Risk-standardized Readmission Rates Research Articles

Effect of disability, homelessness, and neighborhood marginalization on risk-adjustment for hospital performance measurement.

Background It is not known how disability, homelessness, or neighborhood marginalization influence risk-adjusted hospital performance measurement in a universal health care system. Methods We evaluated the effect of including these equity-related factors in risk-adjustment models for in-hospital mortality, and 7- and 30-day readmission in 28 hospitals in Ontario, Canada. We compared risk-adjustment with commonly-used clinical factors to models that also included homelessness, disability, and neighborhood indices of marginalization. We evaluated models in historical data using internal-external cross-validation. We calculated risk-standardized outcome rates for each hospital in a recent reporting period using mixed-effects logistic regression. Results The cohort included 544,805 admissions. Adjustment for disability, homelessness, and neighborhood marginalization had little impact on discrimination or calibration of risk-adjustment models. However, it influenced comparative hospital performance on risk-standardized 30-day readmission rates, resulting in 5 hospitals being reclassified between below-average, average, and above-average groups. No hospitals were reclassified for mortality and 7-day readmission. Conclusion In a system with universally insured hospital services, adjustment for disability, homelessness, and neighborhood marginalization influenced estimates of hospital performance for 30-day readmission but not 7-day readmission or in-hospital mortality. These findings can inform researchers and policymakers as they thoughtfully consider when to adjust for these factors in hospital performance measurement.

Incorporating Medicare Advantage Admissions Into the CMS Hospital-Wide Readmission Measure

Medicare Advantage (MA) enrollment is rapidly expanding, yet Centers for Medicare & Medicaid Services (CMS) claims-based hospital outcome measures, including readmission rates, have historically included only fee-for-service (FFS) beneficiaries. To assess the outcomes of incorporating MA data into the CMS claims-based FFS Hospital-Wide All-Cause Unplanned Readmission (HWR) measure. This cohort study assessed differences in 30-day unadjusted readmission rates and demographic and risk adjustment variables for MA vs FFS admissions. Inpatient FFS and MA administrative claims data were extracted from the Integrated Data Repository for all admissions for Medicare beneficiaries from July 1, 2018, to June 30, 2019. Measure reliability and risk-standardized readmission rates were calculated for the FFS and MA cohort vs the FFS-only cohort, overall and within specialty subgroups (cardiorespiratory, cardiovascular, medicine, surgery, neurology), then changes in hospital performance quintiles were assessed after adding MA admissions. Risk-standardized readmission rates. The cohort included 11 029 470 admissions (4 077 633 [37.0%] MA; 6 044 060 [54.8%] female; mean [SD] age, 77.7 [8.2] years). Unadjusted readmission rates were slightly higher for MA vs FFS admissions (15.7% vs 15.4%), yet comorbidities were generally lower among MA beneficiaries. Test-retest reliability for the FFS and MA cohort was higher than for the FFS-only cohort (0.78 vs 0.73) and signal-to-noise reliability increased in each specialty subgroup. Mean hospital risk-standardized readmission rates were similar for the FFS and MA cohort and FFS-only cohorts (15.5% vs 15.3%); this trend was consistent across the 5 specialty subgroups. After adding MA admissions to the FFS-only HWR measure, 1489 hospitals (33.1%) had their performance quintile ranking changed. As their proportion of MA admissions increased, more hospitals experienced a change in their performance quintile ranking (147 hospitals [16.3%] in the lowest quintile of percentage MA admissions; 408 [45.3%] in the highest). The combined cohort added 63 hospitals eligible for public reporting and more than 4 million admissions to the measure. In this cohort study, adding MA admissions to the HWR measure was associated with improved measure reliability and precision and enabled the inclusion of more hospitals and beneficiaries. After MA admissions were included, 1 in 3 hospitals had their performance quintile changed, with the greatest shifts among hospitals with a high percentage of MA admissions.

Including socioeconomic status reduces readmission penalties to safety-net hospitals

ObjectiveMedicare’s Hospital Readmissions Reduction Program (HRRP) financially penalizes “excessive” postoperative readmissions. Concerned with creating a double standard for institutions treating a high percentage of economically vulnerable patients, Medicare elected to exclude socioeconomic status (SES) from its risk-adjustment model. However, recent evidence suggests that safety-net hospitals (SNHs) caring for many low-SES patients are disproportionately penalized under the HRRP. We sought to simulate the impact of including SES-sensitive models on HRRP penalties for hospitals performing lower extremity revascularization (LER). MethodsThis is a retrospective, cross-sectional analysis of national data on Medicare patients undergoing open or endovascular LER procedures between 2007 and 2009. We used hierarchical logistic regression to generate hospital risk-standardized 30-day readmission rates under Medicare’s current model (adjusting for age, sex, comorbidities, and procedure type) compared with models that also adjust for SES. We estimated the likelihood of a penalty and penalty size for SNHs compared with non-SNHs under the current Medicare model and these SES-sensitive models. ResultsOur study population comprised 1708 hospitals performing 284,724 LER operations with an overall unadjusted readmission rate of 14.4% (standard deviation: 5.3%). Compared with the Centers for Medicare and Medicaid Services model, adjusting for SES would not change the proportion of SNHs penalized for excess readmissions (55.1% vs 53.4%, P = .101) but would reduce penalty amounts for 38% of SNHs compared with only 17% of non-SNHs, P < .001. ConclusionsFor LER, changing national Medicare policy to including SES in readmission risk-adjustment models would reduce penalty amounts to SNHs, especially for those that are also teaching institutions. Making further strides toward reducing the national disparity between SNHs and non-SHNs on readmissions, performance measures require strategies beyond simply altering the risk-adjustment model to include SES.

Relationship Between In-Hospital Adverse Events and Hospital Performance on 30-Day All-cause Mortality and Readmission for Patients With Heart Failure.

Hospitals with high mortality and readmission rates for patients with heart failure (HF) might also perform poorly in other quality concepts. We sought to evaluate the association between hospital performance on mortality and readmission with hospital performance rates of safety adverse events. This cross-sectional study linked the 2009 to 2019 patient-level adverse events data from the Medicare Patient Safety Monitoring System, a randomly selected medical records-abstracted patient safety database, to the 2005 to 2016 hospital-level HF-specific 30-day all-cause mortality and readmissions data from the United States Centers for Medicare & Medicaid Services. Hospitals were classified to one of 3 performance categories based on their risk-standardized 30-day all-cause mortality and readmission rates: better (both in <25th percentile), worse (both >75th percentile), and average (otherwise). Our main outcome was the occurrence (yes/no) of one or more adverse events during hospitalization. A mixed-effect model was fit to assess the relationship between a patient's risk of having adverse events and hospital performance categories, adjusted for patient and hospital characteristics. The study included 39 597 patients with HF from 3108 hospitals, of which 252 hospitals (8.1%) and 215 (6.9%) were in the better and worse categories, respectively. The rate of patients with one or more adverse events during a hospitalization was 12.5% (95% CI, 12.1-12.8). Compared with patients admitted to better hospitals, patients admitted to worse hospitals had a higher risk of one or more hospital-acquired adverse events (adjusted risk ratio, 1.24 [95% CI, 1.06-1.44]). Patients admitted with HF to hospitals with high 30-day all-cause mortality and readmission rates had a higher risk of in-hospital adverse events. There may be common quality issues among these 3 measure concepts in these hospitals that produce poor performance for patients with HF.

Achieving Equity in Hospital Performance Assessments Using Composite Race-Specific Measures of Risk-Standardized Readmission and Mortality Rates for Heart Failure.

The contemporary measures of hospital performance for heart failure hospitalization and 30-day risk-standardized readmission rate (RSRR) and risk-standardized mortality rate (RSMR) are estimated using the same risk adjustment model and overall event rate for all patients. Thus, these measures are mainly driven by the care quality and outcomes for the majority racial and ethnic group, and may not adequately represent the hospital performance for patients of Black and other races. Fee-for-service Medicare beneficiaries from January 2014 to December 2019 hospitalized with heart failure were identified. Hospital-level 30-day RSRR and RSMR were estimated using the traditional race-agnostic models and the race-specific approach. The composite race-specific performance metric was calculated as the average of the RSRR/RMSR measures derived separately for each race and ethnicity group. Correlation and concordance in hospital performance for all patients and patients of Black and other races were assessed using the composite race-specific and race-agnostic metrics. The study included 1 903 232 patients (75.7% White [n=1 439 958]; 14.5% Black [n=276 684]; and 9.8% other races [n=186 590]) with heart failure from 1860 hospitals. There was a modest correlation between hospital-level 30-day performance metrics for patients of White versus Black race (Pearson correlation coefficient: RSRR=0.42; RSMR=0.26). Compared with the race-agnostic RSRR and RSMR, composite race-specific metrics for all patients demonstrated stronger correlation with RSRR (correlation coefficient: 0.60 versus 0.74) and RSMR (correlation coefficient: 0.44 versus 0.51) for Black patients. Concordance in hospital performance for all patients and patients of Black race was also higher with race-specific (versus race-agnostic) metrics (RSRR=64% versus 53% concordantly high-performing; 61% versus 51% concordantly low-performing). Race-specific RSRR and RSMR metrics (versus race-agnostic) led to reclassification in performance ranking of 35.8% and 39.2% of hospitals, respectively, with better 30-day and 1-year outcomes for patients of all race groups at hospitals reclassified as high-performing. Among patients hospitalized with heart failure, race-specific 30-day RSMR and RSRR are more equitable in representing hospital performance for patients of Black and other races.

Abstract 157: Association Of Hospital Performance With 1-Year Mortality And Recurrence After Ischemic Stroke Among US Medicare Beneficiaries

Background: Whether stroke patients treated at hospitals with better short-term quality performance metrics have better long-term outcomes is unknown. We determined if ischemic stroke patients treated at US hospitals with better past performance on the Centers for Medicare &amp; Medicaid Services (CMS) 30-day mortality and readmission outcome measures for stroke had better 1-year outcomes compared to those treated at other hospitals. Methods: We included all Medicare fee-for-service beneficiaries aged ≥65 years discharged alive from US hospitals with a principal diagnosis of ischemic stroke from 07/01/2015 to 12/31/2018. We categorized patients by the treating hospital’s performance on the CMS hospital-specific 30-day risk-standardized all-cause mortality and readmission rate measures for stroke from 07/01/2012 to 06/30/2015: Low-Low (both CMS mortality and readmission rates for the hospital were &lt;25 th percentile of national rates), High-High (both &gt;75 th percentile), and Intermediate (all others). We balanced characteristics between hospital performance categories using stabilized inverse probability weights (IPW) based on patient demographic and clinical factors. We fit Cox models to assess patient risks of 1-year mortality and recurrent stroke across hospital performance categories, weighted by the IPW and accounting for competing risks. Results: There were 595,929 stroke patients (mean age 78.9y, 54.4% women, 82.2% White) discharged from 134 Low-Low, 2288 Intermediate, and 141 High-High hospitals. For patients treated at Low-Low, Intermediate, and High-High hospitals, respectively, 1-year mortality rates were 23.8% (95% CI 23.3-24.3%), 25.2% (25.1-25.3%), and 26.5% (26.1-26.9%), and recurrence rates were 8.0% (7.6-8.3%), 7.9% (7.8-8.0%), and 8.0% (7.7-8.3%). Compared with patients at High-High hospitals, those at Low-Low and Intermediate hospitals, respectively, had 15% (hazard ratio 0.85; 95% CI 0.82-0.87) and 9% (0.91; 0.89-0.93) lower risks of 1-year mortality but no difference in 1-year recurrence. Conclusions: Ischemic stroke patients treated at better-performing hospitals had lower risks of post-discharge 1-year mortality but similar risks of recurrent stroke compared with patients treated at other hospitals.

Development of a 30-Day Readmission Risk Calculator for the Inpatient Rehabilitation Setting.

Readmission to acute care from the inpatient rehabilitation facility (IRF) setting is potentially preventable and an important target of quality improvement and cost savings. The objective of this study was to develop a risk calculator to predict 30-day all-cause readmissions from the IRF setting. Retrospective database analysis using the Uniform Data System for Medical Rehabilitation (UDSMR) from 2015 through2019. In total, 956 US inpatient rehabilitation facilities and 1,849,768 IRF discharges comprising patients from 14 impairment groups. Logistic regression models were developed to calculate risk-standardized 30-day all-cause hospital readmission rates for patients admitted to an IRF. Models for each impairment group were assessed using 12 common clinical and demographic variables and all but 4 models included various special variables. Models were assessed for discrimination (c-statistics), calibration (calibration plots), and internal validation (bootstrapping). A readmission risk scoring system was created for each impairment group population and was graphically validated. The mean age of the cohort was 68.7 (15.2) years, 50.7% were women, and 78.3% were Caucasian. Medicare was the primary payer for 73.1% of the study population. The final models for each impairment group included between 4 and 13 total predictor variables. Model c-statistics ranged from 0.65 to 0.70. There was good calibration represented for most models up to a readmission risk of 30%. Internal validation of the models using bootstrap samples revealed little bias. Point systems for determining risk of 30-day readmission were developed for each impairment group. Multivariable risk factor algorithms based upon administrative data were developed to assess 30-day readmission risk for patients admitted from IRF. This report represents the development of a readmission risk calculator for the IRF setting, which could be instrumental in identifying high risk populations for readmission and targeting resources towards a diverse group of IRF impairment groups.

Ninety-Day Risk-Standardized Home Time as a Performance Metric for Cardiac Surgery Hospitals in the United States.

Assessing hospital performance for cardiac surgery necessitates consistent and valid care quality metrics. The association of hospital-level risk-standardized home time for cardiac surgeries with other performance metrics such as mortality rate, readmission rate, and annual surgical volume has not been evaluated previously. The study included Medicare beneficiaries who underwent isolated or concomitant coronary artery bypass graft, aortic valve, or mitral valve surgery from January 1, 2013, to October 1, 2019. Hospital-level performance metrics of annual surgical volume, 90-day risk-standardized mortality rate, 90-day risk-standardized readmission rate, and 90-day risk-standardized home time were estimated starting from the day of surgery using generalized linear mixed models with a random intercept for the hospital. Correlations between the performance metrics were assessed using the Pearson correlation coefficient. Patient-level clinical outcomes were also compared across hospital quartiles by 90-day risk-standardized home time. Last, the temporal stability of performance metrics for each hospital during the study years was also assessed. Overall, 919 698 patients (age 74.2±5.8 years, 32% women) were included from 1179 hospitals. Median 90-day risk-standardized home time was 71.2 days (25th-75th percentile, 66.5-75.6), 90-day risk-standardized readmission rate was 26.0% (19.5%-35.7%), and 90-day risk-standardized mortality rate was 6.0% (4.0%-8.8%). Across 90-day home time quartiles, a graded decline was observed in the rates of in-hospital, 90-day, and 1-year mortality, and 90-day and 1-year readmission. Ninety-day home time had a significant positive correlation with annual surgical volume (r=0.31; P<0.001) and inverse correlation with 90-day risk-standardized readmission rate (r=-0.40; P <0.001) and 90-day risk-standardized mortality rate (r=-0.60; P <0.001). Use of 90-day home time as a performance metric resulted in a meaningful reclassification in performance ranking of 22.8% hospitals compared with annual surgical volume, 11.6% compared with 90-day risk-standardized mortality rate, and 19.9% compared with 90-day risk-standardized readmission rate. Across the 7 years of the study period, 90-day home time demonstrated the most temporal stability of the hospital performance metrics. Ninety-day risk-standardized home time is a feasible, comprehensive, patient-centered metric to assess hospital-level performance in cardiac surgery with greater temporal stability than mortality and readmission measures.

Analysis of Hospital-Level Readmission Rates and Variation in Adverse Events Among Patients With Pneumonia in the United States

It is known that hospitalized patients who experience adverse events are at greater risk of readmission; however, it is unknown whether patients admitted to hospitals with higher risk-standardized readmission rates had a higher risk of in-hospital adverse events. To evaluate whether patients with pneumonia admitted to hospitals with higher risk-standardized readmission rates had a higher risk of adverse events. This cross-sectional study linked patient-level adverse events data from the Medicare Patient Safety Monitoring System (MPSMS), a randomly selected medical record abstracted database, to the hospital-level pneumonia-specific all-cause readmissions data from the Centers for Medicare & Medicaid Services. Patients with pneumonia discharged from July 1, 2010, through December 31, 2019, in the MPSMS data were included. Hospital performance on readmissions was determined by the risk-standardized 30-day all-cause readmission rate. Mixed-effects models were used to examine the association between adverse events and hospital performance on readmissions, adjusted for patient and hospital characteristics. Analysis was completed from October 2019 through July 2020 for data from 2010 to 2017 and from March through April 2022 for data from 2018 to 2019. Patients hospitalized for pneumonia. Adverse events were measured by the rate of occurrence of hospital-acquired events and the number of events per 1000 discharges. The sample included 46 047 patients with pneumonia, with a median (IQR) age of 71 (58-82) years, with 23 943 (52.0%) women, 5305 (11.5%) Black individuals, 37 763 (82.0%) White individuals, and 2979 (6.5%) individuals identifying as another race, across 2590 hospitals. The median hospital-specific risk-standardized readmission rate was 17.0% (95% CI, 16.3%-17.7%), the occurrence rate of adverse events was 2.6% (95% CI, 2.54%-2.65%), and the number of adverse events per 1000 discharges was 157.3 (95% CI, 152.3-162.5). An increase by 1 IQR in the readmission rate was associated with a relative 13% higher patient risk of adverse events (adjusted odds ratio, 1.13; 95% CI, 1.08-1.17) and 5.0 (95% CI, 2.8-7.2) more adverse events per 1000 discharges at the patient and hospital levels, respectively. Patients with pneumonia admitted to hospitals with high all-cause readmission rates were more likely to develop adverse events during the index hospitalization. This finding strengthens the evidence that readmission rates reflect the quality of hospital care for pneumonia.

Association of readmission penalty amount with subsequent 30-day risk standardized readmission and mortality rates among patients hospitalized with heart failure: An analysis of get with the guidelines – heart failure participating centers

The Hospital Readmissions Reduction Program penalizes hospitals with excess 30-day risk-standardized readmission rates (RSRR) for heart failure (HF). The association of financial penalty amount with subsequent short-term clinical outcomes is unknown. Patients admitted to American Heart Association Get With The Guidelines-HF registry participating centers from October 1, 2012 through December 1, 2015 who had Medicare-linked data were included. October 2012 hospital-specific penalty amounts were calculated based on diagnosis-related group payments and excess readmission ratios. Adjusted Cox models were created to evaluate the association of penalty amount categories (non-penalized: 0%; low-penalized: >0%-<0.50%; mid-penalized ≥0.50%-<0.99%; high-penalized ≥0.99%) with subsequent 30-day RSRR and risk-standardized mortality rates (RSMR). Trends in post-discharge 30-day RSRR and RSMR from 2012 to 2015 were analyzed across hospitals stratified by penalty amount categories. The present study included 61,329 patients who were admitted across 262 hospitals. Compared with patients admitted to non-penalized hospitals (36.3%), those admitted to increasingly penalized hospitals were more likely to have higher 30-day RSRR (low-penalized [43.9%]: HR, 1.10 [95% CI, 1.04-1.16]; mid-penalized [12.0%]: HR, 1.07 [95% CI, 0.99-1.16]; high-penalized [7.9%]: HR, 1.23 [95% CI, 1.12-1.35]) but not 30-day RSMR. Over time, 30-day RSRR and RSMR did not meaningfully change across penalized versus non-penalized hospitals. Financial penalties based on 30-day RSRR are not associated with declines in 30-day RSRR or RSMR from 2012 to 2015 among patients hospitalized with HF. Financially penalizing hospitals based on current Hospital Readmissions Reduction Program metrics may not incentivize improvements in short-term clinical outcomes for HF.

The Relationship Between Governing Board Composition and Medicare Shared Savings Program Accountable Care Organizations Outcomes: an Observational Study.

Early studies of Medicare Shared Savings Program (MSSP) accountable care organizations (ACOs) suggested that physician leadership was an important driver of ACO success, but it is unknown whether the demographic and professional composition of current MSSP ACO governing boards is associated with ACOs' publicly reported outcomes. To investigate whether governing boards with higher physician participation and greater female involvement have better outcomes. Cross-sectional observational study. All 2017 MSSP ACOs identified by the Center for Medicare and Medicaid Services ACO Public Use Files (PUF). We collected governing board composition from ACO websites in 2019. Outcome metrics included risk-standardized readmission and unplanned admissions rates. We used descriptive statistics and linear regression models to examine the association between board composition and outcomes. Of the 339 ACOs that still existed in 2019 and had available data, 77% had physician-majority boards and 11.5% had no women on their boards. Eighty-nine percent reported a Medicare beneficiary on their board, of which about one-third had a woman representative. The average number of members on MSSP ACO boards was 12, with a mean of 67% physicians and 24% women. Board composition varied minimally by ACO characteristics, such as geographic region, number of beneficiaries, or type of participants. Higher levels of physician participation in ACO governing boards were associated with lower all-cause unplanned admission rates for patients with heart failure (p = - 0.26, p < 0.001) and for patients with multiple chronic conditions (p = - 0.28, p = 0.001). The number of women on the board was not associated with any outcome differences. MSSP ACO governing boards were predominately male and physician-led. Physician involvement may be important for achieving quality goals, while lack of female involvement showcases an opportunity to diversify boards.

The Association of Readmission Reduction Activities with Primary Care Practice Readmission Rates.

A great deal of research has focused on how hospitals influence readmission rates. While hospitals play a vital role in reducing readmissions, a significant portion of the work also falls to primary care practices. Despite this critical role of primary care, little empirical evidence has shown what primary care characteristics or activities are associated with reductions in hospital admissions. To examine the relationship between practices' readmission reduction activities and their readmission rates. A retrospective study of 1,788 practices who responded to the National Survey of Healthcare Organizations and Systems (fielded 2017-2018) and 415,663 hospital admissions for Medicare beneficiaries attributed to those practices from 2016 100% Medicare claims data. We constructed mixed-effects logistic regression models to estimate practice-level readmission rates and a linear regression model to evaluate the association between practices' readmission rates with their number of readmission reduction activities. Standardized composite score, ranging from 0 to 1, representing the number of a practice's readmission reduction capabilities. The composite score was composed of 12 unique capabilities identified in the literature as being significantly associated with lower readmission rates (e.g., presence of care manager, medication reconciliation, shared-decision making, etc.). Practices' readmission rates for attributed Medicare beneficiaries. Routinely engaging in more readmission reduction activities was significantly associated (P < .05) with lower readmission rates. On average, practices experienced a 0.05 percentage point decrease in readmission rates for each additional activity. Average risk-standardized readmission rates for practices performing 10 or more of the 12 activities in our composite measure were a full percentage point lower than risk-standardized readmission rates for practices engaging in none of the activities. Primary care practices that engaged in more readmission reduction activities had lower readmission rates. These findings add to the growing body of evidence suggesting that engaging in multiple activities, rather thanany single activity, is associated with decreased readmissions.

Characterization of US Hospital Advertising and Association With Hospital Performance, 2008-2016

Hospital advertising has been touted as a tool to improve consumer decision-making, but little is known about its association with objective measures of hospital quality. To document recent trends in hospital advertising in the US and examine the association between concurrent measures of hospital advertising and quality. Retrospective cross-sectional study of all general acute care hospitals operating in the US between January 2008 and December 2016. Data were analyzed from December 6, 2019, to July 15, 2020. Annualized advertising spending for each hospital as measured by a market research firm. Four composites of hospital performance from the Centers for Medicare & Medicaid Services Hospital Compare database were used: risk-standardized mortality rate, risk-standardized readmission rate, Consumer Assessment of Healthcare Providers & Systems (CAHPS) Overall Patient Experience Rating (scale of 1-5; higher scores indicate a more positive patient experience rating), and overall 5-star rating. Linear models adjusted for hospital bed size, hospital revenue, and geographic census region. The study sample included, on average, 4569 general acute care hospitals per year between 2008 and 2016. During this time, approximately half of acute care hospitals (2239 of 4569 [49%]) advertised their services to consumers and spent a total of $3.39 billion. Relative to hospitals that never advertised, advertising hospitals were more likely to be nonprofit facilities (mean [SD], 66% [47%] vs 51% [50%]; P < .001), had larger bed sizes (mean [SD], 234.3 [210.7] beds vs 84.8 [110.6] beds; P < .001), and had higher net incomes (mean [SD], $17 800 000 [$49 000 000] vs $134 099 [$51 600 000]; P < .001). There was no observed association between hospital advertising and performance. For example, hospitals that advertised had a mean (SD) CAHPS 5-star rating of 3.2 (0.9) stars compared with 3.3 (1.0) stars among hospitals that did not advertise, an insignificant difference (P = .92). We observed no difference in performance between advertising and nonadvertising hospitals in 30-day readmission rates (mean [SD], 15.5% [0.8%] vs 15.6% [1.0%]; P = .25), mortality rates (mean [SD], 12.7% [4.0%] vs 12.0% [4.1%]; P = .46), and overall 5-star hospital ratings (mean [SD], 3.1 [0.8] stars vs 3.0 [0.9] stars; P = .50). A significant difference was observed in adjusted mortality rates across terciles of advertising spending, with lower mortality rates for the hospitals with higher ad spending (2016, mean [SD] mortality composite for hospitals in the highest tercile, 11.2% [4.2%] vs hospitals in the middle tercile, 12.0% [3.8%], and for hospitals in the lowest tercile, 12.7% [4.1%]; P = .003). The results of this cross-sectional study suggest that the amount hospitals spent on direct-to-consumer advertising was not associated with publicly reported measures of hospital quality; instead, hospital advertising spending was higher for financially stable hospitals with higher net incomes.

Hospital Variation in 30-Day Readmissions Following Transcatheter Aortic Valve Replacement.

BackgroundData on hospital variation in 30‐day readmission rates after transcatheter aortic valve replacement (TAVR) are limited. Further, whether such variation is explained by differences in hospital characteristics and hospital practice patterns remains unknown.Methods and ResultsWe used the 2017 Nationwide Readmissions Database to identify hospitals that performed at least 5 TAVRs. Hierarchical logistic regression models were used to examine between‐hospital variation in 30‐day all‐cause risk‐standardized readmission rate (RSRR) after TAVR and to explore reasons underlying hospital variation in 30‐day RSRR. The study included 27 091 index TAVRs performed across 325 hospitals. The median (interquartile range) hospital‐level 30‐day RSRR was 11.9% (11.1%–12.8%) ranging from 8.8% to 16.5%. After adjusting for differences in patient characteristics, there was significant between‐hospital variation in 30‐day RSRR (hospital odds ratio, 1.59; 95% CI, 1.39–1.77). Differences in length of stay and discharge disposition accounted for 15% of the between‐hospital variance in RSRRs. There was no significant association between hospital characteristics and 30‐day readmission rates after TAVR. There was statistically significant but weak correlation between 30‐day RSRR after TAVR and that after surgical aortic valve replacement, percutaneous coronary intervention, acute myocardial infarction, heart failure, and pneumonia (r=0.132–0.298; P<0.001 for all). Causes of 30‐day readmission varied across hospitals, with noncardiac readmissions being more common at the bottom 5% hospitals (ie, those with the highest RSRRs).ConclusionsThere is significant variation in 30‐day RSRR after TAVR across hospitals that is not entirely explained by differences in patient or hospital characteristics as well as hospital‐wide practice patterns. Noncardiac readmissions are more common in hospitals with the highest RSRRs.

Association Between Hospital Cardiovascular Procedural Volumes and Transcatheter Mitral Valve Repair Outcomes

BackgroundCardiovascular procedural volumes can serve as metrics of hospital infrastructure and quality, and are the basis for thresholds for initiating transcatheter mitral valve repair (TMVr) programs. Whether hospital volumes of TMVr, surgical mitral valve replacement or repair (SMVRr), and percutaneous coronary intervention (PCI) are indicators of TMVr quality of care is not known. MethodsWe used the 2017 Nationwide Readmissions Database to identify hospitals that performed at least 5 TMVr procedures. Hospitals were divided into quartiles of TMVr volume. Associations of hospital TMVr, SMVRr, and PCI volumes, as well as SMVRr and PCI outcomes with TMVr outcomes were examined. Outcomes studied were risk-standardized in-hospital mortality rate (RSMR) and 30-day readmission rate (RSRR). ResultsThe study included 3404 TMVr procedures performed across 150 hospitals in the US. The median hospital TMVr volume was 17 (IQR 10, 28). The mean hospital-level RSMR and RSRR for TMVr were 3.0% (95% CI 2.5%, 3.4%) and 14.8% (95% CI 14.5%, 15.0%), respectively. There was no significant association between hospital TMVr volume (as quartiles or as a continuous variable) and TMVr RSMR or RSRR (P > 0.05). Similarly, there was weak or no correlation between hospital SMVRr and PCI volumes and outcomes with TMVr RSMR or RSRR (Pearson correlation coefficients, r = −0.199 to 0.269). ConclusionIn this study, we found no relationship between hospital TMVr, SMVRr, and PCI volume and TMVr outcomes. Further studies are needed to determine more appropriate structure and process measures to assess the performance of established and new TMVr centers.

Evidence based processes to prevent readmissions: more is better, a ten-site observational study

Background30-day hospital readmissions are an indicator of quality of care; hospitals are financially penalized by Medicare for high rates. Numerous care transition processes reduce readmissions in clinical trials. The objective of this study was to examine the relationship between the number of evidence-based transitional care processes used and the risk standardized readmission rate (RSRR).MethodsDesign: Mixed method, multi-stepped observational study. Data collection occurred 2014–2018 with data analyses completed in 2021. Setting: Ten VA hospitals, chosen for 5-year trend of improving or worsening RSRR prior to study start plus documented efforts to reduce readmissions. Participants: During five-day site visits, three observers conducted semi-structured interviews (n = 314) with staff responsible for care transition processes and observations of care transitions work (n = 105) in inpatient medicine, geriatrics, and primary care. Exposure: Frequency of use of twenty recommended care transition processes, scored 0–3. Sites’ individual process scores and cumulative total scores were tested for correlation with RSRR. Outcome: best fit predicted RSRR for quarter of site visit based on the 21 months surrounding the site visits.ResultsTotal scores: Mean 38.3 (range 24–47). No site performed all 20 processes. Two processes (pre-discharge patient education, medication reconciliation prior to discharge) were performed at all facilities. Five processes were performed at most facilities but inconsistently and the other 13 processes were more varied across facilities. Total care transition process score was correlated with RSRR (R2 = 0..61, p < 0.007).ConclusionsSites making use of more recommended care transition processes had lower RSRR. Given the variability in implementation and barriers noted by clinicians to consistently perform processes, further reduction of readmissions will likely require new strategies to facilitate implementation of these evidence-based processes, should include consideration of how to better incorporate activities into workflow, and may benefit from more consistent use of some of the more underutilized processes including patient inclusion in discharge planning and increased utilization of community supports. Although all facilities had inpatient social workers and/or dedicated case managers working on transitions, many had none or limited true bridging personnel (following the patient from inpatient to home and even providing home visits). More investment in these roles may also be needed.

Association between 30-day readmission rates and health information technology capabilities in US hospitals.

Health information technology (IT) is often proposed as a solution to fragmentation of care, and has been hypothesized to reduce readmission risk through better information flow. However, there are numerous distinct health IT capabilities, and it is unclear which, if any, are associated with lower readmission risk.To identify the specific health IT capabilities adopted by hospitals that are associated with hospital-level risk-standardized readmission rates (RSRRs) through path analyses using structural equation modeling.This STROBE-compliant retrospective cross-sectional study included non-federal U.S. acute care hospitals, based on their adoption of specific types of health IT capabilities self-reported in a 2013 American Hospital Association IT survey as independent variables. The outcome measure included the 2014 RSRRs reported on Hospital Compare website.A 54-indicator 7-factor structure of hospital health IT capabilities was identified by exploratory factor analysis, and corroborated by confirmatory factor analysis. Subsequent path analysis using Structural equation modeling revealed that a one-point increase in the hospital adoption of patient engagement capability latent scores (median path coefficient ß = -0.086; 95% Confidence Interval, -0.162 to -0.008), including functionalities like direct access to the electronic health records, would generally lead to a decrease in RSRRs by 0.086%. However, computerized hospital discharge and information exchange capabilities with other inpatient and outpatient providers were not associated with readmission rates.These findings suggest that improving patient access to and use of their electronic health records may be helpful in improving hospital performance on readmission; however, computerized hospital discharge and information exchange among clinicians did not seem as beneficial - perhaps because of the quality or timeliness of information transmitted. Future research should use more recent data to study, not just adoption of health IT capabilities, but also whether their usage is associated with lower readmission risk. Understanding which capabilities impact readmission risk can help policymakers and clinical stakeholders better focus their scarce resources as they invest in health IT to improve care delivery.

Association of Inclusion of Medicare Advantage Patients in Hospitals’ Risk-Standardized Readmission Rates, Performance, and Penalty Status

The Hospital Readmissions Reduction Program publicly reports and financially penalizes hospitals according to 30-day risk-standardized readmission rates (RSRRs) exclusively among traditional Medicare (TM) beneficiaries but not persons with Medicare Advantage (MA) coverage. Exclusively reporting readmission rates for the TM population may not accurately reflect hospitals' readmission rates for older adults. To examine how inclusion of MA patients in hospitals' performance is associated with readmission measures and eligibility for financial penalties. This is a retrospective cohort study linking the Medicare Provider Analysis and Review file with the Healthcare Effectiveness Data and Information Set at 4070 US acute care hospitals admitting both TM and MA patients. Participants included patients admitted and discharged alive with a diagnosis of acute myocardial infarction (AMI), congestive heart failure (CHF), or pneumonia between 2011 and 2015. Data analyses were conducted between April 1, 2018, and November 20, 2020. Admission to an acute care hospital. The outcome was readmission for any reason occurring within 30 days after discharge. Each hospital's 30-day RSRR was computed on the basis of TM, MA, and all patients and estimated changes in hospitals' performance and eligibility for financial penalties after including MA beneficiaries for calculating 30-day RSRRs. There were 748 033 TM patients (mean [SD] age, 76.8 [83] years; 360 692 [48.2%] women) and 295 928 MA patients (mean [SD] age, 77.5 [7.9] years; 137 422 [46.4%] women) hospitalized and discharged alive for AMI; 1 327 551 TM patients (mean [SD] age, 81 [8.3] years; 735 855 [55.4%] women) and 457 341 MA patients (mean [SD] age, 79.8 [8.1] years; 243 503 [53.2%] women) for CHF; and 2 017 020 TM patients (mean [SD] age, 80.7 [8.5] years; 1 097 151 [54.4%] women) and 610 790 MA patients (mean [SD] age, 79.6 [8.2] years; 321 350 [52.6%] women) for pneumonia. The 30-day RSRRs for TM and MA patients were correlated (correlation coefficients, 0.31 for AMI, 0.40 for CHF, and 0.41 for pneumonia) and the TM-based RSRR systematically underestimated the RSRR for all Medicare patients for each condition. Of the 2820 hospitals with 25 or more admissions for at least 1 of the outcomes of AMI, CHF, and pneumonia, 635 (23%) had a change in their penalty status for at least 1 of these conditions after including MA data. Changes in hospital performance and penalty status with the inclusion of MA patients were greater for hospitals in the highest quartile of MA admissions. In this cohort study, the inclusion of data from MA patients changed the penalty status of a substantial fraction of US hospitals for at least 1 of 3 reported conditions. This suggests that policy makers should consider including all hospital patients, regardless of insurance status, when assessing hospital quality measures.

828. Evaluation of Home Time as a Patient-Centered Metric for Pneumonia Hospitalizations: A Retrospective Cohort Study of Medicare Fee-For-Service Beneficiaries

BackgroundThe Centers for Medicare & Medicaid Services (CMS) uses hospital readmission to incentivize hospital care delivery for acute conditions including pneumonia. However, current CMS performance metrics do not account for the competing risk of mortality in the post-discharge period or during the hospital stay. Our objective was to assess home time within 30 days after discharge among pneumonia hospitalizations, as a patient-centered metric.MethodsA retrospective observational study was conducted in a cohort of Medicare fee-for-service beneficiaries admitted between 01/01/2015 and 11/30/2017. Home time was the number of days spent alive, out of an acute care setting, skilled nursing facility, or a rehabilitation facility within 30 days of discharge. If a patient spends any part of a day in a care facility or died after discharge, then that day was not included in the calculation for home time. Hospital-level rates of risk-adjusted home time were calculated using multilevel regression models. We compared hospital performance on 30-day risk-standardized home time with its performance on 30-day risk standardized readmission rate (RSRR) and mortality rate (RSMR). Characteristics of hospitals with high and low risk-adjusted home-time were compared.ResultsAmong 1.7 million pneumonia admissions admitted to 3,116 hospitals, the median 30-day risk-standardized home time was 20.5 days (interquartile range: 18.9-21.9 days). Hospital-level characteristics such as case volume, bed size, for-profit ownership, rural location of hospital, teaching status, and participation in the bundle payment program were significantly associated with home-time. RSRR (rho: -0.233, p< 0.0001) and RSMR (rho: -0.223, p< 0.0001) had weak, inverse correlations with home time. Using the home time metric, 35.5% of hospitals were reclassified as high performers compared with their average or poor performance on the RSRR or RSMR metric.ConclusionHome time is a novel, patient-centered, hospital-level metric that can be easily calculated using claims data, accounts for differences in post-discharge mortality and can be intuitively interpreted. Utilization of this metric could potentially have policy implications in assessing hospital performance on delivery of healthcare to pneumonia patients.DisclosuresRajeshwari Nair, PhD, Merck and Company, Inc. (Research Grant or Support)

Evaluation of Risk-Adjusted Home Time After Hospitalization for Heart Failure as a Potential Hospital Performance Metric

Thirty-day home time, defined as time spent alive and out of a hospital or facility, is a novel, patient-centered performance metric that incorporates readmission and mortality. To characterize risk-adjusted 30-day home time in patients discharged with heart failure (HF) as a hospital-level quality metric and evaluate its association with the 30-day risk-standardized readmission rate (RSRR), 30-day risk-standardized mortality rate (RSMR), and 1-year RSMR. This hospital-level cohort study retrospectively analyzed 100% of Medicare claims data from 2 968 341 patients from 3134 facilities from January 1, 2012, to November 30, 2017. Home time, defined as time spent alive and out of a short-term hospital, skilled nursing facility, or intermediate/long-term facility 30 days after discharge. For each hospital, a risk-adjusted 30-day home time for HF was calculated similar to the Centers for Medicare & Medicaid Services risk-adjustment models for 30-day RSRR and RSMR. Hospitals were categorized into quartiles (lowest to highest risk-adjusted home time). The correlations between hospital rates of risk-adjusted 30-day home time and 30-day RSRR, 30-day RSMR, and 1-year RSMR were estimated using the Pearson correlation coefficient. Distribution of days lost from a perfect 30-day home time were calculated. Reclassification of hospital performance using 30-day home time vs 30-day RSRR was also evaluated. Overall, 2 968 341 patients (mean [SD] age, 81.0 [8.3] years; 53.6% female) from 3134 hospitals were included in this study. The median hospital risk-adjusted 30-day home time for patients with HF was 21.77 days (range, 8.22-28.41 days). Hospitals in the highest quartile of risk-adjusted 30-day home time (best-performing hospitals) were larger (mean [SD] number of beds, 285 [275]), with a higher volume of patients with HF (median, 797 patients; interquartile range, 395-1484) and were more likely academic hospitals (59.9%) with availability of cardiac surgery (51.1%) and cardiac rehabilitation (68.8%). A total of 72% of home time lost was attributable to stays in an intermediate- or long-term care facility (mean [SD], 2.65 [6.44] days) or skilled nursing facility (mean [SD], 3.96 [9.04] days), 13% was attributable to short-term readmissions (mean [SD], 1.25 [3.25] days), and 15% was attributable to death (mean [SD], 1.37 [6.04] days). Among 30-day outcomes, the 30-day RSRR and 30-day RSMR decreased in a graded fashion across increasing 30-day home time categories (correlation coefficients: 30-day RSRR and 30-day home time, -0.23, P < .001; 30-day RSMR and 30-day home time, -0.31, P < .001). Similar patterns of association were also noted for 1-year RSMR and 30-day home time (correlation coefficient, -0.35, P < .001). Thirty-day home time meaningfully reclassified hospital performance in 30% of the hospitals compared with 30-day RSRR and in 25% of hospitals compared with 30-day RSMR. In this study, 30-day home time among patients discharged after a hospitalization for HF was objectively assessed as a hospital-level quality metric using Medicare claims data and was associated with readmission and mortality outcomes and with reclassification of hospital performance compared with 30-day RSRR and 30-day RSMR.