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BackgroundPublicly reported hospital risk-standardized mortality rates (RSMRs) for acute myocardial infarction (AMI) are calculated for Medicare beneficiaries. Outcomes for older patients with AMI may not reflect general outcomes.ObjectiveTo examine the relationship between hospital 30-day RSMRs for older patients (aged ≥65 years) and those for younger patients (aged 18 to 64 years) and all patients (aged ≥18 years) with AMI.DesignRetrospective cohort study.Setting986 hospitals in the ACTION (Acute Coronary Treatment and Intervention Outcomes Network) Registry-Get With the Guidelines.ParticipantsAdults hospitalized for AMI from 1 October 2010 to 30 September 2014.MeasurementsHospital 30-day RSMRs were calculated for older, younger, and all patients using an electronic health record measure of AMI mortality endorsed by the National Quality Forum. Hospitals were ranked by their 30-day RSMRs for these 3 age groups, and agreement in rankings was plotted. The correlation in hospital AMI achievement scores for each age group was also calculated using the Hospital Value-Based Purchasing (HVBP) Program method computed with the electronic health record measure.Results267 763 and 276 031 AMI hospitalizations among older and younger patients, respectively, were identified. Median hospital 30-day RSMRs were 9.4%, 3.0%, and 6.2% for older, younger, and all patients, respectively. Most top- and bottom-performing hospitals for older patients were neither top nor bottom performers for younger patients. In contrast, most top and bottom performers for older patients were also top and bottom performers for all patients. Similarly, HVBP achievement scores for older patients correlated weakly with those for younger patients (R = 0.30) and strongly with those for all patients (R = 0.92).LimitationMinority of U.S. hospitals.ConclusionHospital mortality rankings for older patients with AMI inconsistently reflect rankings for younger patients. Incorporation of younger patients into assessment of hospital outcomes would permit further examination of the presence and effect of age-related quality differences.Primary funding sourceAmerican College of Cardiology.

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

The financial barriers to health care services or medication among individuals following acute myocardial infarction (AMI) and their association with subsequent health care outcomes are evaluated. The results have shown that the financial barriers to health care services and medications are increasingly associated with worse recovery after AMI, manifested as more angina, poorer quality of life and higher risk of rehospitalization.

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. [ABSTRACT FROM AUTHOR]

Background: Patients aged ≥ 65 years are vulnerable to readmissions due to a transient period of generalized risk after hospitalization. However, whether young and middle-aged adults share a similar risk pattern is uncertain. We compared the rate, timing, and readmission diagnoses following hospitalization for heart failure (HF), acute myocardial infarction (AMI), and pneumonia among patients aged 18-64 years with patients aged ≥ 65 years. Methods and Findings: We used an all-payer administrative dataset from California consisting of all hospitalizations for HF (n = 206,141), AMI (n = 107,256), and pneumonia (n = 199,620) from 2007-2009. The primary outcomes were unplanned 30- day readmission rate, timing of readmission, and readmission diagnoses. Our findings show that the readmission rate among patients aged 18-64 years exceeded the readmission rate in patients aged ≥ 65 years in the HF cohort (23.4% vs. 22.0%, p < 0.001), but was lower in the AMI (11.2% vs. 17.5%, p < 0.001) and pneumonia (14.4% vs. 17.3%, p < 0.001) cohorts. When adjusted for sex, race, comorbidities, and payer status, the 30-day readmission risk in patients aged 18-64 years was similar to patients ≥ 65 years in the HF (HR 0.99; 95%CI 0.97-1.02) and pneumonia (HR 0.97; 95%CI 0.94- 1.01) cohorts and was marginally lower in the AMI cohort (HR 0.92; 95%CI 0.87-0.96). For all cohorts, the timing of readmission was similar; readmission risks were highest between days 2 and 5 and declined thereafter across all age groups. Diagnoses other than the index admission diagnosis accounted for a substantial proportion of readmissions among age groups < 65 years; a non-cardiac diagnosis represented 39-44% of readmissions in the HF cohort and 37-45% of readmissions in the AMI cohort, while a non-pulmonary diagnosis represented 61-64% of patients in the pneumonia cohort. Conclusion: When adjusted for differences in patient characteristics, young and middle-aged adults have 30-day readmission rates that are similar to elderly patients for HF, AMI, and pneumonia. A generalized risk after hospitalization is present regardless of age., Introduction Reducing readmissions is a key focus of current health policy initiatives in the United States (US). The scope of the problem among elderly individuals is well established--one in five [...]

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

Objective To 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. Design Retrospective observational study of informed consent documents. Setting 25 US hospitals, diverse in size and geographical region. Cohort Among 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 outcome The 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. Results Among 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. Conclusion All 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. [ABSTRACT FROM AUTHOR]

Objective To develop a nationally applicable tool for assessing the quality of informed consent documents for elective procedures. Design Mixed qualitative-quantitative approach. Setting Convened seven meetings with stakeholders to obtain input and feedback on the tool. Participants Team 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. Interventions With 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. Outcomes Abstraction tool to evaluate the quality of informed consent documents. Results We 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%. Conclusions We 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. [ABSTRACT FROM AUTHOR]

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