Measuring Equity in Readmission as a Distinct Assessment of Hospital Performance

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.

Adjustment for Social Risk Factors in a Measure of Clinician Quality Assessing Acute Admissions for Patients With Multiple Chronic Conditions

Importance

Adjusting quality measures used in pay-for-performance programs for social risk factors remains controversial.

Objective

To illustrate a structured, transparent approach to decision-making about adjustment for social risk factors for a measure of clinician quality that assesses acute admissions for patients with multiple chronic conditions (MCCs).

Design, Setting, and Participants

This retrospective cohort study used 2017 and 2018 Medicare administrative claims and enrollment data, 2013 to 2017 American Community Survey data, and 2018 and 2019 Area Health Resource Files. Patients were Medicare fee-for-service beneficiaries 65 years or older with at least 2 of 9 chronic conditions (acute myocardial infarction, Alzheimer disease/dementia, atrial fibrillation, chronic kidney disease, chronic obstructive pulmonary disease or asthma, depression, diabetes, heart failure, and stroke/transient ischemic attack). Patients were attributed to clinicians in the Merit-Based Incentive Payment System (MIPS; primary health care professionals or specialists) using a visit-based attribution algorithm. Analyses were conducted between September 30, 2017, and August 30, 2020.

Exposures

Social risk factors included low Agency for Healthcare Research and Quality Socioeconomic Status Index, low physician-specialist density, and Medicare-Medicaid dual eligibility.

Main Outcomes and Measures

Number of acute unplanned hospital admissions per 100 person-years at risk for admission. Measure scores were calculated for MIPS clinicians with at least 18 patients with MCCs assigned to them.

Results

There were 4 659 922 patients with MCCs (mean [SD] age, 79.0 [8.0] years; 42.5% male) assigned to 58 435 MIPS clinicians. The median (IQR) risk-standardized measure score was 38.9 (34.9-43.6) per 100 person-years. Social risk factors of low Agency for Healthcare Research and Quality Socioeconomic Status Index, low physician-specialist density, and Medicare-Medicaid dual eligibility were significantly associated with the risk of hospitalization in the univariate models (relative risk [RR], 1.14 [95% CI, 1.13-1.14], RR, 1.05 [95% CI, 1.04-1.06], and RR, 1.44 [95% CI, 1.43-1.45], respectively), but the association was attenuated in adjusted models (RR, 1.11 [95% CI 1.11-1.12] for dual eligibility). Across MIPS clinicians caring for variable proportions of dual-eligible patients with MCCs (quartile 1, 0%-3.1%; quartile 2, >3.1%-9.5%; quartile 3, >9.5%-24.5%, and quartile 4, >24.5%-100%), median measure scores per quartile were 37.4, 38.6, 40.0, and 39.8 per 100 person-years, respectively. Balancing conceptual considerations, empirical findings, programmatic structure, and stakeholder input, the Centers for Medicare & Medicaid Services decided to adjust the final model for the 2 area-level social risk factors but not dual Medicare-Medicaid eligibility.

Conclusions and Relevance

This cohort study demonstrated that adjustment for social risk factors in outcome measures requires weighing high-stake, competing concerns. A structured approach that includes evaluation of conceptual and contextual factors, as well as empirical findings, with active engagement of stakeholders can be used to make decisions about social risk factor adjustment.

Identifying high-value care for Medicare beneficiaries: a cross-sectional study of acute care hospitals in the USA

OBJECTIVES

High-value care is providing high quality care at low cost; we sought to define hospital value and identify the characteristics of hospitals which provide high-value care.

DESIGN

Retrospective observational study.

SETTING

Acute care hospitals in the USA.

PARTICIPANTS

All Medicare beneficiaries with claims included in Center for Medicare & Medicaid Services Overall Star Ratings or in publicly available Medicare spending per beneficiary data.

PRIMARY AND SECONDARY OUTCOME MEASURES

Our primary outcome was value defined as the difference between Star Ratings quality score and Medicare spending; the secondary outcome was classification as a 4 or 5 star hospital with lowest quintile Medicare spending ('high value') or 1 or 2 star hospital with highest quintile spending ('low value').

RESULTS

Two thousand nine hundred and fourteen hospitals had both quality and spending data, and were included. The value score had a mean (SD) of 0.58 (1.79). A total of 286 hospitals were classified as high value; these represented 28.6% of 999 4 and 5 star hospitals and 46.8% of 611 low cost hospitals. A total of 258 hospitals were classified as low value; these represented 26.6% of 970 1 and 2 star hospitals and 49.3% of 523 high cost hospitals. In regression models ownership, non-teaching status, beds, urbanity, nurse to bed ratio, percentage of dual eligible Medicare patients and percentage of disproportionate share hospital payments were associated with the primary value score.

CONCLUSIONS

There are high quality hospitals that are not high value, and a number of factors are strongly associated with being low or high value. These findings can inform efforts of policymakers and hospitals to increase the value of care.

The 2018 Merit-based Incentive Payment System: Participation, Performance, and Payment Across Specialties

BACKGROUND

The Merit-based Incentive Payment System (MIPS) incorporates financial incentives and penalties intended to drive clinicians towards value-based purchasing, including alternative payment models (APMs). Newly available Medicare-approved qualified clinical data registries (QCDRs) offer specialty-specific quality measures for clinician reporting, yet their impact on clinician performance and payment adjustments remains unknown.

OBJECTIVES

We sought to characterize clinician participation, performance, and payment adjustments in the MIPS program across specialties, with a focus on clinician use of QCDRs.

RESEARCH DESIGN

We performed a cross-sectional analysis of the 2018 MIPS program.

RESULTS

During the 2018 performance year, 558,296 clinicians participated in the MIPS program across the 35 specialties assessed. Clinicians reporting as individuals had lower overall MIPS performance scores (median [interquartile range (IQR)], 80.0 [39.4-98.4] points) than those reporting as groups (median [IQR], 96.3 [76.9-100.0] points), who in turn had lower adjustments than clinicians reporting within MIPS APMs (median [IQR], 100.0 [100.0-100.0] points) (P<0.001). Clinicians reporting as individuals had lower payment adjustments (median [IQR], +0.7% [0.1%-1.6%]) than those reporting as groups (median [IQR], +1.5% [0.6%-1.7%]), who in turn had lower adjustments than clinicians reporting within MIPS APMs (median [IQR], +1.7% [1.7%-1.7%]) (P<0.001). Within a subpopulation of 202,685 clinicians across 12 specialties commonly using QCDRs, clinicians had overall MIPS performance scores and payment adjustments that were significantly greater if reporting at least 1 QCDR measure compared with those not reporting any QCDR measures.

CONCLUSIONS

Collectively, these findings highlight that performance score and payment adjustments varied by reporting affiliation and QCDR use in the 2018 MIPS.

Identification of Hospitals That Care for a High Proportion of Patients With Social Risk Factors

Question

Are hospitals that care for a high proportion of patients with social risk factors consistently identified using varied definitions of these factors?

Findings

In this cross-sectional study, among 4465 US hospitals qualified for Centers for Medicare & Medicaid Services hospital performance measures, one-third were identified as caring for a high proportion of patients with social risk factors across 7 definitions of social risk; fewer than 1% met all 7 definitions. Most hospitals serving patients with social risk factors could be identified using 3 or 4 definitions.

Meaning

Inconsistencies in identifying hospitals caring for high proportions of patients with social risk factors suggest value in developing a common definition of social risk.

Importance

Hospitals can face significant clinical and financial challenges in caring for patients with social risk factors. Currently the Hospital Readmission Reduction Program stratifies hospitals by proportion of patients eligible for both Medicare and Medicaid when calculating payment penalties to account for the patient population. However, additional social risk factors should be considered.

Objective

To evaluate 7 different definitions of social risk and understand the degree to which differing definitions identify the same hospitals caring for a high proportion of patients with social risk factors.

Design, Setting, and Participants

Across 18 publicly reported Centers for Medicare & Medicaid Services (CMS) hospital performance measures, highly disadvantaged hospitals were identified by the the proportion of patients with social risk factors using the following 7 commonly used definitions of social risk: living below the US poverty line, educational attainment of less than high school, unemployment, living in a crowded household, African American race (as a proxy for the social risk factor of exposure to racism), Medicaid coverage, and Agency for Healthcare Research and Quality index of socioeconomic status score. In this cross-sectional study, social risk factors were evaluated by measure because hospitals may serve a disadvantaged patient population for one measure but not another. Data were collected from April 1, 2014, to June 30, 2017, and analyzed from July 25, 2019, to April 25, 2021.

Main Outcomes and Measures

The proportion of hospitals identified as caring for patients with social risk factors using 7 definitions of social risk, across 18 publicly reported CMS hospital performance measures.

Results

Among 4465 hospitals, a mean of 31.0% (range, 28.9%-32.3%) were identified at least once when using the 7 definitions of social risk as caring for a high proportion of patients with social risk factors. Among all hospitals meeting at least 1 definition of social risk, a mean of 0.7% (range, 0%-1.0%) were identified as highly disadvantaged by all 7 definitions. Among hospitals meeting at least 1 definition of social risk, a mean of 2.7% (range, 1.3%-5.1%) were identified by 6 definitions; 6.5% (range, 5.9%-7.1%), by 5 definitions; 10.4% (range, 9.5%-12.1%), by 4 definitions; 13.2% (range, 10.1%-14.4%), by 3 definitions; 21.4% (range, 20.1%-22.4%), by 2 definitions; and 45.2% (range, 42.6%-47.1%), by only 1 definition. This pattern was consistent across all 18 performance measures.

Conclusions and Relevance

In this cross-sectional study, there were inconsistencies in the identification of hospitals caring for disadvantaged populations using different definitions of social risk factors. Without consensus on how to define disadvantaged hospitals, policies to support such hospitals may be applied inconsistently.

Incorporating Present-on-Admission Indicators in Medicare Claims to Inform Hospital Quality Measure Risk Adjustment Models

Importance

Present-on-admission (POA) indicators in administrative claims data allow researchers to distinguish between preexisting conditions and those acquired during a hospital stay. The impact of adding POA information to claims-based measures of hospital quality has not yet been investigated to better understand patient underlying risk factors in the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision setting.

Objective

To assess POA indicator use on Medicare claims and to assess the hospital- and patient-level outcomes associated with incorporating POA indicators in identifying risk factors for publicly reported outcome measures used by the Centers for Medicare & Medicaid Services (CMS).

Design, Setting, and Participants

This comparative effectiveness study used national CMS claims data between July 1, 2015, and June 30, 2018. Six hospital quality measures assessing readmission and mortality outcomes were modified to include POA indicators in risk adjustment models. The models using POA were then compared with models using the existing complications-of-care algorithm to evaluate changes in risk model performance. Patient claims data were included for all Medicare fee-for-service and Veterans Administration beneficiaries aged 65 years or older with inpatient hospitalizations for acute myocardial infarction, heart failure, or pneumonia within the measurement period. Data were analyzed between September 2019 and March 2020.

Main Outcomes and Measures

Changes in patient-level (C statistics) and hospital-level (quintile shifts in risk-standardized outcome rates) model performance after including POA indicators in risk adjustment.

Results

Data from a total of 6 027 988 index admissions were included for analysis, ranging from 491 366 admissions (269 209 [54.8%] men; mean [SD] age, 78.2 [8.3] years) for the acute myocardial infarction mortality outcome measure to 1 395 870 admissions (677 158 [48.5%] men; mean [SD] age, 80.3 [8.7] years) for the pneumonia readmission measure. Use of POA indicators was associated with improvements in risk adjustment model performance, particularly for mortality measures (eg, the C statistic increased from 0.728 [95% CI, 0.726-0.730] to 0.774 [95% CI, 0.773-0.776] when incorporating POA indicators into the acute myocardial infarction mortality measure).

Conclusions and Relevance

The findings of this quality improvement study suggest that leveraging POA indicators in the risk adjustment methodology for hospital quality outcome measures may help to more fully capture patients' risk factors and improve overall model performance. Incorporating POA indicators does not require extra effort on the part of hospitals and would be easy to implement in publicly reported quality outcome measures.

Administrative Claims Measure for Profiling Hospital Performance Based on 90-Day All-Cause Mortality Following Coronary Artery Bypass Graft Surgery

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.

Community factors and hospital wide readmission rates: Does context matter?

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.

Quality Measure Public Reporting Is Associated with Improved Outcomes Following Hip and Knee Replacement

BACKGROUND

Given the inclusion of orthopaedic quality measures in the Centers for Medicare & Medicaid Services national hospital payment programs, the present study sought to assess whether the public reporting of total hip arthroplasty (THA) and total knee arthroplasty (TKA) risk-standardized readmission rates (RSRRs) and complication rates (RSCRs) was temporally associated with a decrease in the rates of these outcomes among Medicare beneficiaries.

METHODS

Annual trends in national observed and hospital-level RSRRs and RSCRs were evaluated for patients who underwent hospital-based inpatient hip and/or knee replacement procedures from fiscal year 2010 to fiscal year 2016. Hospital-level rates were calculated with use of the same measures and methodology that were utilized in public reporting. Annual trends in the distribution of hospital-level outcomes were then examined with use of density plots.

RESULTS

Complication and readmission rates and variation declined steadily from fiscal year 2010 to fiscal year 2016. Reductions of 33% and 25% were noted in hospital-level RSCRs and RSRRs, respectively. The interquartile range decreased by 18% (relative reduction) for RSCRs and by 34% (relative reduction) for RSRRs. The frequency of risk variables in the complication and readmission models did not systematically change over time, suggesting no evidence of widespread bias or up-coding.

CONCLUSIONS

This study showed that hospital-level complication and readmission rates following THA and TKA and the variation in hospital-level performance declined during a period coinciding with the start of public reporting and financial incentives associated with measurement. The consistently decreasing trend in rates of and variation in outcomes suggests steady improvements and greater consistency among hospitals in clinical outcomes for THA and TKA patients in the 2016 fiscal year compared with the 2010 fiscal year. The interactions between public reporting, payment, and hospital coding practices are complex and require further study.

LEVEL OF EVIDENCE

Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Timely estimation of National Admission, readmission, and observation-stay rates in medicare patients with acute myocardial infarction, heart failure, or pneumonia using near real-time claims data

Background

To estimate, prior to finalization of claims, the national monthly numbers of admissions and rates of 30-day readmissions and post-discharge observation-stays for Medicare fee-for-service beneficiaries hospitalized with acute myocardial infarction (AMI), heart failure (HF), or pneumonia.

Methods

The centers for Medicare & Medicaid Services (CMS) Integrated Data Repository, including the Medicare beneficiary enrollment database, was accessed in June 2015, February 2017, and February 2018. We evaluated patterns of delay in Medicare claims accrual, and used incomplete, non-final claims data to develop and validate models for real-time estimation of admissions, readmissions, and observation stays.

Results

These real-time reporting models accurately estimate, within 2 months from admission, the monthly numbers of admissions, 30-day readmission and observation-stay rates for patients with AMI, HF, or pneumonia.

Conclusions

This work will allow CMS to track the impact of policy decisions in real time and enable hospitals to better monitor their performance nationally.

An instrument for assessing the quality of informed consent documents for elective procedures: development and testing

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.

Quality of informed consent documents among US. hospitals: a cross-sectional study

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.

Association Between Medicare Expenditures and Adverse Events for Patients With Acute Myocardial Infarction, Heart Failure, or Pneumonia in the United States

IMPORTANCE

Studies have shown that adverse events are associated with increasing inpatient care expenditures, but contemporary data on the association between expenditures and adverse events beyond inpatient care are limited.

OBJECTIVE

To evaluate whether hospital-specific adverse event rates are associated with hospital-specific risk-standardized 30-day episode-of-care Medicare expenditures for fee-for-service patients discharged with acute myocardial infarction (AMI), heart failure (HF), or pneumonia.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study used the 2011 to 2016 hospital-specific risk-standardized 30-day episode-of-care expenditure data from the Centers for Medicare & Medicaid Services and medical record-abstracted in-hospital adverse event data from the Medicare Patient Safety Monitoring System. The setting was acute care hospitals treating at least 25 Medicare fee-for-service patients for AMI, HF, or pneumonia in the United States. Participants were Medicare fee-for-service patients 65 years or older hospitalized for AMI, HF, or pneumonia included in the Medicare Patient Safety Monitoring System in 2011 to 2016. The dates of analysis were July 16, 2017, to May 21, 2018.

MAIN OUTCOMES AND MEASURES

Hospitals' risk-standardized 30-day episode-of-care expenditures and the rate of occurrence of adverse events for which patients were at risk.

RESULTS

The final study sample from 2194 unique hospitals included 44 807 patients (26.1% AMI, 35.6% HF, and 38.3% pneumonia) with a mean (SD) age of 79.4 (8.6) years, and 52.0% were women. The patients represented 84 766 exposures for AMI, 96 917 exposures for HF, and 109 641 exposures for pneumonia. Patient characteristics varied by condition but not by expenditure category. The mean (SD) risk-standardized expenditures were $22 985 ($1579) for AMI, $16 020 ($1416) for HF, and $16 355 ($1995) for pneumonia per hospitalization. The mean risk-standardized rates of occurrence of adverse events for which patients were at risk were 3.5% (95% CI, 3.4%-3.6%) for AMI, 2.5% (95% CI, 2.5%-2.5%) for HF, and 3.0% (95% CI, 2.9%-3.0%) for pneumonia. An increase by 1 percentage point in the rate of occurrence of adverse events was associated with an increase in risk-standardized expenditures of $103 (95% CI, $57-$150) for AMI, $100 (95% CI, $29-$172) for HF, and $152 (95% CI, $73-$232) for pneumonia per discharge.

CONCLUSIONS AND RELEVANCE

Hospitals with high adverse event rates were more likely to have high 30-day episode-of-care Medicare expenditures for patients discharged with AMI, HF, or pneumonia.

Impact of left ventricular assist devices and heart transplants on acute myocardial infarction and heart failure mortality and readmission measures

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.

Post-discharge acute care and outcomes following readmission reduction initiatives: national retrospective cohort study of Medicare beneficiaries in the United States

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.

Association of Hospital Payment Profiles With Variation in 30-Day Medicare Cost for Inpatients With Heart Failure or Pneumonia

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 < .001) lower for heart failure and $2907 (95% CI, $1760-$4054; P < .001) lower for pneumonia than at the highest-payment hospitals. More than half of the difference was associated with the payment during the index hospitalization ($1425 [95% CI, $695-$2154; P < .001] for heart failure and $1659 [95% CI, $731-$2588; P < .001] for pneumonia).

CONCLUSIONS AND RELEVANCE

This study found that the same Medicare beneficiaries who were admitted with the same diagnosis to hospitals with the highest payment profiles incurred higher costs than when they were admitted to hospitals with the lowest payment profiles. The findings suggest that variations in payments to hospitals are, at least in part, associated with the hospitals independently of non-time-varying patient characteristics.

Development and Testing of Improved Models to Predict Payment Using Centers for Medicare & Medicaid Services Claims Data

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.

Comparative Effectiveness of New Approaches to Improve Mortality Risk Models From Medicare Claims Data

IMPORTANCE

Risk adjustment models using claims-based data are central in evaluating health care performance. Although US Centers for Medicare & Medicaid Services (CMS) models apply well-vetted statistical approaches, recent changes in the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) coding system and advances in computational capabilities may provide an opportunity for enhancement.

OBJECTIVE

To examine whether changes using already available data would enhance risk models and yield greater discrimination in hospital-level performance measures.

DESIGN, SETTING, AND PARTICIPANTS

This comparative effectiveness study used ICD-9-CM codes from all Medicare fee-for-service beneficiary claims for hospitalizations for acute myocardial infarction (AMI), heart failure (HF), or pneumonia among patients 65 years and older from July 1, 2013, through September 30, 2015. Changes to current CMS mortality risk models were applied incrementally to patient-level models, and the best model was tested on hospital performance measures to model 30-day mortality. Analyses were conducted from April 19, 2018, to September 19, 2018.

MAIN OUTCOMES AND MEASURES

The main outcome was all-cause death within 30 days of hospitalization for AMI, HF, or pneumonia, examined using 3 changes to current CMS mortality risk models: (1) incorporating present on admission coding to better exclude potential complications of care, (2) separating index admission diagnoses from those of the 12-month history, and (3) using ungrouped ICD-9-CM codes.

RESULTS

There were 361 175 hospital admissions (mean [SD] age, 78.6 [8.4] years; 189 225 [52.4%] men) for AMI, 716 790 hospital admissions (mean [SD] age, 81.1 [8.4] years; 326 825 [45.6%] men) for HF, and 988 225 hospital admissions (mean [SD] age, 80.7 [8.6] years; 460 761 [46.6%] men) for pneumonia during the study; mean 30-day mortality rates were 13.8% for AMI, 12.1% for HF, and 16.1% for pneumonia. Each change to the models was associated with incremental gains in C statistics. The best model, incorporating all changes, was associated with significantly improved patient-level C statistics, from 0.720 to 0.826 for AMI, 0.685 to 0.776 for HF, and 0.715 to 0.804 for pneumonia. Compared with current CMS models, the best model produced wider predicted probabilities with better calibration and Brier scores. Hospital risk-standardized mortality rates had wider distributions, with more hospitals identified as good or bad performance outliers.

CONCLUSIONS AND RELEVANCE

Incorporating present on admission coding and using ungrouped index and historical ICD-9-CM codes were associated with improved patient-level and hospital-level risk models for mortality compared with the current CMS models for all 3 conditions.

Promoting Health Equity And Eliminating Disparities Through Performance Measurement And Payment

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

Trends in 30-Day Readmission Rates for Medicare and Non-Medicare Patients in the Era of the Affordable Care Act

BACKGROUND

Temporal changes in the readmission rates for patient groups and conditions that were not directly under the purview of the Hospital Readmissions Reduction Program (HRRP) can help assess whether efforts to lower readmissions extended beyond targeted patients and conditions.

METHODS

Using the Nationwide Readmissions Database (2010-2015), we assessed trends in all-cause readmission rates for 1 of the 3 HRRP conditions (acute myocardial infarction, heart failure, pneumonia) or conditions not targeted by the HRRP in age-insurance groups defined by age group (≥65 years or <65 years) and payer (Medicare, Medicaid, or private insurance).

RESULTS

In the group aged ≥65 years, readmission rates for those covered by Medicare, Medicaid, and private insurance decreased annually for acute myocardial infarction (risk-adjusted odds ratio [OR; 95% confidence interval] among Medicare patients, 0.94 [0.94-0.95], among Medicaid patients, 0.93 [0.90-0.97], and among patients with private-insurance, 0.95 [0.93-0.97]); heart failure (ORs, 0.96 [0.96-0.97], 0.96 [0.94-0.98], and 0.97 [0.96-0.99], for the 3 payers, respectively), and pneumonia (ORs, 0.96 [0.96-0.97), 0.94 [0.92-0.96], and 0.96 [0.95-0.97], respectively). Readmission rates also decreased in the group aged <65 years for acute myocardial infarction (ORs: Medicare 0.97 [0.96-0.98], Medicaid 0.94 [0.92-0.95], and private insurance 0.93 [0.92-0.94]), heart failure (ORs, 0.98 [0.97-0.98]: 0.96 [0.96-0.97], and 0.97 [0.95-0.98], for the 3 payers, respectively), and pneumonia (ORs, 0.98 [0.97-0.99], 0.98 [0.97-0.99], and 0.98 [0.97-1.00], respectively). Further, readmission rates decreased significantly for non-target conditions.

CONCLUSIONS

There appears to be a systematic improvement in readmission rates for patient groups beyond the population of fee-for-service, older, Medicare beneficiaries included in the HRRP.

Variation in and Hospital Characteristics Associated With the Value of Care for Medicare Beneficiaries With Acute Myocardial Infarction, Heart Failure, and Pneumonia

IMPORTANCE

Payers and policy makers have advocated for transitioning toward value-based payment models. However, little is known about what is the extent of hospital variation in the value of care and whether there are any hospital characteristics associated with high-value care.

OBJECTIVES

To investigate the association between hospital-level 30-day risk-standardized mortality rates (RSMRs) and 30-day risk-standardized payments (RSPs) for acute myocardial infarction (AMI), heart failure (HF), and pneumonia (PNA); to characterize patterns of value in care; and to identify hospital characteristics associated with high-value care (defined by having lower than median RSMRs and RSPs).

DESIGN, SETTING, AND PARTICIPANTS

This national cross-sectional study applied weighted linear correlation to investigate the association between hospital RSMRs and RSPs for AMI, HF, and PNA between July 1, 2011, and June 30, 2014, among all hospitals; examined correlations in subgroups of hospitals based on key characteristics; and assessed the proportion and characteristics of hospitals delivering high-value care. The data analysis was completed in October 2017. The setting was acute care hospitals. Participants were Medicare fee-for-service beneficiaries discharged with AMI, HF, or PNA.

MAIN OUTCOMES AND MEASURES

Hospital-level 30-day RSMRs and RSPs for AMI, HF, and PNA.

RESULTS

The AMI sample consisted of 4339 hospitals with 487 141 hospitalizations for mortality and 462 905 hospitalizations for payment. The HF sample included 4641 hospitals with 960 960 hospitalizations for mortality and 903 721 hospitalizations for payment. The PNA sample contained 4685 hospitals with 952 022 hospitalizations for mortality and 901 764 hospitalizations for payment. The median (interquartile range [IQR]) RSMRs and RSPs, respectively, was 14.3% (IQR, 13.8%-14.8%) and $21 620 (IQR, $20 966-$22 567) for AMI, 11.7% (IQR, 11.0%-12.5%) and $15 139 (IQR, $14 310-$16 118) for HF, and 11.5% (IQR, 10.6%-12.6%) and $14 220 (IQR, $13 342-$15 097) for PNA. There were statistically significant but weak inverse correlations between the RSMRs and RSPs of -0.08 (95% CI, -0.11 to -0.05) for AMI, -0.21 (95% CI, -0.24 to -0.18) for HF, and -0.07 (95% CI, -0.09 to -0.04) for PNA. The largest shared variance between the RSMRs and RSPs was only 4.4% (for HF). The correlations between the RSMRs and RSPs did not differ significantly across teaching status, safety-net status, urban/rural status, or the proportion of patients with low socioeconomic status. Approximately 1 in 4 hospitals (20.9% for AMI, 23.0% for HF, and 23.9% for PNA) had both lower than median RSMRs and RSPs.

CONCLUSIONS AND RELEVANCE

These findings suggest that there is significant potential for improvement in the value of AMI, HF, and PNA care and also suggest that high-value care for these conditions is attainable across most hospital types.

Association of the Hospital Readmissions Reduction Program With Mortality During and After Hospitalization for Acute Myocardial Infarction, Heart Failure, and Pneumonia

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 < .001), but increased from 7.4% to 9.2% for HF (P for trend < .001) and from 7.6% to 8.6% for pneumonia (P for trend < .001). Before the HRRP announcement, monthly postdischarge mortality was stable for AMI (slope for monthly change, 0.002%; 95% CI, -0.001% to 0.006% per month), and increased by 0.004% (95% CI, 0.000% to 0.007%) per month for HF and by 0.005% (95% CI, 0.002% to 0.008%) per month for pneumonia. There were no inflections in slope around HRRP announcement or implementation (P > .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.

Association of Racial and Socioeconomic Disparities With Outcomes Among Patients Hospitalized With Acute Myocardial Infarction, Heart Failure, and Pneumonia: An Analysis of Within- and Between-Hospital Variation

IMPORTANCE

Although studies have described differences in hospital outcomes by patient race and socioeconomic status, it is not clear whether such disparities are driven by hospitals themselves or by broader systemic effects.

OBJECTIVE

To determine patterns of racial and socioeconomic disparities in outcomes within and between hospitals for patients with acute myocardial infarction, heart failure, and pneumonia.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective cohort study initiated before February 2013, with additional analyses conducted during the peer-review process. Hospitals in the United States treating at least 25 Medicare fee-for-service beneficiaries aged 65 years or older in each race (ie, black and white) and neighborhood income level (ie, higher income and lower income) for acute myocardial infarction, heart failure, and pneumonia between 2009 and 2011 were included.

MAIN OUTCOMES AND MEASURES

For within-hospital analyses, risk-standardized mortality rates and risk-standardized readmission rates for race and neighborhood income subgroups were calculated at each hospital. The corresponding ratios using intraclass correlation coefficients were then compared. For between-hospital analyses, risk-standardized rates were assessed according to hospitals' proportion of patients in each subgroup. These analyses were performed for each of the 12 analysis cohorts reflecting the unique combinations of outcomes (mortality and readmission), demographics (race and neighborhood income), and conditions (acute myocardial infarction, heart failure, and pneumonia).

RESULTS

Between 74% (3545 of 4810) and 91% (4136 of 4554) of US hospitals lacked sufficient racial and socioeconomic diversity to be included in this analysis, with the number of hospitals eligible for analysis varying among cohorts. The 12 analysis cohorts ranged in size from 418 to 1265 hospitals and from 144 417 to 703 324 patients. Within included hospitals, risk-standardized mortality rates tended to be lower among black patients (mean [SD] difference between risk-standardized mortality rates in black patients compared with white patients for acute myocardial infarction, -0.57 [1.1] [P = .47]; for heart failure, -4.7 [1.3] [P < .001]; and for pneumonia, -1.0 [2.0] [P = .05]). However, risk-standardized readmission rates among black patients were higher (mean [SD] difference between risk-standardized readmission rates in black patients compared with white patients for acute myocardial infarction, 4.3 [1.4] [P < .001]; for heart failure, 2.8 [1.8] [P < .001], and for pneumonia, 3.7 [1.3] [P < .001]). Intraclass correlation coefficients ranged from 0.68 to 0.79, indicating that hospitals generally delivered consistent quality to patients of differing races. While the coefficients in the neighborhood income analysis were slightly lower (0.46-0.60), indicating some heterogeneity in within-hospital performance, differences in mortality rates and readmission rates between the 2 neighborhood income groups were small. There were no strong, consistent associations between risk-standardized outcomes for white or higher-income neighborhood patients and hospitals' proportion of black or lower-income neighborhood patients.

CONCLUSIONS AND RELEVANCE

Hospital performance according to race and socioeconomic status was generally consistent within and between hospitals, even as there were overall differences in outcomes by race and neighborhood income. This finding indicates that disparities are likely to be systemic, rather than localized to particular hospitals.

Association Between Postdischarge Emergency Department Visitation and Readmission Rates

BACKGROUND

Hospital readmission rates are publicly reported by the Centers for Medicare & Medicaid Services (CMS); however, the implications of emergency department (ED) visits following hospital discharge on readmissions are uncertain. We describe the frequency, diagnoses, and hospital-level variation in ED visitation following hospital discharge, including the relationship between risk-standardized ED visitation and readmission rates.

METHODS

This is a cross-sectional analysis of Medicare beneficiaries hospitalized for acute myocardial infarction (AMI), heart failure, and pneumonia between July 2011 and June 2012. We used Medicare Standard Analytic Files to identify admissions, readmissions, and ED visits consistent with CMS measures. Postdischarge ED visits were defined as treat-and-discharge ED services within 30 days of hospitalization without readmission. We utilized hierarchical generalized linear models to calculate hospital risk-standardized postdischarge ED visit rates and readmission rates.

RESULTS

We included 157,035 patients hospitalized at 1656 hospitals for AMI, 391,209 at 3044 hospitals for heart failure, and 342,376 at 3484 hospitals for pneumonia. After hospitalization for AMI, heart failure, and pneumonia, there were 14,714 (9%), 31,621 (8%), and 26,681 (8%) ED visits, respectively. Hospital-level variation in postdischarge ED visit rates was substantial: AMI (median: 8.3%; 5th and 95th percentile: 2.8%-14.3%), heart failure (median: 7.3%; 5th and 95th percentile: 3.0%-13.3%), and pneumonia (median: 7.1%; 5th and 95th percentile: 2.4%-13.2%). There was statistically significant inverse correlation between postdischarge ED visit rates and readmission rates: AMI (-0.23), heart failure (-0.29), and pneumonia (-0.18).

CONCLUSIONS

Following hospital discharge, ED treatand- discharge visits are half as common as readmissions for Medicare beneficiaries. There is wide hospital-level variation in postdischarge ED visitation, and hospitals with higher ED visitation rates demonstrated lower readmission rates.

Age Differences in Hospital Mortality for Acute Myocardial Infarction: Implications for Hospital Profiling

BACKGROUND

Publicly reported hospital risk-standardized mortality rates (RSMRs) for acute myocardial infarction (AMI) are calculated for Medicare beneficiaries. Outcomes for older patients with AMI may not reflect general outcomes.

OBJECTIVE

To examine the relationship between hospital 30-day RSMRs for older patients (aged ≥65 years) and those for younger patients (aged 18 to 64 years) and all patients (aged ≥18 years) with AMI.

DESIGN

Retrospective cohort study.

SETTING

986 hospitals in the ACTION (Acute Coronary Treatment and Intervention Outcomes Network) Registry-Get With the Guidelines.

PARTICIPANTS

Adults hospitalized for AMI from 1 October 2010 to 30 September 2014.

MEASUREMENTS

Hospital 30-day RSMRs were calculated for older, younger, and all patients using an electronic health record measure of AMI mortality endorsed by the National Quality Forum. Hospitals were ranked by their 30-day RSMRs for these 3 age groups, and agreement in rankings was plotted. The correlation in hospital AMI achievement scores for each age group was also calculated using the Hospital Value-Based Purchasing (HVBP) Program method computed with the electronic health record measure.

RESULTS

267 763 and 276 031 AMI hospitalizations among older and younger patients, respectively, were identified. Median hospital 30-day RSMRs were 9.4%, 3.0%, and 6.2% for older, younger, and all patients, respectively. Most top- and bottom-performing hospitals for older patients were neither top nor bottom performers for younger patients. In contrast, most top and bottom performers for older patients were also top and bottom performers for all patients. Similarly, HVBP achievement scores for older patients correlated weakly with those for younger patients (R = 0.30) and strongly with those for all patients (R = 0.92).

LIMITATION

Minority of U.S. hospitals.

CONCLUSION

Hospital mortality rankings for older patients with AMI inconsistently reflect rankings for younger patients. Incorporation of younger patients into assessment of hospital outcomes would permit further examination of the presence and effect of age-related quality differences.

PRIMARY FUNDING SOURCE

American College of Cardiology.

Hospital-Readmission Risk - Isolating Hospital Effects from Patient Effects

BACKGROUND

To isolate hospital effects on risk-standardized hospital-readmission rates, we examined readmission outcomes among patients who had multiple admissions for a similar diagnosis at more than one hospital within a given year.

METHODS

We divided the Centers for Medicare and Medicaid Services hospital-wide readmission measure cohort from July 2014 through June 2015 into two random samples. All the patients in the cohort were Medicare recipients who were at least 65 years of age. We used the first sample to calculate the risk-standardized readmission rate within 30 days for each hospital, and we classified hospitals into performance quartiles, with a lower readmission rate indicating better performance (performance-classification sample). The study sample (identified from the second sample) included patients who had two admissions for similar diagnoses at different hospitals that occurred more than 1 month and less than 1 year apart, and we compared the observed readmission rates among patients who had been admitted to hospitals in different performance quartiles.

RESULTS

In the performance-classification sample, the median risk-standardized readmission rate was 15.5% (interquartile range, 15.3 to 15.8). The study sample included 37,508 patients who had two admissions for similar diagnoses at a total of 4272 different hospitals. The observed readmission rate was consistently higher among patients admitted to hospitals in a worse-performing quartile than among those admitted to hospitals in a better-performing quartile, but the only significant difference was observed when the patients were admitted to hospitals in which one was in the best-performing quartile and the other was in the worst-performing quartile (absolute difference in readmission rate, 2.0 percentage points; 95% confidence interval, 0.4 to 3.5; P=0.001).

CONCLUSIONS

When the same patients were admitted with similar diagnoses to hospitals in the best-performing quartile as compared with the worst-performing quartile of hospital readmission performance, there was a significant difference in rates of readmission within 30 days. The findings suggest that hospital quality contributes in part to readmission rates independent of factors involving patients. (Funded by Yale-New Haven Hospital Center for Outcomes Research and Evaluation and others.).

Association of Changing Hospital Readmission Rates With Mortality Rates After Hospital Discharge

IMPORTANCE

The Affordable Care Act has led to US national reductions in hospital 30-day readmission rates for heart failure (HF), acute myocardial infarction (AMI), and pneumonia. Whether readmission reductions have had the unintended consequence of increasing mortality after hospitalization is unknown.

OBJECTIVE

To examine the correlation of paired trends in hospital 30-day readmission rates and hospital 30-day mortality rates after discharge.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective study of Medicare fee-for-service beneficiaries aged 65 years or older hospitalized with HF, AMI, or pneumonia from January 1, 2008, through December 31, 2014.

EXPOSURE

Thirty-day risk-adjusted readmission rate (RARR).

MAIN OUTCOMES AND MEASURES

Thirty-day RARRs and 30-day risk-adjusted mortality rates (RAMRs) after discharge were calculated for each condition in each month at each hospital in 2008 through 2014. Monthly trends in each hospital's 30-day RARRs and 30-day RAMRs after discharge were examined for each condition. The weighted Pearson correlation coefficient was calculated for hospitals' paired monthly trends in 30-day RARRs and 30-day RAMRs after discharge for each condition.

RESULTS

In 2008 through 2014, 2 962 554 hospitalizations for HF, 1 229 939 for AMI, and 2 544 530 for pneumonia were identified at 5016, 4772, and 5057 hospitals, respectively. In January 2008, mean hospital 30-day RARRs and 30-day RAMRs after discharge were 24.6% and 8.4% for HF, 19.3% and 7.6% for AMI, and 18.3% and 8.5% for pneumonia. Hospital 30-day RARRs declined in the aggregate across hospitals from 2008 through 2014; monthly changes in RARRs were -0.053% (95% CI, -0.055% to -0.051%) for HF, -0.044% (95% CI, -0.047% to -0.041%) for AMI, and -0.033% (95% CI, -0.035% to -0.031%) for pneumonia. In contrast, monthly aggregate changes across hospitals in hospital 30-day RAMRs after discharge varied by condition: HF, 0.008% (95% CI, 0.007% to 0.010%); AMI, -0.003% (95% CI, -0.005% to -0.001%); and pneumonia, 0.001% (95% CI, -0.001% to 0.003%). However, correlation coefficients in hospitals' paired monthly changes in 30-day RARRs and 30-day RAMRs after discharge were weakly positive: HF, 0.066 (95% CI, 0.036 to 0.096); AMI, 0.067 (95% CI, 0.027 to 0.106); and pneumonia, 0.108 (95% CI, 0.079 to 0.137). Findings were similar in secondary analyses, including with alternate definitions of hospital mortality.

CONCLUSIONS AND RELEVANCE

Among Medicare fee-for-service beneficiaries hospitalized for heart failure, acute myocardial infarction, or pneumonia, reductions in hospital 30-day readmission rates were weakly but significantly correlated with reductions in hospital 30-day mortality rates after discharge. These findings do not support increasing postdischarge mortality related to reducing hospital readmissions.

Trends in readmission rates for safety net hospitals and non-safety net hospitals in the era of the US Hospital Readmission Reduction Program: a retrospective time series analysis using Medicare administrative claims data from 2008 to 2015

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<0.001) greater among safety net hospitals over the entire study period, and no differential change among safety net and non-safety net hospitals was found after either HRRP was passed or penalties enacted.

CONCLUSIONS

Since HRRP was passed and penalties implemented, readmission rates for safety net hospitals have decreased more rapidly than those for non-safety net hospitals.

Association Between Hospital Penalty Status Under the Hospital Readmission Reduction Program and Readmission Rates for Target and Nontarget Conditions

IMPORTANCE

Readmission rates declined after announcement of the Hospital Readmission Reduction Program (HRRP), which penalizes hospitals for excess readmissions for acute myocardial infarction (AMI), heart failure (HF), and pneumonia.

OBJECTIVE

To compare trends in readmission rates for target and nontarget conditions, stratified by hospital penalty status.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective cohort study of Medicare fee-for-service beneficiaries older than 64 years discharged between January 1, 2008, and June 30, 2015, from 2214 penalty hospitals and 1283 nonpenalty hospitals. Difference-interrupted time-series models were used to compare trends in readmission rates by condition and penalty status.

EXPOSURE

Hospital penalty status or target condition under the HRRP.

MAIN OUTCOMES AND MEASURES

Thirty-day risk adjusted, all-cause unplanned readmission rates for target and nontarget conditions.

RESULTS

The study included 48 137 102 hospitalizations of 20 351 161 Medicare beneficiaries. In January 2008, the mean readmission rates for AMI, HF, pneumonia, and nontarget conditions were 21.9%, 27.5%, 20.1%, and 18.4%, respectively, at hospitals later subject to financial penalties and 18.7%, 24.2%, 17.4%, and 15.7% at hospitals not subject to penalties. Between January 2008 and March 2010, prior to HRRP announcement, readmission rates were stable across hospitals (except AMI at nonpenalty hospitals). Following announcement of HRRP (March 2010), readmission rates for both target and nontarget conditions declined significantly faster for patients at hospitals later subject to financial penalties compared with those at nonpenalized hospitals (for AMI, additional decrease of -1.24 [95% CI, -1.84 to -0.65] percentage points per year relative to nonpenalty discharges; for HF, -1.25 [95% CI, -1.64 to -0.86]; for pneumonia, -1.37 [95% CI, -1.80 to -0.95]; and for nontarget conditions, -0.27 [95% CI, -0.38 to -0.17]; P < .001 for all). For penalty hospitals, readmission rates for target conditions declined significantly faster compared with nontarget conditions (for AMI, additional decline of -0.49 [95% CI, -0.81 to -0.16] percentage points per year relative to nontarget conditions [P = .004]; for HF, -0.90 [95% CI, -1.18 to -0.62; P < .001]; and for pneumonia, -0.57 [95% CI, -0.92 to -0.23; P < .001]). In contrast, among nonpenalty hospitals, readmissions for target conditions declined similarly or more slowly compared with nontarget conditions (for AMI, additional increase of 0.48 [95% CI, 0.01-0.95] percentage points per year [P = .05]; for HF, 0.08 [95% CI, -0.30 to 0.46; P = .67]; for pneumonia, 0.53 [95% CI, 0.13-0.93; P = .01]). After HRRP implementation in October 2012, the rate of change for readmission rates plateaued (P < .05 for all except pneumonia at nonpenalty hospitals), with the greatest relative change observed among hospitals subject to financial penalty.

CONCLUSIONS AND RELEVANCE

Medicare fee-for-service patients at hospitals subject to penalties under the HRRP had greater reductions in readmission rates compared with those at nonpenalized hospitals. Changes were greater for target vs nontarget conditions for patients at the penalized hospitals but not at the other hospitals.

Accounting For Patients' Socioeconomic Status Does Not Change Hospital Readmission Rates

There is an active public debate about whether patients' socioeconomic status should be included in the readmission measures used to determine penalties in Medicare's Hospital Readmissions Reduction Program (HRRP). Using the current Centers for Medicare and Medicaid Services methodology, we compared risk-standardized readmission rates for hospitals caring for high and low proportions of patients of low socioeconomic status (as defined by their Medicaid status or neighborhood income). We then calculated risk-standardized readmission rates after additionally adjusting for patients' socioeconomic status. Our results demonstrate that hospitals caring for large proportions of patients of low socioeconomic status have readmission rates similar to those of other hospitals. Moreover, readmission rates calculated with and without adjustment for patients' socioeconomic status are highly correlated. Readmission rates of hospitals caring for patients of low socioeconomic status changed by approximately 0.1 percent with adjustment for patients' socioeconomic status, and only 3-4 percent fewer such hospitals reached the threshold for payment penalty in Medicare's HRRP. Overall, adjustment for socioeconomic status does not change hospital results in meaningful ways.

Risk-standardized Acute Admission Rates Among Patients With Diabetes and Heart Failure as a Measure of Quality of Accountable Care Organizations: Rationale, Methods, and Early Results

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.

Association of Admission to Veterans Affairs Hospitals vs Non-Veterans Affairs Hospitals With Mortality and Readmission Rates Among Older Men Hospitalized With Acute Myocardial Infarction, Heart Failure, or Pneumonia

IMPORTANCE

Little contemporary information is available about comparative performance between Veterans Affairs (VA) and non-VA hospitals, particularly related to mortality and readmission rates, 2 important outcomes of care.

OBJECTIVE

To assess and compare mortality and readmission rates among men in VA and non-VA hospitals.

DESIGN, SETTING, AND PARTICIPANTS

Cross-sectional analysis involving male Medicare fee-for-service beneficiaries aged 65 years or older hospitalized between 2010 and 2013 in VA and non-VA acute care hospitals for acute myocardial infarction (AMI), heart failure (HF), or pneumonia using the Medicare Standard Analytic Files and Enrollment Database together with VA administrative claims data. To avoid confounding geographic effects with health care system effects, we studied VA and non-VA hospitals within the same metropolitan statistical area (MSA).

EXPOSURES

Hospitalization in a VA or non-VA hospital in MSAs that contained at least 1 VA and non-VA hospital.

MAIN OUTCOMES AND MEASURES

For each condition, 30-day risk-standardized mortality rates and risk-standardized readmission rates for VA and non-VA hospitals. Mean aggregated within-MSA differences in mortality and readmission rates were also assessed.

RESULTS

We studied 104 VA and 1513 non-VA hospitals, with each condition-outcome analysis cohort for VA and non-VA hospitals containing at least 7900 patients (men; ≥65 years), in 92 MSAs. Mortality rates were lower in VA hospitals than non-VA hospitals for AMI (13.5% vs 13.7%, P = .02; -0.2 percentage-point difference) and HF (11.4% vs 11.9%, P = .008; -0.5 percentage-point difference), but higher for pneumonia (12.6% vs 12.2%, P = .045; 0.4 percentage-point difference). In contrast, readmission rates were higher in VA hospitals for all 3 conditions (AMI, 17.8% vs 17.2%, 0.6 percentage-point difference; HF, 24.7% vs 23.5%, 1.2 percentage-point difference; pneumonia, 19.4% vs 18.7%, 0.7 percentage-point difference, all P < .001). In within-MSA comparisons, VA hospitals had lower mortality rates for AMI (percentage-point difference, -0.22; 95% CI, -0.40 to -0.04) and HF (-0.63; 95% CI, -0.95 to -0.31), and mortality rates for pneumonia were not significantly different (-0.03; 95% CI, -0.46 to 0.40); however, VA hospitals had higher readmission rates for AMI (0.62; 95% CI, 0.48 to 0.75), HF (0.97; 95% CI, 0.59 to 1.34), or pneumonia (0.66; 95% CI, 0.41 to 0.91).

CONCLUSIONS AND RELEVANCE

Among older men with AMI, HF, or pneumonia, hospitalization at VA hospitals, compared with hospitalization at non-VA hospitals, was associated with lower 30-day risk-standardized all-cause mortality rates for AMI and HF, and higher 30-day risk-standardized all-cause readmission rates for all 3 conditions, both nationally and within similar geographic areas, although absolute differences between these outcomes at VA and non-VA hospitals were small.

Development and Validation of an Algorithm to Identify Planned Readmissions From Claims Data

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.

Improvements in the distribution of hospital performance for the care of patients with acute myocardial infarction, heart failure, and pneumonia, 2006-2011

BACKGROUND

Medicare hospital core process measures have improved over time, but little is known about how the distribution of performance across hospitals has changed, particularly among the lowest performing hospitals.

METHODS

We studied all US hospitals reporting performance measure data on process measures for acute myocardial infarction (AMI), heart failure (HF), and pneumonia (PN) to the Centers for Medicare & Medicaid Services from 2006 to 2011. We assessed changes in performance across hospital ranks, variability in the distribution of performance rates, and linear trends in the 10th percentile (lowest) of performance over time for both individual measures and a created composite measure for each condition.

RESULTS

More than 4000 hospitals submitted measure data each year. There were marked improvements in hospital performance measures (median performance for composite measures: AMI: 96%-99%, HF: 85%-98%, PN: 83%-97%). A greater number of hospitals reached the 100% performance level over time for all individual and composite measures. For the composite measures, the 10th percentile significantly improved (AMI: 90%-98%, P<0.0001 for trend; HF: 70%-92%, P=0.0002; PN: 71%-92%, P=0.0003); the variation (90th percentile rate minus 10th percentile rate) decreased from 9% in 2006 to 2% in 2011 for AMI, 25%-8% for HF, and 20%-7% for PN.

CONCLUSIONS

From 2006 to 2011, not only did the median performance improve but the distribution of performance narrowed. Focus needs to shift away from processes measures to new measures of quality.

Abstract 176: Variation in the Content and Timing of Informed Consent in Cardiovascular Procedures: An Opportunity to Improve Decision-making

Background: Informed consent, when effectively implemented, provides a powerful opportunity to build trust between the patient and clinician; support patient autonomy; and enable high-quality decision-making. Yet despite being legally and ethically mandated, consistent quality standards for informed consent are lacking, resulting in varied laws and practices. We examined variation in the quality of informed consent among three cardiovascular procedures - cardiac catheterization (CC), transesophageal echocardiography (TEE), and implantable cardioverter defibrillator (ICD) - at a single institution.

Methods: We conducted a chart review of elective outpatient CCs, TEEs, and ICDs performed from April 1 to June 30, 2014, evaluating: presence of consent form; content (documentation of purpose of procedure, benefits, risks, risk probabilities, and alternatives); legibility (assessed subjectively); and timing of consent prior to start of procedure. We also assessed documentation of procedure-specific risks (CC: bleeding, infection, heart attack, stroke, arrhythmia, vessel damage, heart damage, kidney injury, contrast allergy, adverse reaction, death; TEE: oral damage, esophageal damage, arrhythmia, blood pressure changes, breathing changes, aspiration pneumonia, death; ICD: pneumothorax, vessel damage, cardiac puncture, bleeding, infection, arrhythmia, adverse reaction, lead failure, death).

Results: A generic consent document was used for all procedures (79 CCs, 39 TEEs, 36 ICDs); however, personalized, hand-written information was heterogeneous. Some documents did not specify the procedure’s purpose (CC: 19% [15 of 79]; TEE: 8% [3 of 39]; ICD: 6% [2 of 36]). Description of procedure-specific risks varied, and in some cases no risks were specified (CC: 32% [25 of 79]; TEE: 18% [7 of 39]; ICD: 42% [15 of 36]); only 8 total documents cited risk probabilities. Alternatives were never listed, and benefits were frequently not specified (CC: 100% [79 of 79]; TEE: 100% [39 of 39]; ICD: 86% [31 of 36]). Written documentation was often illegible (CC: 41% [32 of 79]; TEE: 31% [12 of 39]; ICD: 28% [10 of 36]). Timing of consent prior to procedure varied (median time: 32 min for CC, 29 min for TEE, and 1.6 days for ICD). Last, 43% (34 of 79 CCs), 49% (19 of 39 TEEs), and 3% (1 of 36 ICDs) were consented less than 30 minutes prior to start time.

Conclusion: We observed notable variation in the content, legibility and timing of informed consent documents within and across procedures. These components are necessary, though may not be sufficient, to support a high-quality informed consent process. Our results highlight opportunities for improving informed consent. Standardization of content and increased time for patients to consider the risks, benefits, and alternatives of elective procedures may result in higher quality decision-making and facilitate patient autonomy.

Development and use of an administrative claims measure for profiling hospital-wide performance on 30-day unplanned readmission

BACKGROUND

Existing publicly reported readmission measures are condition-specific, representing less than 20% of adult hospitalizations. An all-condition measure may better measure quality and promote innovation.

OBJECTIVE

To develop an all-condition, hospital-wide readmission measure.

DESIGN

Measure development study.

SETTING

4821 U.S. hospitals.

PATIENTS

Medicare fee-for-service beneficiaries aged 65 years or older.

MEASUREMENTS

Hospital-level, risk-standardized unplanned readmissions within 30 days of discharge. The measure uses Medicare fee-for-service claims and is a composite of 5 specialty-based, risk-standardized rates for medicine, surgery/gynecology, cardiorespiratory, cardiovascular, and neurology cohorts. The 2007-2008 admissions were randomly split for development and validation. Models were adjusted for age, principal diagnosis, and comorbid conditions. Calibration in Medicare and all-payer data was examined, and hospital rankings in the development and validation samples were compared.

RESULTS

The development data set contained 8 018 949 admissions associated with 1 276 165 unplanned readmissions (15.9%). The median hospital risk-standardized unplanned readmission rate was 15.8 (range, 11.6 to 21.9). The 5 specialty cohort models accurately predicted readmission risk in both Medicare and all-payer data sets for average-risk patients but slightly overestimated readmission risk at the extremes. Overall hospital risk-standardized readmission rates did not differ statistically in the split samples (P = 0.71 for difference in rank), and 76% of hospitals' validation-set rankings were within 2 deciles of the development rank (24% were more than 2 deciles). Of hospitals ranking in the top or bottom deciles, 90% remained within 2 deciles (10% were more than 2 deciles) and 82% remained within 1 decile (18% were more than 1 decile).

LIMITATION

Risk adjustment was limited to that available in claims data.

CONCLUSION

A claims-based, hospital-wide unplanned readmission measure for profiling hospitals produced reasonably consistent results in different data sets and was similarly calibrated in both Medicare and all-payer data.

PRIMARY FUNDING SOURCE

Centers for Medicare & Medicaid Services.

Payments for acute myocardial infarction episodes-of-care initiated at hospitals with and without interventional capabilities

BACKGROUND

It is unknown whether hospitals with percutaneous coronary intervention (PCI) capability provide costlier care than hospitals without PCI capability for patients with acute myocardial infarction. The growing number of PCI hospitals and higher rate of PCI use may result in higher costs for episodes-of-care initiated at PCI hospitals. However, higher rates of transfers and postacute care procedures may result in higher costs for episodes-of-care initiated at non-PCI hospitals.

METHODS AND RESULTS

We identified all 2008 acute myocardial infarction admissions among Medicare fee-for-service beneficiaries by principal discharge diagnosis and classified hospitals as PCI- or non-PCI-capable on the basis of hospitals' 2007 PCI performance. We added all payments from admission through 30 days postadmission, including payments to hospitals other than the admitting hospital. We calculated and compared risk-standardized payment for PCI and non-PCI hospitals using 2-level hierarchical generalized linear models, adjusting for patient demographics and clinical characteristics. PCI hospitals had a higher mean 30-day risk-standardized payment than non-PCI hospitals (PCI, $20 340; non-PCI, $19 713; P<0.001). Patients presenting to PCI hospitals had higher PCI rates (39.2% versus 13.2%; P<0.001) and higher coronary artery bypass graft rates (9.5% versus 4.4%; P<0.001) during index admissions, lower transfer rates (2.2% versus 25.4%; P<0.001), and lower revascularization rates within 30 days (0.15% versus 0.27%; P<0.0001) than those presenting to non-PCI hospitals.

CONCLUSIONS

Despite higher PCI and coronary artery bypass graft rates for Medicare patients initially presenting to PCI hospitals, PCI hospitals were only $627 costlier than non-PCI hospitals for the treatment of patients with acute myocardial infarction in 2008.

National patterns of risk-standardized mortality and readmission after hospitalization for acute myocardial infarction, heart failure, and pneumonia: update on publicly reported outcomes measures based on the 2013 release

BACKGROUND

The Centers for Medicare & Medicaid Services publicly reports risk-standardized mortality rates (RSMRs) within 30-days of admission and, in 2013, risk-standardized unplanned readmission rates (RSRRs) within 30-days of discharge for patients hospitalized with acute myocardial infarction (AMI), heart failure (HF), and pneumonia. Current publicly reported data do not focus on variation in national results or annual changes.

OBJECTIVE

Describe U.S. hospital performance on AMI, HF, and pneumonia mortality and updated readmission measures to provide perspective on national performance variation.

DESIGN

To identify recent changes and variation in national hospital-level mortality and readmission for AMI, HF, and pneumonia, we performed cross-sectional panel analyses of national hospital performance on publicly reported measures.

PARTICIPANTS

Fee-for-service Medicare and Veterans Health Administration beneficiaries, 65 years or older, hospitalized with principal discharge diagnoses of AMI, HF, or pneumonia between July 2009 and June 2012. RSMRs/RSRRs were calculated using hierarchical logistic models risk-adjusted for age, sex, comorbidities, and patients' clustering among hospitals.

RESULTS

Median (range) RSMRs for AMI, HF, and pneumonia were 15.1% (9.4-21.0%), 11.3% (6.4-17.9%), and 11.4% (6.5-24.5%), respectively. Median (range) RSRRs for AMI, HF, and pneumonia were 18.2% (14.4-24.3%), 22.9% (17.1-30.7%), and 17.5% (13.6-24.0%), respectively. Median RSMRs declined for AMI (15.5% in 2009-2010, 15.4% in 2010-2011, 14.7% in 2011-2012) and remained similar for HF (11.5% in 2009-2010, 11.9% in 2010-2011, 11.7% in 2011-2012) and pneumonia (11.8% in 2009-2010, 11.9% in 2010-2011, 11.6% in 2011-2012). Median hospital-level RSRRs declined: AMI (18.5% in 2009-2010, 18.5% in 2010-2011, 17.7% in 2011-2012), HF (23.3% in 2009-2010, 23.1% in 2010-2011, 22.5% in 2011-2012), and pneumonia (17.7% in 2009-2010, 17.6% in 2010-2011, 17.3% in 2011-2012).

CONCLUSIONS

We report the first national unplanned readmission results demonstrating declining rates for all three conditions between 2009-2012. Simultaneously, AMI mortality continued to decline, pneumonia mortality was stable, and HF mortality experienced a small increase.

Readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia among young and middle-aged adults: a retrospective observational cohort study

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. Please see later in the article for the Editors' Summary.

Place of residence and outcomes of patients with heart failure: analysis from the telemonitoring to improve heart failure outcomes trial

BACKGROUND

Recent studies show an association between neighborhood-level measures of socioeconomic status (SES) and outcomes for patients with heart failure. We do not know whether neighborhood SES has a primary effect or is a marker for individual SES.

METHODS AND RESULTS

We used the data from participants of the Telemonitoring to Improve Heart Failure Outcomes (Tele-HF) trial, recruited from 33 US internal medicine and cardiology practices and examined the association between neighborhood SES and outcomes of patients with heart failure. We used census tracts as proxies for neighborhoods and constructed summary SES scores that included information about wealth and income, education, and occupation. The primary end points were readmission and all-cause mortality at 6 months. We conducted patient interviews and medical chart reviews to obtain demographic information, clinical factors, therapies, and individual SES. We included 1557 patients: 524, 516, and 517 from low, medium, and high SES neighborhoods, respectively (mean age, 61.1±15.2 years; 42.2% women).Overall, 745 patients (47.8%) had ≥1 readmission and 179 patients (11.5%) died. When compared with patients in high SES neighborhoods, those living in low-SES neighborhoods were more likely to be readmitted (odds ratio, 1.35; 95% confidence interval, 1.01-1.82), but the mortality rates were not significantly different (odds ratio, 0.78; 95% confidence interval, 0.50-1.18). The results were consistent after multivariable adjustments for individual demographics, clinical factors, and individual SES.

CONCLUSIONS

Among patients with heart failure, neighborhood SES was significantly associated with 6-month all-cause readmission even after adjusting for other patient-level factors, including individual SES. Greater number of events and longer follow-up is required to ascertain the potential effect of neighborhood SES on mortality.

CLINICAL TRIAL REGISTRATION URL

http://clinicaltrials.gov/. Unique identifier: NCT00303212.

Abstract 19: Episode of Care Payments Across Two Cardiac Conditions: Where Does the Money Go?

Objective: One approach to reduce health care spending and improve coordination of care is to pay for an episode of care rather than individual services. Anchoring these episodes around an index hospitalization is sensible because hospitalizations are a leading contributor to rising healthcare costs and an index admission provides a clear time to begin the episode. Understanding which care settings are responsible for a greater proportion of expenditures can inform efforts to improve efficiencies in the care provided. Our objectives were to: 1) characterize total episode payments for two conditions and 2) examine the care settings accounting for the highest proportions of the 30-day episode of care payment.

Study Design: We used Medicare Claims data from 2008 and 2009 to identify hospital discharges for acute myocardial infarction (AMI) and heart failure among fee-for-service beneficiaries ≥ 65 years. We defined an episode as admission plus 30 days. To reflect differences in payments due to clinical care rather than geographic or policy adjustments, we omitted or averaged payment adjustments such as wage index and indirect medical education. For hospitalizations involving transfers, we summed the per diem and diagnosis related group payments and attributed this amount to the transfer-out hospital.

Principal Findings: For AMI and heart failure patients respectively, the median unadjusted episode payment was $15,581 (interquartile range (IQR) $11,416, $24,675); and $10,139 (IQR $6,972, $17,827). For AMI, 77% of total payments were for index admission and 23% for post-acute care. Among post-acute care payments, 35% were for readmissions, 30% for skilled nursing facilities (SNF), and 13% for non-acute inpatient stays (i.e., inpatient rehabilitation, inpatient psychiatric facilities, and long-term care facilities). For heart failure, 61% of total payments were for index admission and 37% for post-acute care. Among post-acute care payments for heart failure, 35% were for readmissions, 33% for SNF, and 7% for non-acute inpatient stays.

Conclusions: Total episode payments vary by condition. For heart failure, which does not routinely require procedures, 61% of total episode payments are attributable to the index admissions compared to 77% for AMI. Across both conditions, the highest proportion of payments made after discharge were for readmissions, SNF, and non-acute inpatient stays. These results can inform efforts to reduce costs, including payment reforms such as bundled payment initiatives.