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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.

ImportanceThe coronavirus disease 2019 (COVID-19) outbreak has been associated with decreases in acute myocardial infarction diagnoses (AMI) and admissions in the United States. Whether this affected heart disease deaths is unknown.ObjectiveTo determine whether changes in heart disease deaths occurred during the early pandemic period in the US, we analyzed areas without large COVID-19 outbreaks. This isolated the effect of decreased healthcare-seeking behavior during the early outbreak.Design, Setting, and ParticipantsWe performed an observational study of heart disease-specific mortality using National Center for Health Statistics data (NCHS). Weekly provisional counts were disaggregated by jurisdiction of occurrence during 2019 and 2020 for all-cause deaths, COVID-19 deaths, and heart disease deaths. For the primary analysis, jurisdictions were included if; 1) There was no all-cause excess mortality during the early pandemic period (weeks 14–17, 2020); 2) The completeness of that data was estimated by NCHS to be >97% as of July 22, 2020, and; 3) Decreases in emergency department (ED) visits occurred during the study period. We compared heart disease death rates during the early pandemic period with corresponding weeks in 2019 and a pre-pandemic control period of 2020 as a sensitivity analysis. Incident rate and rate ratios were calculated.ExposureThe US COVID-19 outbreak.Main Outcomes and MeasuresIncidence of heart disease deaths.ResultsTwelve states met the primary inclusion criteria, capturing 747,375,188 person-weeks for the early pandemic period and 740,987,984 person-weeks for the 2019 control period. The mean incidence rate (per 100,000 person-weeks) for heart disease in states without excess deaths during the early pandemic period was 3.95 (95% CI 3.83 to 4.06) versus 4.19 (95% CI 4.14 to 4.23) during the corresponding period in 2019. The incident rate ratio (2020/2019) was 0.91 (95% CI 0.87 to 0.97). No state recorded an increase from either the corresponding period in 2019 or the 2020 prepandemic control period. Two states recorded fewer heart disease deaths.Conclusions and RelevanceThis observational study found a decrease in heart disease deaths during the early US outbreak in regions without significant COVID-19 burdens, despite decreases in ED utilization. Long term follow-up data are needed.

During the 1918 H1N1 influenza pandemic, there were approximately 50 million influenza-related deaths worldwide, including 675 000 in the US. Few persons in the US have a frame of reference for the historic levels of excess mortality currently being observed during the coronavirus disease 2019 (COVID-19) pandemic.1 In this study, excess deaths in New York City during the peak of the 1918 H1N1 influenza pandemic were compared with those during the initial period of the COVID-19 outbreak.

Importance: A seroprevalence study can estimate the percentage of people with SARS-CoV-2 antibodies in the general population. Most existing reports have used a convenience sample, which may bias their estimates. Objective: To estimate the seroprevalence of antibodies against SARS-CoV-2 based on a random sample of adults living in Connecticut between March 1 and June 1, 2020. Design: Cross-sectional. Setting: We sought a representative sample of Connecticut residents who completed a survey between June 4 and June 23, 2020 and underwent serology testing for SARS-CoV-2-specific IgG antibodies between June 10 and July 6, 2020. Participants: 505 respondents, aged ≥18 years, residing in non-congregate settings who completed both the survey and the serology test. Main outcomes and measures: We estimated the seroprevalence of SARS-CoV-2-specific IgG antibodies among the overall population and across pre-specified subgroups. We also assessed the prevalence of symptomatic illness, risk factors for virus exposure, and self-reported adherence to risk mitigation behaviors among this population. Results: Of the 505 respondents (mean age 50 [±17] years; 54% women; 76% non-Hispanic White individuals) included, 32% reported having at least 1 symptom suggestive of COVID-19 since March 1, 2020. Overall, 18 respondents had SARS-CoV-2-specific antibodies, resulting in the state-level weighted seroprevalence of 3.1 (90% CI 1.4-4.8). Individuals who were asymptomatic had significantly lower seroprevalence (0.6% [90% CI 0.0-1.5]) compared with the overall state estimate, while those who reported having had ≥1 and ≥2 symptoms had a seroprevalence of 8.0% (90% CI 3.1-12.9) and 13.0% (90% CI 3.5-22.5), respectively. All 9 of the respondents who reported previously having a positive coronavirus test were positive for SARS-CoV-2-specific IgG antibodies. Nearly two-third of respondents reported having avoided public places (74%) and small gatherings of family or friends (75%), and 97% reported wearing a mask outside their home, at least part of the time. Conclusions and relevance: These estimates indicate that most people in Connecticut do not have detectable levels of antibodies against SARS-CoV-2. There is a need for continued adherence to risk mitigation behaviors among Connecticut residents, to prevent resurgence of COVID-19 in this region.

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.

The SARS-CoV-2 pandemic is associated with a reduction in hospitalization for an acute cardiovascular conditions. In a major health system in Massachusetts, there was a 43% reduction in these types of hospitalizations in March 2020 compared with March 2019.4 Whether mortality rates from heart disease have changed over this period is unknown.We assembled information from the National Center for Health Statistics (Centers for Disease Control and Prevention) for 118,356,533 person-weeks from Week 1 (ending January 4) through Week 17 (ending April 25) of 2020 for the state of Massachusetts. We found that heart disease deaths are unchanged during the Covid-19 pandemic period as compared to the corresponding period of 2019. This is despite reports that admissions for acute myocardial infarction have fallen during this time.

ObjectiveTo develop a nationally applicable tool for assessing the quality of informed consent documents for elective procedures.DesignMixed qualitative-quantitative approach.SettingConvened seven meetings with stakeholders to obtain input and feedback on the tool.ParticipantsTeam of physician investigators, measure development experts, and a working group of nine patients and patient advocates (caregivers, advocates for vulnerable populations and patient safety experts) from different regions of the country.InterventionsWith stakeholder input, we identified elements of high-quality informed consent documents, aggregated into three domains: content, presentation and timing. Based on this comprehensive taxonomy of key elements, we convened the working group to offer input on the development of an abstraction tool to assess the quality of informed consent documents in three phases: (1) selecting the highest-priority elements to be operationalised as items in the tool; (2) iteratively refining and testing the tool using a sample of qualifying informed consent documents from eight hospitals; and (3) developing a scoring approach for the tool. Finally, we tested the reliability of the tool in a subsample of 250 informed consent documents from 25 additional hospitals.OutcomesAbstraction tool to evaluate the quality of informed consent documents.ResultsWe identified 53 elements of informed consent quality; of these, 15 were selected as highest priority for inclusion in the abstraction tool and 8 were feasible to measure. After seven cycles of iterative development and testing of survey items, and development and refinement of a training manual, two trained raters achieved high item-level agreement, ranging from 92% to 100%.ConclusionsWe identified key quality elements of an informed consent document and operationalised the highest-priority elements to define a minimum standard for informed consent documents. This tool is a starting point that can enable hospitals and other providers to evaluate and improve the quality of informed consent.

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.

Background: Whether angiotensin-converting enzyme (ACE) Inhibitors and angiotensin receptor blockers (ARBs) mitigate or exacerbate SARS-CoV-2 infection remains uncertain. In a national study, we evaluated the association of ACE inhibitors and ARB with coronavirus disease-19 (COVID-19) hospitalization and mortality among individuals with hypertension. Methods: Among Medicare Advantage and commercially insured individuals, we identified 2,263 people with hypertension, receiving ≥1 antihypertensive agents, and who had a positive outpatient SARS-CoV-2 test (outpatient cohort). In a propensity score-matched analysis, we determined the association of ACE inhibitors and ARBs with the risk of hospitalization for COVID-19. In a second study of 7,933 individuals with hypertension who were hospitalized with COVID-19 (inpatient cohort), we tested the association of these medications with in-hospital mortality. We stratified all our assessments by insurance groups. Results: Among individuals in the outpatient and inpatient cohorts, 31.9% and 29.8%, respectively, used ACE inhibitors and 32.3% and 28.1% used ARBs. In the outpatient study, over a median 30.0 (19.0 - 40.0) days after testing positive, 12.7% were hospitalized for COVID-19. In propensity score-matched analyses, neither ACE inhibitors (HR, 0.77 [0.53, 1.13], P = 0.18), nor ARBs (HR, 0.88 [0.61, 1.26], P = 0.48), were significantly associated with risk of hospitalization. In analyses stratified by insurance group, ACE inhibitors, but not ARBs, were associated with a significant lower risk of hospitalization in the Medicare group (HR, 0.61 [0.41, 0.93], P = 0.02), but not the commercially insured group (HR: 2.14 [0.82, 5.60], P = 0.12; P-interaction 0.09). In the inpatient study, 14.2% died, 59.5% survived to discharge, and 26.3% had an ongoing hospitalization. In propensity score-matched analyses, neither use of ACE inhibitor (0.97 [0.81, 1.16]; P = 0.74) nor ARB (1.15 [0.95, 1.38]; P = 0.15) was associated with risk of in-hospital mortality, in total or in the stratified analyses. Conclusions: The use of ACE inhibitors and ARBs was not associated with the risk of hospitalization or mortality among those infected with SARS-CoV-2. However, there was a nearly 40% lower risk of hospitalization with the use of ACE inhibitors in the Medicare population. This finding merits a clinical trial to evaluate the potential role of ACE inhibitors in reducing the risk of hospitalization among older individuals, who are at an elevated risk of adverse outcomes with the infection.

This study compares statewide cardiac surgery outcome reporting in California and New York with contemporary performance.

Additional file 1: Figure A1. Monthly cohort definition for AMI, HF or Pneumonia Readmissions and Observation Stays (Pg.2). Figure A2. Hierarchy for multiple post-discharge care events (Pg. 2). Table A1. Cumulative numbers and percentages of final action inpatient claims uploaded to the IDR for all conditions with a discharge date in January 2013 and July 2013 (as of December 2014) (Pg.3). Figure A3: Modeling approach (Pg.3). Figure A4. Timing of calculating monthly outcomes (Pg.4). Table A2. Final model specification for prediction of number of admissions, readmission rate, and observation-stay rate in AMI cohort (Pg.4). Table A3: Specifications of the final real-time reporting models for Heart failure and pneumonia (Pg.6). Table A4: Results of look-back validation where we compare the rates estimated (for the months July 2016 through December 2016) in February 2017 using RTR models with the final rates later observed using data downloaded from the IDR in February 2018 for AMI, HF and Pneumonia (Pg.7). Figure A5. Prediction and look-back validation for heart failure admission, readmission and observation stays (July 2016–December 2016) (Pg.8). Figure A6. Prediction and look-back validation for pneumonia admission, readmission and observation stays (July 2016–December 2016 (Pg.9).

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.

BackgroundThere is conflicting evidence about whether the Hospital Readmission Reduction Program (HRRP) is associated with an increase in mortality.MethodsIn a cohort of Medicare beneficiaries hospitalized with heart failure (HF), we compared two published approaches to evaluating the association of HRRP and risk-adjusted 30-day mortality, including changes in average mortality across periods and changes in slope of monthly mortality rates across discrete periods. We also tested various methods with simulated data that was designed with an inflection in mortality.ResultsWe identified 4,313,523 hospitalizations for HF, 1,788,219 for AMI, and 3,758,111 for pneumonia. Monthly slope-change models identified an increase in mortality for HF and pneumonia in the pre-HRRP period (PConclusionA national policy incentivizing efforts to reduce readmission did not increase the risk of mortality.

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.

BackgroundThe evaluation of trends in patient outcomes requires adjustment for the changes in case-mix over time and, thus, could be influenced by the expansion of code slots on inpatient claims that occurred in January 2011. We tested the hypothesis that the changes in code slots caused an artifactual increase in the case mix over time compared with a strategy that restricted inpatient codes to the same number of slots over time, excluding consideration of codes beyond the first 9 after the expansion.MethodsIn Medicare claims over a 5-year period spanning the inpatient code slot expansion (2008-2012), we created cohorts of hospitalizations for heart failure (HF), acute myocardial infarction (AMI) and pneumonia, common hospitalization conditions included in federal policies. We obtained information on risk factors for 30-day post-discharge mortality or readmission for each condition from inpatient facility Medicare claims, outpatient facility claims and professional (or carrier) claims. We evaluated the effects of additional codes captured from the expanded slots in inpatient claims on the number of risk factors or model covariates, overall and based on their contribution to the risk of mortality or readmission. We modelled the effects of code expansion on risk-assessment using an interrupted time series framework.ResultsThere were 2,102,509 eligible hospitalizations for HF, 872,734 for AMI and 1,824,079 for pneumonia. The average number of risk factors increased across all covariate selection strategies. There was a larger increase in monthly average covariate count that included all codes at the time of the code slot increase relative to a strategy that consistently used only 9 inpatient codes (level change in interrupted time series model, 0.9% [95% CI 0.7% to 1.1%] in HF, 0.6% [0.5% to 0.7%] for AMI and 0.6% [0.4% to 0.8%] for pneumonia). Using both inpatient and outpatient/carrier codes for assessing risk factors, there was a smaller difference between strategies using 9 inpatient codes, compared with all inpatient claims (relative excess increase in covariates by 0.6% [0.4% to 0.8%] in HF, 0.4% [0.3% to 0.5%] for AMI, and 0.3% [0.1% to 0.6%] in pneumonia). However, the additional codes were limited to covariates with small contributions to the risk-adjustment models for mortality, without a significant inflection in measured risk of mortality across code expansion (P>0.05 in interrupted time-series models). Measured readmission risk increased with using only inpatient claims for risk assessment, but not if all outpatient and carrier claims were also used (P>0.05 in interrupted time-series models).ConclusionsThe expansion of inpatient code slots did not meaningfully affect the measurement of the risk of mortality or readmission, especially if comprehensive inpatient and outpatient claims are used, because the additional covariates only included conditions with a modest influence on risk adjusted models. The use of all versus limited codes after the code slot expansion has a minimal effect on evaluating trends in these conditions.

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.

RESEARCH OBJECTIVE: Socioeconomic disparities in hospital readmissions for Medicare patients are well established. Medicare currently provides hospitals with within‐hospital disparities in readmission using dual eligibility status as a social risk factor. Community‐based social risk factors, such as the AHRQ SES Index (ASI), may allow capture of different types of information regarding social risk, as compared to income and assets, which are principally used for establishing dual eligibility. This study examines concordance of within‐hospital disparities in 30‐day readmission rates for Medicare patients that arise from dual eligibility status and ASI, and how this concordance varies across patients admitted for three common clinical conditions (AMI, HF, and pneumonia). STUDY DESIGN: We conducted a retrospective cohort study of hospitalized Medicare fee‐for‐service (FFS) patients meeting the inclusion criteria for the 30‐day AMI, HF, and PN hospital readmission measures used by Medicare. We used Medicare Part A and B Standard Analytic Files and Enrollment Files to identify the study cohort, clinical risk factors for adjustment, and enrollment information. We calculated the within‐hospital disparity score, for each hospital, using dual eligibility (defined as full‐benefit Medicaid enrollment at time of discharge) and the ASI score for beneficiary 9‐digit ZIP code of residence. Using the method currently used by CMS for estimating within‐hospital disparities, we calculated the hospital‐specific disparity effect for each hospital and each social risk factor for each of the three conditions. Disparity effects are reported as hospital‐specific odds ratios (ORs) and absolute risk differences; we calculated the Spearman rank‐order correlations between the two disparity effects for each condition. POPULATION STUDIED: Hospitalized U.S. Medicare FFS patients aged 65 years and older admitted to U.S. acute care, critical access, and Veterans Affairs hospitals from July 1, 2015, to June 30, 2018. PRINCIPAL FINDINGS: The final sample included 3,126,185 Medicare FFS admissions to 4,730 U.S. hospitals. Both dual eligibility (mean ORs for three conditions ranging from 1.04 to 1.08) and AHRQ SES Index (mean ORs ranging 1.03‐1.05) were associated with higher adjusted odds of 30‐day readmission. However, hospital‐specific disparity effects for the two risk factors were uncorrelated for AMI (Spearman’s correlation □ = ‐0.017, P = .28), weakly correlated for HF (□ = 0.10, P

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.

Background With incentives to reduce readmission rates in the United States, there are concerns that patients who need hospitalization after a recent hospital discharge may be denied access, which would increase their risk of mortality. Objective We determined whether patients with hospitalizations for conditions covered by national readmission programs who received care in emergency department (ED) or observation units but were not hospitalized within 30 days had an increased risk of death. We also evaluated temporal trends in post-discharge acute care utilization in inpatient units, emergency department (ED) and observation units for these patients. Design, Setting, and Participants In this observational study of national Medicare claims data for 2008-2016, we identified patients ≥65 years hospitalized with heart failure (HF), acute myocardial infarction (AMI), or pneumonia, conditions included in the HRRP. Main Outcomes and Measures Post-discharge 30-day mortality according to patients’ 30-day acute care utilization. Acute care utilization in inpatient and observation units, and the ED during the 30-day and 31-90-day post-discharge period. Results There were 3,772,924 hospitalizations for HF, 1,570,113 for AMI, and 3,131,162 for pneumonia. The overall post-discharge 30-day mortality was 8.7% for HF, 7.3% for AMI, and 8.4% for pneumonia. Risk-adjusted mortality increased annually by 0.05% (95% CI, 0.02% to 0.08%) for HF, decreased by 0.06% (95% CI, −0.09% to −0.04%) for AMI, and did not significantly change for pneumonia. Specifically, mortality increased for HF patients who did not utilize any post-discharge acute care, increasing at a rate of 0.08% per year (95% CI, 0.05% to 0.12%), exceeding the overall absolute annual increase in post-discharge mortality in heart failure, without an increase in mortality in observation units or the ED. Concurrent with a reduction in 30-day readmission rates, 30-day observation stays and visits to the ED increased across all 3 conditions during and beyond the post-discharge 30-day period. There was no significant change in overall 30-day post-acute care utilization. Conclusions The only condition with an increasing mortality through the study period was HF; the increase preceded the policy and was not present among those received ED or observation unit care without hospitalization. Overall, during this period, there was not a significant change in the overall 30-day post-discharge acute care utilization. What is already known on this topic Among Medicare beneficiaries hospitalized for heart failure, mortality in the 30-day post-discharge period has been increasing over the past several years. However, the relationship between post-discharge acute care and mortality in the early post-discharge period remains poorly understood. Observation units and the emergency departments (ED) have increasingly been used as avenues for patient care in the United States. However, the utilization of these services in the early post-discharge period for conditions targeted in the Hospital Readmissions Reduction Program (HRRP), and the outcomes of patients in these settings, is required to appropriately evaluate the effects of the program. What this study adds Among conditions targeted in the HRRP, patients with heart failure, but not those with acute myocardial infarction or pneumonia, experienced increase in post-discharge 30-day mortality. This increase preceded the announcement of the program and was concentrated among individuals who sought no post-discharge acute care, nearly half of whom had been discharged to hospice. Despite increasing utilization of observation units and the ED in the post-discharge period, care in these settings was not associated with increased mortality risk.

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.

Background The Hospital Readmissions Reduction Program (HRRP) has been associated with reduced 30-day readmissions for acute myocardial infarction (AMI) and heart failure (HF). Objectives The purpose of this study was to test whether this 30-day readmission reduction is a manifestation of practices that defer or avoid hospitalizations beyond the 30-day period. Methods At all U.S. hospitals under HRRP, the authors calculated daily readmission rates for elderly Medicare fee-for-service beneficiaries through day-60 post-discharge following a hospitalization for AMI and HF—the 2 target cardiovascular conditions—as well as pneumonia in July 2008 to June 2016. The authors applied a robust bias-corrected nonparametric regression approach to evaluate for discontinuities in rates around day 30. Results The authors identified 3,256 eligible hospitals, with median readmission rates in the days 1 to 30 and 31 to 60 post-discharge of 19.6% (interquartile range [IQR]: 16.7% to 22.9%) and 7.8% (IQR: 6.5% to 9.4%) for AMI, 23.0% (IQR: 20.6% to 25.3%) and 11.4% (IQR: 10.2% to 12.6%) for HF, and 17.5% (IQR: 15.4% to 19.8%) and 8.3% (IQR: 7.3% to 9.3%) for pneumonia, respectively. Daily readmission rates decreased across most of the 60 post-discharge days, with no discontinuities in the local polynomial regression for readmission at the 30-day mark, with a >95% power to detect 0.1% difference for each outcome across post-discharge day 30. Similarly, there was no discontinuity in mortality at 30 days post-discharge, or for either outcome at hospitals that incurred readmission penalties. Conclusions There was no evidence that clinicians adopted strategies that specifically deferred admissions or affected mortality in the 30-day period after discharge. The findings are consistent with the institution of strategies that generally affected readmission risk after discharge.

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 The number of percutaneous coronary interventions (PCI) in China has increased more than 20-fold over the last decade. Consequently, there is a need for national-level information to characterize PCI indications and long-term patient outcomes, including health status, to understand and improve evolving practice patterns. Objectives This nationwide prospective study of patients receiving PCI is to: (1) measure long-term clinical outcomes (including death, acute myocardial infarction [AMI], and/or revascularization), patient-reported outcomes (PROs), cardiovascular risk factor control and adherence to medications for secondary prevention; (2) determine patient- and hospital-level factors associated with care process and outcomes; and (3) assess the appropriateness of PCI procedures. Methods The China Patient-centered Evaluative Assessment of Cardiac Events (PEACE) Prospective Study of PCI has enrolled 5,000 consecutive patients during 2012-2014 from 34 diverse hospitals across China undergoing PCI for any indication. We abstracted details of patient's medical history, treatments, and in-hospital outcomes from medical charts, and conducted baseline, 1-, 6-, and 12-month interviews to characterize patient demographics, risk factors, clinical presentation, healthcare utilization, and health status using validated PRO measures. The primary outcome, a composite measure of death, AMI and/or revascularization, as well as PROs, medication adherence and cardiovascular risk factor control, was assessed throughout the 12-month follow-up. Blood and urine samples were collected at baseline and 12 months and stored for future analyses. To validate reports of coronary anatomy, 2,000 angiograms are randomly selected and read by two independent core laboratories. Hospital characteristics regarding their facilities, processes and organizational characteristics are assessed by site surveys. Conclusion China PEACE Prospective Study of PCI will be the first study to generate novel, high-quality, comprehensive national data on patients' socio-demographic, clinical, treatment, and metabolic/genetic factors, and importantly, their long-term outcomes following PCI, including health status. This will build the foundation for PCI performance improvement efforts in China. © 2016 The Authors. Catheterization and Cardiovascular Interventions. Published by Wiley Periodicals, Inc.

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.

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

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 Since clinical management of heart failure relies on weights that are self-reported by the patient, errors in reporting will negatively impact the ability of health care professionals to offer timely and effective preventive care. Errors might often result from rounding, or more generally from individual preferences for numbers ending in certain digits, such as 0 or 5. We apply fraud detection methods to assess preferences for numbers ending in these digits in order to inform medical decision making. Methods The Telemonitoring to Improve Heart Failure Outcomes trial tested an approach to telemonitoring that used existing technology; intervention patients (n = 826) were asked to measure their weight daily using a digital scale and to relay measurements using their telephone keypads. First, we estimated the number of weights subject to end-digit preference by dividing the weights by five and comparing the resultant distribution with the uniform distribution. Then, we assessed the characteristics of patients reporting an excess number of weights ending in 0 or 5, adjusting for chance reporting of these values. Results Of the 114,867 weight readings reported during the trial, 18.6% were affected by end-digit preference, and the likelihood of these errors occurring increased with the number of days that had elapsed since trial enrolment (odds ratio per day: 1.002, p

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

Background: 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.