized for acute myocardial infarction (AMI), heart failure (HF), and pneumonia. It is important to assess whether there has been a signal for concomitant harm with an increase in mortality. Objective To evaluate whether the announcement or the implementation of HRRP was associated with an increase in either in-hospital or 30-day postdischarge mortality following hospitalization for AMI, HF, or pneumonia. Design, Setting, and Participants In this cohort study, using Medicare data, all hospitalizations for AMI, HF, and pneumonia were identified among fee-for-service Medicare beneficiaries aged 65 years and older from January 1, 2006, to December 31, 2014. These were assessed for changes in trends for risk-adjusted rates of in-hospital and 30-day postdischarge mortality after announcement and implementation of the HRRP using an interrupted time series framework. Analyses were done in November 2017 and December 2017. Exposures Announcement of the HRRP in March 2010, and implementation of its penalties in October 2012. Main Outcomes and Measures Monthly risk-adjusted rates of in-hospital and 30-day postdischarge mortality. Results The sample included 1.7 million AMI, 4 million HF, and 3.5 million pneumonia hospitalizations. Between 2006 and 2014, in-hospital mortality decreased for the 3 conditions (AMI, from 10.4% to 9.7%; HF, from 4.3% to 3.5%; pneumonia, from 5.3% to 4.0%) while 30-day postdischarge mortality decreased from 7.4% to 7.0% for AMI (P for trend .05 for all). In contrast, there were significant negative deflections in slopes for readmission rates at HRRP announcement for all conditions. Conclusions and Relevance Among Medicare beneficiaries, there was no evidence for an increase in in-hospital or postdischarge mortality associated with HRRP announcement or implementation—a period with substantial reductions in readmissions. The improvement in readmission was therefore not associated with any increase in in-hospital or 30-day postdischarge mortality., This cohort study uses Medicare data to evaluate whether the announcement or implementation of the Hospital Readmissions Reduction Program (HRRP) was associated with an increase in either in-hospital or 30-day postdischarge mortality following hospitalization for acute myocardial infarction (AMI), heart failure (HF), or pneumonia.

Faust et al discuss the study which compares all-cause excess mortality and COVID-19-related mortality during the early pandemic period (March-July 2020) with unintentional drug overdoses, the usual leading cause of death in young adults, during the same period in 2018 among adults aged 25 to 44 years COVID-19 has caused a marked increase in all-cause deaths in the US, mostly among older adults Although the burden of COVID-19 among hospitalized younger adults has been described, fewer data focus on mortality in this demographic, owing to lower case-fatality rates

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.

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.

This cohort study analyzes Centers for Medicare & Medicaid Services data for heart failure and pneumonia discharges to ascertain hospital payment profiles associated with hospitalization costs for Medicare beneficiaries., Key Points Question Is the amount that the Centers for Medicare & Medicaid Services pays hospitals associated with characteristics of the hospital independent of non–time-varying characteristics of patients? Findings In this cohort study of 1615 Medicare beneficiaries hospitalized with heart failure and 708 beneficiaries hospitalized with pneumonia, the same patients admitted with the same diagnosis to hospitals in the highest Medicare payment quartile had higher costs than when they were admitted to hospitals in the lowest quartile. Meaning Findings from this study suggest that hospitals operate at different cost levels independent of their patients., Importance Some uncertainty exists about whether hospital variations in cost are largely associated with differences in case mix. Objective To establish whether the same patients admitted with the same diagnosis (heart failure or pneumonia) at 2 different hospitals incur different costs associated with the hospital’s Medicare payment profile. Design, Setting, and Participants This observational cohort study used Centers for Medicare & Medicaid Services (CMS) discharge data of patients with a principal diagnosis of heart failure (n = 1615) or pneumonia (n = 708) occurring between July 1, 2013, and June 30, 2016. Patients were individuals aged 65 years or older who were enrolled in Medicare fee-for-service Part A and Part B and were discharged from nonfederal, short-term, acute care or critical access hospitals in the United States. Data were analyzed from March 16, 2018, to September 25, 2019. Main Outcomes and Measures The CMS heart failure and pneumonia payment measure cohorts were divided into 2 random samples. In the first sample, hospitals were classified into payment quartiles for heart failure and pneumonia. In the second sample, patients with 2 admissions for heart failure or pneumonia, one in a lowest-quartile hospital and one in a highest-quartile hospital more than 1 month apart, were identified. Standardized Medicare payments for these patients were compared for the lowest- and the highest-quartile payment hospitals. Results The study sample included 1615 patients with heart failure (mean [SD] age, 78.7 [8.0] years; 819 [50.7%] male) and 708 with pneumonia (mean [SD] age, 78.3 [8.0] years; 401 [56.6%] male). The observed 30-day mortality rates for patients among lowest- compared with highest-payment hospitals were not significantly different. The median (interquartile range) hospital 30-day risk-standardized mortality rates were 8.1% (7.7%-8.5%) for heart failure and 11.3% (10.7%-12.1%) for pneumonia. The 30-day episode payment for hospitalization for the same patients at the lowest-payment hospitals was $2118 (95% CI, $1168-$3068; P

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

Readmission rates after hospitalizations for heart failure (HF), acute myocardial infarction (AMI), and pneumonia among Medicare beneficiaries are used to assess quality and determine reimbursement. Whether these measures reflect readmission rates for other conditions or insurance groups is unknown.To investigate whether hospital-level 30-day readmission measures for publicly reported conditions (HF, AMI, and pneumonia) among Medicare patients reflect those for Medicare patients hospitalized for unreported conditions or non-Medicare patients hospitalized with HF, AMI, or pneumonia.Cross-sectional.Population-based.Hospitals in the all-payer Nationwide Readmissions Database in 2013 and 2014.Hospital-level 30-day all-cause risk-standardized excess readmission ratios (ERRs) were compared for 3 groups of patients: Medicare beneficiaries admitted for HF, AMI, or pneumonia (Medicare reported group); Medicare beneficiaries admitted for other conditions (Medicare unreported group); and non-Medicare beneficiaries admitted for HF, AMI, or pneumonia (non-Medicare group).Within-hospital differences in ERRs varied widely among groups. Medicare reported ratios differed from Medicare unreported ratios by more than 0.1 for 29% of hospitals and from non-Medicare ratios by more than 0.1 for 46% of hospitals. Among hospitals with higher readmission ratios, ERRs for the Medicare reported group tended to overestimate ERRs for the non-Medicare group but underestimate those for the Medicare unreported group.Medicare groups and risk adjustment differed slightly from those used by the Centers for MedicareMedicaid Services.Hospital ERRs, as estimated by Medicare to determine financial penalties, have poor agreement with corresponding measures for populations and conditions not tied to financial penalties. Current publicly reported measures may not be good surrogates for overall hospital quality related to 30-day readmissions.Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology.

Background Whether types of hospitals with high readmission rates also have high overall postdischarge acute care utilization (including emergency department and observation care) is unknown. Design Cross-sectional analysis. Subjects Nonfederal United States acute care hospitals. Measures Using methodology established by the Centers for Medicare & Medicaid Services, we calculated each hospital's "excess days in acute care" for fee-for-service (FFS) Medicare beneficiaries aged over 65 years discharged after hospitalization for acute myocardial infarction, heart failure (HF), or pneumonia, representing the mean difference between predicted and expected total days of acute care utilization in the 30 days following hospital discharge, per 100 discharges. We assessed the multivariable association of 8 hospital characteristics with excess days in acute care and the proportion of hospitals with each characteristic that were statistical outliers (95% credible interval estimate does not include 0). Results We included 2184 hospitals for acute myocardial infarction [228 (10.4%) better than expected, 549 (25.1%) worse than expected], 3720 hospitals for HF [484 (13.0%) better and 840 (22.6%) worse], and 4195 hospitals for pneumonia [673 (16.0%) better, 1005 (24.0%) worse]. Results for all conditions were similar. Worse than expected outliers for pneumonia included: 18.8% of safety net hospitals versus 26.1% of nonsafety net hospitals; 16.7% of public hospitals versus 33.1% of for-profit hospitals; 19.5% of nonteaching hospitals versus 52.2% of major teaching hospitals; 7.9% of rural hospitals versus 42.1% of large urban hospitals; 5.9% of hospitals with 24- 500 beds; and 29.0% of hospitals with nurse-to-bed ratios >1.0-1.5 versus 21.7% of hospitals with ratios >2.0. Conclusions Including emergency department and observation stays in measures of postdischarge utilization produces similar results as measuring only readmissions in that major teaching, urban and for-profit hospitals still perform disproportionately poorly versus nonteaching or public hospitals. However, it enables identification of more outliers and a more granular assessment of the association of hospital factors and outcomes.

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.

Objective To compare trends in readmission rates among safety net and non-safety net hospitals under the US Hospital Readmission Reduction Program (HRRP). Design A retrospective time series analysis using Medicare administrative claims data from January 2008 to June 2015. Setting We examined 3254 US hospitals eligible for penalties under the HRRP, categorised as safety net or non-safety net hospitals based on the hospital’s proportion of patients with low socioeconomic status. Participants Admissions for Medicare fee-for-service patients, age ≥65 years, discharged alive, who had a valid five-digit zip code and did not have a principal discharge diagnosis of cancer or psychiatric illness were included, for a total of 52 516 213 index admissions. Primary and secondary outcome measures Mean hospital-level, all-condition, 30-day risk-adjusted standardised unplanned readmission rate, measured quarterly, along with quarterly rate of change, and an interrupted time series examining: April–June 2010, after HRRP was passed, and October–December 2012, after HRRP penalties were implemented. Results 58.0% (SD 15.3) of safety net hospitals and 17.1% (SD 10.4) of non-safety net hospitals’ patients were in the lowest quartile of socioeconomic status. The mean safety net hospital standardised readmission rate declined from 17.0% (SD 3.7) to 13.6% (SD 3.6), whereas the mean non-safety net hospital declined from 15.4% (SD 3.0) to 12.7% (SD 2.5). The absolute difference in rates between safety net and non-safety net hospitals declined from 1.6% (95% CI 1.3 to 1.9) to 0.9% (0.7 to 1.2). The quarterly decline in standardised readmission rates was 0.03 percentage points (95% CI 0.03 to 0.02, p

Objective To characterize rates and trends over time of emergency department treatment-and-discharge stays, repeat observation stays, inpatient stays, any hospital revisit, and death within 30 days of discharge from observation stays. Design Retrospective cohort study. Setting 4750 hospitals in the USA. Participants Nationally representative sample of Medicare fee for service beneficiaries aged 65 or over discharged after 363 037 index observation stays, 2 540 000 index emergency department treatment-and-discharge stays, and 2 667 525 index inpatient stays from 2006-11. Main outcome measures Rates of emergency department treatment-and-discharge stays, observation stays, inpatient stays, any hospital revisit, and death within 30 days of discharge from index observation stays. Rates were compared with corresponding outcomes within 30 days of discharge from both index emergency department treatment-and-discharge stays and index inpatient stays. Results Among 363 037 index observation stays resulting in discharge from 2006-11, 30 day rates of emergency department treatment-and-discharge stays were 8.4%, repeat observation stays were 2.9%, inpatient stays were 11.2%, any hospital revisit was 20.1%, and death was 1.8%. Of all revisits, 49.7% were for inpatient stays. Revisit rates for emergency department treatment-and-discharge stays, repeat observation stays, and any hospital revisit increased from 2006-11 (P

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.

This comparative effectiveness study examines current US Centers for Medicare & Medicaid Services 30-day mortality risk models vs novel risk models for acute myocardial infarction, heart failure, and pneumonia hospitalizations., Key Points Question Could present on admission indicators and ungrouped diagnostic codes enhance risk models for acute myocardial infarction, heart failure, and pneumonia mortality measures and improve discrimination of hospital-level performance? Findings In this comparative effectiveness study including all Medicare fee-for-service beneficiaries hospitalized for acute myocardial infarction, heart failure, or pneumonia at acute care hospitals, incorporating present on admission coding and ungrouped historical and index admission International Classification of Diseases, Ninth Revision, Clinical Modification codes was associated with greater discrimination in patient-level and hospital-level 30-day mortality risk models. Meaning Changes incurring no additional cost could enhance the risk adjustment for mortality and increase discrimination of hospital-level performance., Importance Risk adjustment models using claims-based data are central in evaluating health care performance. Although US Centers for Medicare & Medicaid Services (CMS) models apply well-vetted statistical approaches, recent changes in the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) coding system and advances in computational capabilities may provide an opportunity for enhancement. Objective To examine whether changes using already available data would enhance risk models and yield greater discrimination in hospital-level performance measures. Design, Setting, and Participants This comparative effectiveness study used ICD-9-CM codes from all Medicare fee-for-service beneficiary claims for hospitalizations for acute myocardial infarction (AMI), heart failure (HF), or pneumonia among patients 65 years and older from July 1, 2013, through September 30, 2015. Changes to current CMS mortality risk models were applied incrementally to patient-level models, and the best model was tested on hospital performance measures to model 30-day mortality. Analyses were conducted from April 19, 2018, to September 19, 2018. Main Outcomes and Measures The main outcome was all-cause death within 30 days of hospitalization for AMI, HF, or pneumonia, examined using 3 changes to current CMS mortality risk models: (1) incorporating present on admission coding to better exclude potential complications of care, (2) separating index admission diagnoses from those of the 12-month history, and (3) using ungrouped ICD-9-CM codes. Results There were 361 175 hospital admissions (mean [SD] age, 78.6 [8.4] years; 189 225 [52.4%] men) for AMI, 716 790 hospital admissions (mean [SD] age, 81.1 [8.4] years; 326 825 [45.6%] men) for HF, and 988 225 hospital admissions (mean [SD] age, 80.7 [8.6] years; 460 761 [46.6%] men) for pneumonia during the study; mean 30-day mortality rates were 13.8% for AMI, 12.1% for HF, and 16.1% for pneumonia. Each change to the models was associated with incremental gains in C statistics. The best model, incorporating all changes, was associated with significantly improved patient-level C statistics, from 0.720 to 0.826 for AMI, 0.685 to 0.776 for HF, and 0.715 to 0.804 for pneumonia. Compared with current CMS models, the best model produced wider predicted probabilities with better calibration and Brier scores. Hospital risk-standardized mortality rates had wider distributions, with more hospitals identified as good or bad performance outliers. Conclusions and Relevance Incorporating present on admission coding and using ungrouped index and historical ICD-9-CM codes were associated with improved patient-level and hospital-level risk models for mortality compared with the current CMS models for all 3 conditions.

Importance Payers and policy makers have advocated for transitioning toward value-based payment models. However, little is known about what is the extent of hospital variation in the value of care and whether there are any hospital characteristics associated with high-value care. Objectives To investigate the association between hospital-level 30-day risk-standardized mortality rates (RSMRs) and 30-day risk-standardized payments (RSPs) for acute myocardial infarction (AMI), heart failure (HF), and pneumonia (PNA); to characterize patterns of value in care; and to identify hospital characteristics associated with high-value care (defined by having lower than median RSMRs and RSPs). Design, Setting, and Participants This national cross-sectional study applied weighted linear correlation to investigate the association between hospital RSMRs and RSPs for AMI, HF, and PNA between July 1, 2011, and June 30, 2014, among all hospitals; examined correlations in subgroups of hospitals based on key characteristics; and assessed the proportion and characteristics of hospitals delivering high-value care. The data analysis was completed in October 2017. The setting was acute care hospitals. Participants were Medicare fee-for-service beneficiaries discharged with AMI, HF, or PNA. Main Outcomes and Measures Hospital-level 30-day RSMRs and RSPs for AMI, HF, and PNA. Results The AMI sample consisted of 4339 hospitals with 487 141 hospitalizations for mortality and 462 905 hospitalizations for payment. The HF sample included 4641 hospitals with 960 960 hospitalizations for mortality and 903 721 hospitalizations for payment. The PNA sample contained 4685 hospitals with 952 022 hospitalizations for mortality and 901 764 hospitalizations for payment. The median (interquartile range [IQR]) RSMRs and RSPs, respectively, was 14.3% (IQR, 13.8%-14.8%) and $21 620 (IQR, $20 966-$22 567) for AMI, 11.7% (IQR, 11.0%-12.5%) and $15 139 (IQR, $14 310-$16 118) for HF, and 11.5% (IQR, 10.6%-12.6%) and $14 220 (IQR, $13 342-$15 097) for PNA. There were statistically significant but weak inverse correlations between the RSMRs and RSPs of −0.08 (95% CI, −0.11 to −0.05) for AMI, −0.21 (95% CI, −0.24 to −0.18) for HF, and −0.07 (95% CI, −0.09 to −0.04) for PNA. The largest shared variance between the RSMRs and RSPs was only 4.4% (for HF). The correlations between the RSMRs and RSPs did not differ significantly across teaching status, safety-net status, urban/rural status, or the proportion of patients with low socioeconomic status. Approximately 1 in 4 hospitals (20.9% for AMI, 23.0% for HF, and 23.9% for PNA) had both lower than median RSMRs and RSPs. Conclusions and Relevance These findings suggest that there is significant potential for improvement in the value of AMI, HF, and PNA care and also suggest that high-value care for these conditions is attainable across most hospital types.

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.

Key Points Question What is the source of known disparities in outcomes after hospitalization according to patients’ race and socioeconomic status? Findings This cohort study found differences in outcomes by race and neighborhood income, but hospital performance within and between hospitals by patient race and neighborhood income was generally consistent. Meaning There was no evidence that specific hospitals that treated patients from a range of racial and socioeconomic backgrounds contributed to observed disparities after hospitalization, suggesting a systemic effect may be contributory., This cohort study uses Medicare claims data to compare performance within and between US hospitals concerning the association of race and neighborhood income with patient outcomes after hospitalization for acute myocardial infarction (AMI), heart failure, and pneumonia., Importance Although studies have described differences in hospital outcomes by patient race and socioeconomic status, it is not clear whether such disparities are driven by hospitals themselves or by broader systemic effects. Objective To determine patterns of racial and socioeconomic disparities in outcomes within and between hospitals for patients with acute myocardial infarction, heart failure, and pneumonia. Design, Setting, and Participants Retrospective cohort study initiated before February 2013, with additional analyses conducted during the peer-review process. Hospitals in the United States treating at least 25 Medicare fee-for-service beneficiaries aged 65 years or older in each race (ie, black and white) and neighborhood income level (ie, higher income and lower income) for acute myocardial infarction, heart failure, and pneumonia between 2009 and 2011 were included. Main Outcomes and Measures For within-hospital analyses, risk-standardized mortality rates and risk-standardized readmission rates for race and neighborhood income subgroups were calculated at each hospital. The corresponding ratios using intraclass correlation coefficients were then compared. For between-hospital analyses, risk-standardized rates were assessed according to hospitals’ proportion of patients in each subgroup. These analyses were performed for each of the 12 analysis cohorts reflecting the unique combinations of outcomes (mortality and readmission), demographics (race and neighborhood income), and conditions (acute myocardial infarction, heart failure, and pneumonia). Results Between 74% (3545 of 4810) and 91% (4136 of 4554) of US hospitals lacked sufficient racial and socioeconomic diversity to be included in this analysis, with the number of hospitals eligible for analysis varying among cohorts. The 12 analysis cohorts ranged in size from 418 to 1265 hospitals and from 144 417 to 703 324 patients. Within included hospitals, risk-standardized mortality rates tended to be lower among black patients (mean [SD] difference between risk-standardized mortality rates in black patients compared with white patients for acute myocardial infarction, −0.57 [1.1] [P = .47]; for heart failure, −4.7 [1.3] [P

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 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 characterize the absolute risks for older patients of readmission to hospital and death in the year after hospitalization for heart failure, acute myocardial infarction, or pneumonia. Design Retrospective cohort study. Setting 4767 hospitals caring for Medicare fee for service beneficiaries in the United States, 2008-10. Participants More than 3 million Medicare fee for service beneficiaries, aged 65 years or more, surviving hospitalization for heart failure, acute myocardial infarction, or pneumonia. Main outcome measures Daily absolute risks of first readmission to hospital and death for one year after discharge. To illustrate risk trajectories, we identified the time required for risks of readmission to hospital and death to decline 50% from maximum values after discharge; the time required for risks to approach plateau periods of minimal day to day change, defined as 95% reductions in daily changes in risk from maximum daily declines after discharge; and the extent to which risks are higher among patients recently discharged from hospital compared with the general elderly population. Results Within one year of hospital discharge, readmission to hospital and death, respectively, occurred following 67.4% and 35.8% of hospitalizations for heart failure, 49.9% and 25.1% for acute myocardial infarction, and 55.6% and 31.1% for pneumonia. Risk of first readmission had declined 50% by day 38 after hospitalization for heart failure, day 13 after hospitalization for acute myocardial infarction, and day 25 after hospitalization for pneumonia; risk of death declined 50% by day 11, 6, and 10, respectively. Daily change in risk of first readmission to hospital declined 95% by day 45, 38, and 45; daily change in risk of death declined 95% by day 21, 19, and 21. After hospitalization for heart failure, acute myocardial infarction, or pneumonia, the magnitude of the relative risk for hospital admission over the first 90 days was 8, 6, and 6 times greater than that of the general older population; the relative risk of death was 11, 8, and 10 times greater. Conclusions Risk declines slowly for older patients after hospitalization for heart failure, acute myocardial infarction, or pneumonia and is increased for months. Specific risk trajectories vary by discharge diagnosis and outcome. Patients should remain vigilant for deterioration in health for an extended time after discharge. Health providers can use knowledge of absolute risks and their changes over time to better align interventions designed to reduce adverse outcomes after discharge with the highest risk periods for patients.

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.