Hospital Characteristics Associated With Risk-standardized Readmission Rates

Scalable kernel balancing weights in a nationwide observational study of hospital profit status and heart attack outcomes

Weighting is a general and often-used method for statistical adjustment. Weighting has two objectives: first, to balance covariate distributions, and second, to ensure that the weights have minimal dispersion and thus produce a more stable estimator. A recent, increasingly common approach directly optimizes the weights toward these two objectives. However, this approach has not yet been feasible in large-scale datasets when investigators wish to flexibly balance general basis functions in an extended feature space. To address this practical problem, we describe a scalable and flexible approach to weighting that integrates a basis expansion in a reproducing kernel Hilbert space with state-of-the-art convex optimization techniques. Specifically, we use the rank-restricted Nyström method to efficiently compute a kernel basis for balancing in nearly linear time and space, and then use the specialized first-order alternating direction method of multipliers to rapidly find the optimal weights. In an extensive simulation study, we provide new insights into the performance of weighting estimators in large datasets, showing that the proposed approach substantially outperforms others in terms of accuracy and speed. Finally, we use this weighting approach to conduct a national study of the relationship between hospital profit status and heart attack outcomes in a comprehensive dataset of 1.27 million patients. We find that for-profit hospitals use interventional cardiology to treat heart attacks at similar rates as other hospitals but have higher mortality and readmission rates.

Racial Disparities in 30-day Readmissions after Surgery for Head and Neck Cancer

BACKGROUND

Native Hawaiians and other Pacific Islanders (NHPI) patients with head and neck cancer are often aggregated with Asian individuals despite evidence of heterogeneous health outcomes and mortality. The aim of this study was to determine the association of race with unplanned 30-day hospital readmission rate after head and neck surgery across the five federally recognized racial categories.

METHODS

This retrospective cohort study used a national hospital-based database and included patients ≥18 years old with diagnostically confirmed, nonmetastatic head and neck cancer of any subsite treated surgically between 2004 and 2017. The primary endpoint was unplanned readmission within 30 days of discharge after primary surgery.

RESULTS

A total of 365,834 patients were included who were predominantly White (87%), treated at academic cancer centers (47%), lower income (63%), with early-stage disease (60%), and with thyroid (47%) or oral cavity (23%) cancers. Median follow-up duration was 47 months. Of the 10,717 (3%) readmissions, 5,845 (1.6%) were unplanned. Adjusted for confounders and compared with White patients, NHPI patients had the highest likelihood of unplanned (aOR 2.07, 95%CI 1.16-3.40, p = 0.008) readmissions. Within the NHPI group, patients with lower income (aOR 4.27, 95%CI 1.28-20.4, p = 0.035) and those residing in an urban or rural area (aOR 7.42, 95%CI 1.14-49.5, p = 0.034) were more likely to be readmitted.

CONCLUSIONS

NHPI patients with head and neck cancers experience significantly higher 30-day readmissions following definitive surgical treatment. These results highlight the importance of racial disaggregation in clinical studies.

LEVEL OF EVIDENCE

4 Laryngoscope, 134:1282-1287, 2024.

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.

Examining sources of post-acute care inequities with layered target matching

OBJECTIVE

To examine factors associated with racial inequities in discharge location, skilled nursing facility (SNF) utilization, and readmissions.

DATA SOURCES

A 20% sample of longitudinal Medicare claims from 2016 to 2018.

STUDY DESIGN

We present layered target matching, a method for studying sources of inequities. Layered target matching examines a fixed target population profile representing any race, ethnicity, or vulnerable population, sequentially adjusting for sets of characteristics that may contribute to inequities these groups endure. We use the method to study racial inequities in post-acute care use and readmissions.

DATA COLLECTION/EXTRACTION METHODS

We studied Black and non-Hispanic White fee-for-service Medicare beneficiaries aged 66+ admitted to short-term acute-care hospitals for qualifying diagnoses or procedures between January 1, 2016 and November 30, 2018.

PRINCIPAL FINDINGS

Admitted Black patients tended to be younger, had significantly higher rates of risk factors such as diabetes, stroke, or renal disease, and were much more frequently admitted to large or academic hospitals. Relative to demographically similar White patients, Black patients were significantly more likely to be discharged to SNFs (21.8% vs. 19.3%, difference = 2.5%, p < 0.0001) and to receive any SNF care within 30 days of discharge (25.3% vs. 22.4%, difference = 2.9%, p < 0.0001). Black patients were also significantly more likely to experience 30-day readmission (18.7% vs. 14.5%, difference = 4.2%, p < 0.0001). Differences in reasons for hospitalization and risk factors explained most of the differences in discharge location, post-acute care use, and readmission rates, while additional adjustment for differences in hospital characteristics and complications made little difference for any of the measures studied.

CONCLUSIONS

We found significant Black-White differences in discharge to SNFs, SNF utilization, and readmission rates. Using layered target matching, we found that differences in risk factors and reasons for hospitalization explained most of these differences, while differences in hospitals did not materially impact the differences.

Post-Acute Care Use Associated with Medicare Shared Savings Program and Disparities

OBJECTIVES

Medicare Shared Savings Program (MSSP) was implemented in 2012, but the impact of the MSSP on institutional post-acute care (PAC) use, and by race/ethnicity and payer status is less studied. We studied the impact of hospital participation in the MSSP on institutional PAC use and variations by race/ethnicity and payer status among 3 Medicare patient groups: ischemic stroke, hip fracture, and elective total joint arthroplasty (TJA).

DESIGN

A retrospective analysis of 2010-2016 Medicare Provider Analysis and Review files.

SETTING AND PARTICIPANTS

Medicare fee-for-service patients originally admitted for ischemic stroke, hip fracture, or elective TJA in MSSP-participating hospitals or nonparticipating hospitals.

METHODS

Patient-level linear probability models with difference-in-differences approach were used to compare the changes in institutional PAC use in MSSP-participating hospitals with nonparticipating hospitals as well as to compare the changes in differences by race/ethnicity and payer status in institutional PAC use over time.

RESULTS

Hospital participation in MSSP was significantly associated with increased institutional PAC use for the ischemic stroke cohort by 1.5 percentage points [95% confidence interval (CI) 0.00-0.3, P < .05] compared with non-MSSP participating hospitals. Regarding variations by race/ethnicity and payer status, for the elective TJA patients, racial minority patients in MSSP-participating hospitals had 3.8 percentage points greater (95% CI 0.01-0.06, P < .01) in institutional PAC use than white patients. Also, for ischemic stroke cohort, dual-eligible patients in MSSP-participating hospitals had 2.0 percentage points greater (95% CI 0.00-0.04, P < .10) in institutional PAC use than Medicare-only patients.

CONCLUSIONS AND IMPLICATIONS

This study found that hospital participation in the MSSP was associated with slightly increased institutional PAC use for ischemic stroke Medicare patients. Also, compared to non-MSSP participating hospitals, MSSP-participating hospitals were more likely to discharge racial minority patients for elective TJA and dual-eligible patients for ischemic stroke to institutional PAC.

Hospitals serving nursing home residents disproportionately penalized under hospital readmissions reduction program

BACKGROUND

Risk factors common to nursing home (NH) residents are potentially not fully captured by the Hospital Readmissions Reduction Program (HRRP). The unique challenges faced by hospitals that disproportionately serve NH residents who are at greater risk of readmissions have not been studied.

METHODS

Using 100% Medicare Provider Analysis and Review File and the Minimum Data Set from 2010-2013, we constructed a measure of hospital share of NH-originating hospitalizations (NOHs). We defined hospital share of NOHs as the proportion of inpatient stays by patients aged 65 or older who were directly admitted from NHs. To evaluate the impact of the share of NOHs on readmission penalties, we categorized hospitals into quartiles according to their share of NOHs and estimated the differences in the adjusted penalties across hospital quartiles after accounting for hospital characteristics, market characteristics and state fixed effects. We repeated the analyses for the penalties incurred in each year between 2015 and 2019.

RESULTS

Hospitals varied substantially in the share of NOHs (median [interquartile range], 11.3% [8.2%-15.1%]), with limited variation over time. In 2015, hospitals in the highest quartile of NOH received on average 0.58% Medicare payment reduction compared to 0.44% reduction among those in the lowest quartile (32.9% higher penalties, p < 0.001). The increase in penalties continued to grow in 2017 and 2018 when the HRRP expanded to include additional target conditions (47.3% and 66.7%, respectively, p < 0.001 for both). Although the effect diminished in 2019 following the additional adjustment for hospital's dual-eligible share, hospitals in the highest quartile of NOH still incurred 43.0% (p < 0.001) higher penalties than those in the lowest quartile.

CONCLUSIONS

Hospitals varied considerably in their share of NOHs. Hospitals having a higher share of NOHs were disproportionately penalized for excess readmissions, even under the revised policy that adjusts for the share of dual-eligible admissions.

Does Geographic Location Affect the Quality of Care? The Difference in Readmission Rates Between the Border and Non-Border Hospitals in Texas

Purpose

Materials and Methods

Results

Conclusion

Age- and Diagnosis-Based Trends for Unplanned Pediatric Rehospitalizations in the United States

Background and objectives: Hospital readmission rate helps to highlight the effectiveness of post-discharge care. There remains a paucity of plausible age-based categorization especially for ages below one year for hospital readmission rates.

Methods: Data from the 2017 Healthcare Cost and Utilization Project National Readmissions Database was analyzed for ages 0-18 years. Logistic regression analysis was performed to identify predictors for unplanned early readmissions. 

Results: We identified 5,529,389 inpatient pediatric encounters which were further divided into age group cohorts. The overall rate of readmissions was identified at 3.2%. Beyond infancy, the readmission rate was found to be 6.7%. Across all age groups, the major predictors of unplanned readmission were cancers, diseases affecting transplant recipients and sickle cell patients. It was determined that reflux, milk protein allergy, hepatitis and inflammatory bowel diseases were significant diagnoses leading to readmission. Anxiety, depression and suicidal ideation depicted higher readmission rates in those older than 13 years. Across ages one to four years, dehydration, asthma and bronchiolitis were negative predictors of unplanned readmission. 

Conclusions: Thirty-day unplanned readmissions remain a problem leading to billions of taxpayer dollars lost per annum. Effective strategies for mandatory outpatient follow-up may help the financial aspect of care while also enhancing the quality of care.

Demystifying hospital charges for hospital readmissions in 2017 in the United States for psychosis (DRG = 885)

Existing research on hospital charges is primarily focused on hospital admissions, but not on hospital readmissions. Our research fills this gap. We utilize the 2017 Hospital Readmissions database from the Agency for Healthcare Research and Quality (AHRQ) to empirically study factors that impact hospital charges for hospital readmissions. We focus on psychosis (DRG = 885) which has 609,360 records in 2017 in the AHRQ database. We employ regression analyses using patient demographics, inpatient care variables, and hospital characteristics to explain variance in hospital charges. Results show that inpatient care (diagnoses, procedures, length of stay), hospital ownership, and younger patients result in higher hospital charges.

A Dynamic Risk Management Approach for Reducing Harm From Invasive Bedside Procedures Performed During Residency

Internal medicine (IM) residents frequently perform invasive bedside procedures during residency training. Bedside procedure training in IM programs may compromise patient safety. Current evidence suggests that IM training programs rely heavily on the number of procedures completed during training as a proxy for resident competence instead of using objective postprocedure patient outcomes. The authors posit that the results of procedural training effectiveness should be reframed with outcome metrics rather than process measures alone. This article introduces the as low as reasonably achievable (ALARA) approach, which originated in the nuclear industry to increase safety margins, to help assess and reduce bedside procedural risks. Training program directors are encouraged to use ALARA calculations to define the risk trade-offs inherent in current procedural training and assess how best to reliably improve patient outcomes. The authors describe 5 options to consider: training all residents in bedside procedures, training only select residents in bedside procedures, training no residents in bedside procedures, deploying 24-hour procedure teams supervised by IM faculty, and deploying 24-hour procedure teams supervised by non-IM faculty. The authors explore how quality improvement approaches using process maps, fishbone diagrams, failure mode effects and analyses, and risk matrices can be effectively implemented to assess training resources, choices, and aims. Future research should address the drivers behind developing optimal training programs that support independent practice, correlations with patient outcomes, and methods that enable faculty to justify their supervisory decisions while adhering to ALARA risk management standards.

What Can We Learn from the Past by Means of Very Long-Term Follow-Up after Aortic Valve Replacement?

Background: Studies on very long-term outcomes after aortic valve replacement are sparse. Methods: In this retrospective cohort study, long-term outcomes during 25.1 ± 2.8 years of follow-up were determined in 673 patients who underwent aortic valve replacement with or without concomitant coronary artery bypass surgery for severe aortic stenosis and/or regurgitation. Independent predictors of decreased long-term survival were determined. Cumulative incidence rates of major adverse events in patients with a mechanical versus those with a biologic prosthesis were assessed, as well as of major bleeding events in patients with a mechanical prosthesis under the age of 60 versus those above the age of 60. Results: Impaired left ventricular function, severe prosthesis–patient mismatch, and increased aortic cross-clamp time were independent predictors of decreased long-term survival. Left ventricular hypertrophy, a mechanical or biologic prosthesis, increased cardiopulmonary bypass time, new-onset postoperative atrial fibrillation, and the presence of symptoms did not independently predict decreased long-term survival. The risk of major bleeding events was higher in patients with a mechanical in comparison with those with a biologic prosthesis. Younger age (under 60 years) did not protect patients with a mechanical prosthesis against major bleeding events. Conclusions: Very long-term outcome data are invaluable for careful decision-making on aortic valve replacement.

Community socioeconomic disadvantage drives type of 30-day medical-surgical revisits among patients with serious mental illness

Background

Patients with serious mental illness (SMI) are vulnerable to medical-surgical readmissions and emergency department visits.

Methods

We studied 1,914,619 patients with SMI discharged after medical-surgical admissions in Florida and New York between 2012 and 2015 and their revisits to the hospital within 30 days of discharge.

Results

Patients with SMI from the most disadvantaged communities had greater adjusted 30-day revisit rates than patients from less disadvantaged communities. Among those that experienced a revisit, patients from the most disadvantaged communities had 7.3 % greater 30-day observation stay revisits.

Conclusions

These results suggest that additional investments are needed to ensure that patients with SMI from the most disadvantaged communities are receiving appropriate post-discharge care.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06605-y.

Sustainers: Hospitals with sustained superior performance

PURPOSE

The purpose of this article was to investigate the organizational and market-level variables associated with sustained superior hospital performance on Value-Based Purchasing total performance scores (TPS).

METHODOLOGY

TPS for 2014 through 2017 was obtained from the Centers for Medicare & Medicaid Services Hospital Compare website. Market-level data were from the 2017 Area Health Resource File, and hospital-level data were from the 2014 American Hospital Association Annual Survey database. We specified a logistic regression model to identify significant predictors of hospitals with sustained superior performance on TPS, that is, "sustainers."

PRINCIPAL FINDINGS

Only 8.4% of hospitals were classified as sustainers. Hospitals located in rural markets with a high Medicare Advantage penetration had a higher likelihood of being classified as sustainers. High RN staffing levels, lower Medicare share of inpatient days, not-for-profit ownership, and small size were all significant organizational predictors of sustained superior performance.

CONCLUSIONS

Both modifiable characteristics, such as nurse staffing levels, and nonmodifiable characteristics, such as rural markets and small hospital size, are associated with the likelihood of hospitals sustaining superior performance over time.

PRACTICE IMPLICATIONS

Managers need to carefully examine their staffing levels as they pursue interventions to sustain high TPS overtime. Moreover, factors such as Medicare share of inpatient days and size need to be considered when understanding barriers to sustained performance on Value-Based Purchasing domains.

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

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

The Effect of Medicare Shared Savings Program on Readmissions and Variations by Race/Ethnicity and Payer Status (December 9, 2020)

OBJECTIVE

Accountable care organizations in the Medicare Shared Savings Program (MSSP) in the United States attempt to reduce cost and improve quality for their patients by improving care coordination across care settings. We examined the impact of hospital participation in the MSSP on 30-day readmissions for several groups of Medicare inpatients, and by race/ethnicity and payer status.

MAIN DATA SOURCE

A 2010-2016 Medicare Provider Analysis and Review files.

RESEARCH DESIGN

With propensity score matched sample of MSSP and non-MSSP-participating hospitals, patient-level linear probability models with difference-in-differences approach were used to compare the changes in readmission rates for Medicare fee-for-service patients initially admitted for ischemic stroke, hip fracture, or total joint arthroplasty in MSSP-participating hospitals with non-MSSP-participating hospitals as well as to compare the changes in disparities in readmission rates over time.

PRINCIPAL FINDINGS

Hospital participation in MSSP was associated with further reduced readmission rate by 1.1 percentage points (95% confidence interval: -0.02 to 0.00, P<0.05) and 1.5 percentage points (95% confidence interval: -0.03 to 0.00, P=0.08) for ischemic stroke and hip fracture cohorts, respectively, compared with non-MSSP-participating hospitals, after the third year of hospital participation in the MSSP. There was no evidence that MSSP had an impact on racial/ethnic disparities, but increased disparity by payer status (dual vs. Medicare-only) was observed. These findings together suggest that MSSP accountable care organizations may take at least 3 years to achieve reduced readmissions and may increase disparities by payer status.

Impact of Self-Care and Mobility on One or More Post-Acute Care Transitions

Objective: To investigate the association between functional status and post-acute care (PAC) transition(s). Methods: Secondary analysis of 2013-2014 Medicare data for individuals aged ≥66 years with stroke, lower extremity joint replacements, and hip/femur fracture discharged to one of three PAC settings (inpatient rehabilitation facilities, skilled nursing facilities, and home health agencies). Functional scores were co-calibrated into a 0-100 scale across settings. Multilevel logistic regression was used to test the partition of variance (%) and the probability of PAC transition attributed to the functional score in the initial PAC setting. Results: Patients discharged to inpatient rehabilitation facilities with higher function were less likely to use additional PAC. Function level in an inpatient rehabilitation facility explained more of the variance in PAC transitions than function level while in a skilled nursing facility. Discussion: The function level affected PAC transitions more for those discharged to an inpatient rehabilitation facility than to a skilled nursing facility.

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.

Measures of Care Coordination at Inpatient Psychiatric Facilities and the Medicare 30-Day All-Cause Readmission Rate

OBJECTIVE

Since late 2012, the Medicare Inpatient Psychiatric Facility Quality Reporting (IPFQR) program of the Centers for Medicare and Medicaid Services (CMS) has required inpatient psychiatric facilities to collect and publicly report a suite of quality measures. This study explored the association between facility-level 30-day risk-adjusted all-cause readmission (medical or psychiatric) after psychiatric hospitalization (READM-30-IPF) and care coordination process measures in the IPFQR program.

METHODS

The study used publicly reported IPFQR facility-level performance data of the Hospital Compare Web site for 1,343 inpatient psychiatric facilities, reflecting performance from July 2015 to June 2017. The authors used a cross-sectional design and linear regression models controlling for hospital and community characteristics and using state as fixed effect.

RESULTS

The mean±SD facility-level READM-30-IPF was 20%±3%, with substantial variation by facility type, ownership status, rurality, and percentage of racial-ethnic minority residents in the county. Regression results showed that facilities with performance in the top tercile on the measure of 7-day mental health follow-up after discharge had readmission rates significantly lower than facilities in the bottom tercile (coefficient=-0.58, p<0.01), although the magnitude of this difference was small. READM-30-IPF, however, did not vary by facilities' performance on measures of discharge plan creation and transmission.

CONCLUSIONS

Results suggest that facilities have substantial opportunities to reduce readmissions after psychiatric hospitalization. The association between hospital performance on care coordination process measures and the all-cause readmission measure currently included in the IPFQR program was minimal. The CMS should evaluate whether the IPFQR measures adequately capture compliance with evidence-based processes and desired outcomes.

Substantial hospital level variation in all-cause readmission rates among medicare beneficiaries with serious mental illness

BACKGROUND

Patients with serious mental illness (i.e., SMI; bipolar disorder, major depressive disorder, and schizophrenia) are at increased risk of readmission, yet little is known about the extent to which readmission rates among these patients vary across hospitals. The purpose of this study was to examine the variation across hospitals in readmissions for patients with SMI and differences in the characteristics of hospitals with the highest and lowest adjusted readmission rates.

METHODS

We conducted a cross-sectional analysis of pooled inpatient claims from 2013-2016. Mixed logit models with hospital random effects were used to estimate the hospital-level variance. The sample included patients with SMI from a 5% sample of fee-for-service Medicare beneficiaries.

RESULTS

We identified 2066 hospitals with at least 30 index admissions for Medicare beneficiaries with SMI. In multivariate analyses, factors most strongly associated with increased risk of readmission included substance use disorder (OR 2.311; p < 0.001) and end stage renal disease (OR 2.024; p < 0.001). Unadjusted readmission for hospitals at the 5th and 95th percentiles of performance were 7.05% and 15.24%, respectively, constituting an 8.2% difference. Adjusting for patient and community characteristics reduced the spread in readmission rates between the 5th and 95th percentiles of hospitals by 1.0% (i.e. to 7.2%). Hospitals in the lowest vs. highest quintiles of adjusted readmission rates were more likely to be teaching hospitals (11.1% vs. 16.7%; p < 0.05) and located in the South (37.7% vs. 40.4%) or Midwest (19.8% vs. 30.0%; p < 0.001 for region differences).

CONCLUSIONS

There is substantial hospital-level variation in readmission rates among patients with serious mental illness, even after adjusting for patient and community characteristics. This has implications for policy guiding investment in hospital-based services and community resources, to improve transitions of care for patients with SMI.

Medicare Shared Savings Program and readmission rate among patients with ischemic stroke

OBJECTIVE

Hospitals participating in the Medicare Shared Savings Program (MSSP) share with the Centers for Medicare and Medicaid Services (CMS) the savings generated by reduced cost of care. Our aim was to determine whether MSSP is associated with changes in readmissions and mortality for Medicare patients hospitalized with ischemic stroke, and whether MSSP has a different impact on safety net hospitals (SNHs) compared to non-SNHs.

METHODS

This study was based on the CMS Hospital Compare data for risk-standardized 30-day readmission and mortality rates for Medicare patients hospitalized with ischemic strokes between 2010 and 2017. With a propensity score-matched sample, hospital-level difference-in-difference analysis was used to determine whether MSSP was associated with changes in hospital readmission and mortality as well as to examine the impact of MSSP on SNHs compared to non-SNHs.

RESULTS

MSSP-participating hospitals had slightly greater reductions in readmission rates compared to matched nonparticipating hospitals (difference, 0.25 percentage points; 95% confidence interval [CI], -0.42 to -0.08). Mortality rates decreased among all hospitals, but mortality reduction was not significantly different between MSSP-participating hospitals and matched hospitals (difference, 0.06 percentage points; 95% CI, -0.28 to 0.17). Prior to MSSP, readmission rates in SNHs were higher compared to non-SNHs, but MSSP did not have significantly different impact on hospital readmission and mortality rates for SNHs and non-SNHs.

CONCLUSION

MSSP led to slightly fewer readmissions without increases in mortality for Medicare patients hospitalized with ischemic stroke. Similar reductions in readmission rates were observed in SNHs and non-SNHs participating in MSSP, indicating persistent gaps between SNHs and non-SNHs.

The Association Between Structural, Performance, and Community Factors and the Likelihood of Receiving a Penalty Under the Hospital Readmissions Reduction Program (Fiscal Year 2013-2019)

Purpose: Little is known about the role of structural, performance, and community factors that impact the likelihood of receiving a penalty under the Hospital Readmission Reduction Program. This study examined the association between structural, performance, and community factors and the likelihood of receiving a penalty as well as investigated the likelihood of hospitals serving vulnerable populations of receiving a penalty.

Methods: Centers for Medicare and Medicaid Services and United States Census Bureau data were used in this analysis. Ordered logistic regressions in a cross-sectional analysis were employed to estimate the probability of receiving a high or low penalty in the fiscal year 2013 through 2019.

Results: On average, medium-sized, major teaching, and safety-net hospitals had the highest proportion of hospitals with a high penalty. After controlling for performance and community factors, structural factor variables such as safety-net status, rural status, and teaching status either were no longer significant or the likelihood magnitude changed. However, after controlling for performance and community factors, the statistical significance of hospital size variables and geographic location persisted across the years. Length of stay and occupancy rate variables were also statistically significant across the 7 years under review.

Conclusion: Taken together, structural, performance, and community factors are important in explaining variation in the likelihood of receiving a penalty. There is no evidence that safety-net, rural, and public hospitals are more likely to receive a penalty. The results also suggest that there is room for providers to reduce avoidable readmissions and policymakers to mitigate unintended consequences.

Factors Associated With Multiple Psychiatric Readmissions for Youth With Mood Disorders

OBJECTIVE

Inpatient psychiatric readmission rates are increasingly considered indicators of quality of care. This study builds upon prior research by examining patient-, hospital-, and community-level factors associated with single and multiple readmissions for youth.

METHOD

A retrospective cohort study was conducted using Medicaid claims data from four states supplemented with the American Hospital Association survey, the Area Resource File, and the National Survey of Mental Health Treatment Services. Multinomial logistic regression examined patient-, hospital-, and community-level factors that were associated with inpatient psychiatric readmission for 6,797 Medicaid-eligible youth with a primary diagnosis of mood disorder using a three-level nominal dependent variable coded as no readmission, one readmission, and two or more readmissions within 6 months after discharge.

RESULTS

Six months after initial discharge, 941 youth (13.8%) were readmitted once and 471 (6.9%) were readmitted two or more times. The odds of single or multiple readmissions were significantly higher (p < .05) for youth classified as disabled or in foster care, those with multiple psychiatric comorbidities, medical comorbidity, and prior psychiatric hospitalization. Treatment in hospitals with high percentage of Medicaid discharges and a high number of beds was associated with lower odds of readmission. There was a significant interaction between length of stay and outpatient mental health follow-up within 7 days of discharge.

CONCLUSION

Patient- and hospital-level factors are associated with likelihood of both single and multiple youth inpatient psychiatric readmissions, suggesting potential risk markers for psychiatric readmission.

Increased 30-day readmission rate after craniotomy for tumor resection at safety net hospitals in small metropolitan areas

PURPOSE

Unplanned readmission of post-operative brain tumor patients is often attributed to hospital and patient characteristics and is associated with higher mortality and cost. Previous studies demonstrate multiple patient outcome disparities in safety net hospitals (SNHs) when compared to non-SNHs. This study uses the Nationwide Readmissions Database (NRD) to determine if initial brain tumor resection at SNHs is associated with increased 30-day non-elective readmission rates.

METHODS

Patients with benign or malignant primary or metastatic brain tumor undergoing craniotomy for surgical resection were retrospectively identified in the NRD from 2010 to 2014. SNHs were defined as hospitals with Medicaid and uninsured patient burden in the top quartile. Descriptive and multivariate analyses employing survey-adjusted logistic regression evaluated patient and hospital level factors influencing 30-day readmissions.

RESULTS

During the study period, 83,367 patients met inclusion criteria. 44.7% of patients had a benign tumor, and 55.3% had a malignant tumor. Secondary CNS neoplasm (5.99%), post-operative infection (5.96%), and septicemia (4.26%) caused most readmissions within 30 days. Patients had increased unplanned readmission rates if they underwent craniotomy for tumor resection at a SNH in a small metropolitan area (OR 1.11, 95% CI 1.02-1.21, p = 0.01), but not at a SNH in a large metropolitan area (OR 0.99, 95% CI 0.93-1.05, p = 0.73).

CONCLUSION

This finding may reflect differences in access to care and disparities in neurosurgical resources between small and large metropolitan areas. Inequities in expertise and capacity are relevant as surgical volume was also related to readmission rates. Further studies may be warranted to address such disparities.

Factors associated with 30-day and 1-year readmission among psychiatric inpatients in Beijing China: a retrospective, medical record-based analysis

BACKGROUND

Psychiatric readmissions negatively impact patients and their families while increasing healthcare costs. This study aimed at investigating factors associated with psychiatric readmissions within 30 days and 1 year of the index admissions and exploring the possibilities of monitoring and improving psychiatric care quality in China.

METHODS

Data on index admission, subsequent admission(s), clinical and hospital-related factors were extracted in the inpatient medical record database covering 10 secondary and tertiary psychiatric hospitals in Beijing, China. Logistic regressions were used to examine the associations between 30-day and 1-year readmissions plus frequent readmissions (≥3 times/year), and clinical variables as well as hospital characteristics.

RESULTS

The 30-day and 1-year psychiatric readmission rates were 16.69% (1289/7724) and 33.79% (2492/7374) respectively. 746/2492 patients (29.34%) were readmitted 3 times or more within a year (frequent readmissions). Factors significantly associated with the risk of both 30-day and 1-year readmission were residing in an urban area, having medical comorbidities, previous psychiatric admission(s), length of stay > 60 days in the index admission and being treated in tertiary hospitals (p < 0.001). Male patients were more likely to have frequent readmissions (OR 1.30, 95%CI 1.04-1.64). Receiving electroconvulsive therapy (ECT) was significantly associated with a lower risk of 30-day readmission (OR 0.72, 95%CI 0.56-0.91) and frequent readmissions (OR 0.60, 95%CI 0.40-0.91).

CONCLUSION

More than 30% of the psychiatric inpatients were readmitted within 1 year. Urban residents, those with medical comorbidities and previous psychiatric admission(s) or a longer length of stay were more likely to be readmitted, and men are more likely to be frequently readmitted. ECT treatment may reduce the likelihood of 30-day readmission and frequent admissions. Targeted interventions should be designed and piloted to effectively monitor and reduce psychiatric readmissions.

Comparison of Risk-Standardized Readmission Rates of Surgical Patients at Safety-Net and Non-Safety-Net Hospitals Using Agency for Healthcare Research and Quality and American Hospital Association Data

Importance

Medical patients discharged from safety-net hospitals (SNHs) experience higher readmission rates compared with those discharged from non-SNHs. However, little is known about whether this association persists for surgical patients.

Objectives

To examine differences in readmission rates between SNHs and non-SNHs among surgical patients after discharge and determine whether hospital characteristics might account for some of the variation.

Design, Setting, and Participants

This observational retrospective study linked the Healthcare Cost and Utilization Project State Inpatient Databases of the Agency for Healthcare Research and Quality from January 1, 2011, through December 31, 2014, for 4 states (New York, Florida, Iowa, and Washington) with data from the 2014 American Hospital Association annual survey. After identifying surgical discharges, SNHs were defined as those with the top quartile of inpatient stays paid by Medicaid or self-paid. Hospital-level risk-standardized readmission rates (RSRRs) for surgical discharges were calculated. The association between hospital RSRRs and hospital characteristics was evaluated with bivariate analyses. An estimated multivariable hierarchical linear regression model was used to examine variation in hospital RSRRs, adjusting for hospital characteristics, state, year, and SNH status. Data were analyzed from June 1, 2017, through March 1, 2018.

Exposures

Surgical care at an SNH.

Main Outcomes and Measures

Readmission after an index surgical admission.

Results

A total of 1 252 505 patients across all 4 years and states were included in the analysis (51.7% women; mean [SD] age, 52.7 [18.1] years). Bivariate analyses found that SNHs had higher mean (SD) surgical RSRRs compared with non-SNHs; significant differences were found for New York (9.6 [0.1] vs 10.9 [0.1]; P < .001) and Florida (11.6 [0.1] vs 12.1 [0.1]; P = .001). The SNHs also had higher RSRRs in these 2 states when stratified by hospital funding (nonfederal government SNHs in New York, 11.9 [0.2]; for-profit, private SNHs in Florida, 13.1 [0.2]; P < .001 for both); however, bed size was a significant factor for higher mean (SD) RSRRs only for New York (200 to 399 beds, 12.0 [0.4]; P = .006). Similar results were found for multivariable linear regression models; RSRRs were 1.02% higher for SNHs compared with non-SNHs (95% CI, 0.75%-1.29%; P < .001). Increased RSRRs were observed for hospitals in New York and Florida, teaching hospitals, and investor-owned hospitals. Factors associated with reduced RSRRs included presence of an ambulatory surgery center, cardiac catheterization capabilities, and high surgical volume.

Conclusions and Relevance

According to results of this study, surgical patients treated at SNHs experienced slightly higher RSRRs compared with those treated at non-SNHs. This association persisted after adjusting for year, state, and hospital factors, including teaching status, hospital bed size, and hospital volume.

The 30-day unplanned readmission rate and hospital volume: a national population-based study in South Korea

OBJECTIVE

To examine the association between hospital volume and the unplanned 30-day readmission rate as a quality measure.

DESIGN

A retrospective cross-sectional study.

SETTING

The Korea healthcare system is operated by a single payer under the National Health Insurance Service.

PARTICIPANTS

Using national health claims data of the Health Insurance Review and Assessment in South Korea, we examined 1 296 275 adult discharges (≥18 years old) from 90 hospitals (≥500 beds) in the 2013 calendar year.

MAIN OUTCOME MEASURES

We analysed the 30-day, unplanned, observed-to-expected standardized readmission rate for hospitals and for five specialty cohorts: medicine, surgery/gynaecology, cardiovascular, cardiorespiratory, and neurology. We assessed the association between hospital volume by tertiles and the 30-day standardized readmission rates with and without adjustment for hospital characteristics.

RESULTS

The rate for the lowest-volume hospitals was 6.10 compared with 6.20 for the highest-volume hospitals. We observed the standardized readmission rates did not differ significantly between the lowest- and highest-volume groups, except for the neurology cohort, which remained significant after adjusting for hospital characteristics.

CONCLUSIONS

The standardized readmission rates were not associated with hospital volume, except for the neurology cohort, in which the standardized readmission rate was significantly higher in the highest-volume hospitals than in lowest- and intermediate-volume hospitals, which was not consistent with the typical association of greater hospital volume with better outcomes. This association was independent of hospital characteristics. Therefore, the rate of readmissions should be used with caution when gauging the quality of hospital care according to hospital volume.

Who Cares for Total Hip Arthroplasty Complications? Rates of Readmission to a Hospital Different From the Location of the Index Procedure

INTRODUCTION

While the number of total hip arthroplasties (THAs) performed increases, so is the number of postoperative readmissions, resulting in costly episodes of care that may disproportionately affect certain hospitals. This study examines the rates of readmission of patients to the same hospital at which they underwent index THA, compared with readmission to a different hospital.

METHODS

Data for all hospital discharges from 1995 to 2010 were obtained from the California Office of Statewide Health Planning and Development database. Patient outcomes, readmission data, demographic information, hospital teaching status, and location were analyzed. Regression modeling was used to evaluate the effect of hospital teaching status, location, and individual complications on the risk of readmission to the same hospital as opposed to a different hospital following the index procedure.

RESULTS

The overall postoperative readmission rate for specific defined complications or all-cause 30-day readmissions was 3.92%, with 75.17% readmitted to the same hospital. Following index THA at a nonacademic or academic hospital, 95.9% and 84.6% of patients were readmitted to the same type of hospital, respectively. Patients who had their index procedure at an academic hospital had lower odds for readmission to the same hospital (odds ratio, 0.734; P < 0.0001) compared with nonacademic centers. Hospitals in midsize towns had higher odds of readmission to the same hospital (odds ratio, 1.735; P = 0.0012) compared with those in large metropolitan areas.

DISCUSSION

Although more than 75% of patients with unplanned readmissions went to the same hospital as their index THA, academic and larger metropolitan hospitals had higher odds of postoperative readmissions to a different hospital.

Unplanned Readmissions After Acute Myocardial Infarction: 1-Year Trajectory Following Discharge From a Safety Net Hospital

BACKGROUND

Financial penalties rendered by the Centers for Medicare and Medicaid Services have brought about new challenges for safety net hospitals that serve a vulnerable patient population with risk factors associated with high readmission rates. Our goal was to determine the 1-year trajectory of unplanned readmissions in post-myocardial infarction (MI) patients, and to identify factors associated with readmission.

METHODS

A total of 261 acute MI patients admitted from April 2015 to April 2016 were evaluated in a multidisciplinary cardiology clinic within 10 days of hospital discharge and baseline characteristics and medical comorbidities were collected. Readmission and mortality data were obtained at 1 year through chart review and telephone follow-up.

RESULTS

At 1 year, there were 90 (34%) unplanned readmissions of which half were for noncardiac diagnoses. Of these, 69 patients (77%) were readmitted once, 16 (18%) were readmitted twice, 2 (2%) were readmitted 3 times, and 3 (3%) were readmitted 4 times over the subsequent year. Cardiac causes of 1-year readmission included recurrent MI in 23 (9%) and decompensated heart failure in 18 (7%) patients. Depressed left ventricular systolic function (hazard ratio, 2.23; 95% confidence interval, 2.00-2.44; P = 0.0003) and diabetes mellitus (hazard ratio, 1.60; 95% confidence interval, 1.38-1.82; P = 0.029) were associated with a significantly higher risk of readmission at 1 year.

CONCLUSION

Following acute MI, patients are readmitted for cardiac and noncardiac diagnoses well beyond the 30-day mark. This is likely a function of the vulnerability of the patient population rather than a reflection of the medical care provided. More frequent surveillance may attenuate this problem.

Excellence in Population Health: A Successful Community-Based Care Transitions Program Model

PURPOSE/OBJECTIVE

The Community-based Care Transitions Program (CCTP) defined a broad spectrum of interventions and services for elderly patients at high risk of hospital readmission. The purposes for a CCTP as developed by the Centers for Medicare & Medicaid Services are to improve transitions of beneficiaries from the inpatient hospital setting to other care settings, to improve quality of care, to reduce readmissions for high-risk beneficiaries, and to document measurable savings. The goals for this CCTP initiative were as follows: achievement of a 20% reduction in the 30-day all-cause readmission rate across all partner hospitals compared with baseline; reduction in the 30-day all-cause readmission rate among the high-risk cohort served; and achievement of the target volumes for full enrollment.

PRIMARY PRACTICE SETTINGS

The partnership included acute care institutions and community-based care organizations that have been involved with care transition programs for years and have a long history of working collaboratively to provide services to a largely low-income, underserved, and ethnically and racially diverse target population.

FINDINGS/CONCLUSIONS

The program successfully transitioned to full operation within the first year of inception. To date, the partnership of the acute hospital setting and the community-based organizations has reached and provided services to nearly 8,000 total individuals, surpassing our target enrollment goal. To date, the readmission rate has decreased to 12.5%, which is an 11% decline since inception of the program.

IMPLICATIONS FOR CASE MANAGEMENT PRACTICE

The collaboration of health care providers, social workers, nurse practitioners, physicians, community pharmacists, and the visiting nurses is integral to a successful transition from hospital to home. Home visits by the transition facilitators allowed for the coordination of a multitude of services in the community, including those previously available to patients in the past that have rarely been accessed. Including a pharmacist on the team provided teaching regarding medication adherence, medication management, and pharmacy services, which added to interventions to decrease future hospitalizations.

Hospital Characteristics Associated With Postdischarge Hospital Readmission, Observation, and Emergency Department Utilization

BACKGROUND

Whether types of hospitals with high readmission rates also have high overall postdischarge acute care utilization (including emergency department and observation care) is unknown.

DESIGN

Cross-sectional analysis.

SUBJECTS

Nonfederal United States acute care hospitals.

MEASURES

Using methodology established by the Centers for Medicare & Medicaid Services, we calculated each hospital's "excess days in acute care" for fee-for-service (FFS) Medicare beneficiaries aged over 65 years discharged after hospitalization for acute myocardial infarction, heart failure (HF), or pneumonia, representing the mean difference between predicted and expected total days of acute care utilization in the 30 days following hospital discharge, per 100 discharges. We assessed the multivariable association of 8 hospital characteristics with excess days in acute care and the proportion of hospitals with each characteristic that were statistical outliers (95% credible interval estimate does not include 0).

RESULTS

We included 2184 hospitals for acute myocardial infarction [228 (10.4%) better than expected, 549 (25.1%) worse than expected], 3720 hospitals for HF [484 (13.0%) better and 840 (22.6%) worse], and 4195 hospitals for pneumonia [673 (16.0%) better, 1005 (24.0%) worse]. Results for all conditions were similar. Worse than expected outliers for pneumonia included: 18.8% of safety net hospitals versus 26.1% of nonsafety net hospitals; 16.7% of public hospitals versus 33.1% of for-profit hospitals; 19.5% of nonteaching hospitals versus 52.2% of major teaching hospitals; 7.9% of rural hospitals versus 42.1% of large urban hospitals; 5.9% of hospitals with 24-<50 beds versus 58% of hospitals with >500 beds; and 29.0% of hospitals with nurse-to-bed ratios >1.0-1.5 versus 21.7% of hospitals with ratios >2.0.

CONCLUSIONS

Including emergency department and observation stays in measures of postdischarge utilization produces similar results as measuring only readmissions in that major teaching, urban and for-profit hospitals still perform disproportionately poorly versus nonteaching or public hospitals. However, it enables identification of more outliers and a more granular assessment of the association of hospital factors and outcomes.

Fall Rates in Urban and Rural Nursing Units: Does Location Matter?

Patient falls remain a leading adverse event in hospitals. In a study of 65 rural hospitals with 222 nursing units and 560 urban hospitals with 4274 nursing units, we found that geographic region, unit type, and nurse staffing, education, experience, and outcomes were associated with fall rates. Implications include specific attention to fall prevention in rehabilitation units, creating better work environments that promote nurse retention, and provide RN-BSN educational opportunities.

Annual Report on Health Care for Children and Youth in the United States: Focus on 30-Day Unplanned Inpatient Readmissions, 2009 to 2014

OBJECTIVE

To describe trends in unplanned 30-day all-condition hospital readmissions for children aged 1 to 17 years between 2009 and 2014.

METHODS

Analysis was conducted with the 2009-14 Nationwide Readmissions Database from the Agency for Healthcare Research and Quality's Healthcare Cost and Utilization Project. Annual hospital readmission rates, resource use, and the most common reasons for readmission were calculated for the 2009-14 period.

RESULTS

The rate of readmission for children aged 1 to 17 years was essentially stable between 2009 and 2014 (5.5% in 2009 and 5.9% in 2014). In 2009, the most common reason (principal diagnosis) for readmission was sickle cell anemia, whereas in 2014 the most common reason was epilepsy. Pneumonia fell from the second to the sixth most common reason for readmission over this period (from 3832 to 2418 stays). Other respiratory infections were among the top 10 principal readmission diagnoses in 2009, but not in 2014. Septicemia was among the 10 most common reasons for readmission in 2014, but not in 2009. Although the average cost of index (ie, initial) stays with a subsequent readmission were similar in 2009 and 2014, the average cost of index stays without a readmission and cost of readmission stays increased by approximately 23%. In both 2009 and 2014, the average cost of the index stays with a subsequent readmission was 73% to 89% higher than that of the index stays of children who were not readmitted within 30 days. The average cost of index stays preceding a readmission was 33% to 45% higher than average costs for readmitted stays. In 2014, the aggregate cost of index stays plus readmissions was $1.58 billion, with 42.9% of the costs attributable to readmissions. Regarding the average costs and lengths of stay for the 10 most common readmission diagnoses, in 2009 the average cost per stay for complications of devices, implants, or grafts was nearly 5 times greater than that of asthma ($21,200 vs $4500, respectively). In 2014, average cost per stay ranged from $5500 for asthma to $39,500 for septicemia. In 2009, the average length of stay (LOS) for complications of devices, implants, or grafts was more than 3 three times higher than that for asthma (7.8 days vs 2.5 days, respectively), and in 2014, the average LOS for septicemia was nearly 4 times higher than that for asthma (10.4 days vs. 2.6 days).

CONCLUSIONS

This study provides a baseline assessment for examining trends in 30-day unplanned pediatric readmissions, an important quality metric as the provisions of the Children's Health Insurance Program Reauthorization Act and the Affordable Care Act are changed and implemented in the future. More than 50,000 pediatric hospital stays in 2014 occurred within 30 days of a previous hospitalization, with an average cost of $13,800. This report is timely, as the health care system works to become more patient-centered and public and private payers grapple with how to pay for quality care for children. The report provides baseline information that can be used to further explore ways to reduce unplanned readmissions.

Practice Environment Characteristics Associated With Missed Nursing Care

PURPOSE

To examine which characteristics of the practice environment were associated with missed nursing care in U.S. acute care hospital units.

DESIGN

A descriptive, correlational study used secondary analysis of the 2015 National Database of Nursing Quality Indicators^® Registered Nurse (RN) Survey data. Subscales of the Practice Environment Scale of the Nursing Work Index were used to measure practice environment characteristics. The sample included 1,583 units in 371 hospitals, containing survey responses from 31,650 RNs.

METHODS

Multilevel logistic regression was performed to estimate the effects of the practice environment characteristics on missed care, controlling for hospital and unit characteristics.

RESULTS

About 84.1% of unit RNs reported missing at least one of the 15 necessary care activities. Good environment units had 63.3% significantly lower odds of having RNs miss care activities than poor environment units. Units had 81.5% lower odds of having RNs miss any necessary activities with 1 point increase of the staffing and resource adequacy score; 21.9% lower odds for 1 point increase in the nurse-physician relations score; and approximately 2.1 times higher odds with 1 point increase in the nurse participation in hospital affairs score.

CONCLUSIONS

Good environments were significantly associated with lower levels of missed care. The impact on missed care differed by the characteristics of the practice environment.

CLINICAL RELEVANCE

Hospital and nursing administrators should maintain good practice environments for nurses to reduce missed care activities and thus potentially improve patient outcomes. Specifically, their efforts should be targeted on improving staffing and resource adequacy and nurse-physician relations and on reducing workloads on hospital affairs.

Proprietary management and higher readmission rates: A correlation

INTRODUCTION

Reducing preventable readmissions among Medicare beneficiaries is an effective way to not only reduce the exorbitantly rising cost in healthcare but also as a measure to improve the quality of patient care. Many of the previous efforts in reducing readmission rate of patients have not been very successful because of ill-defined quality measures, improper data collection methods and lack of effective strategies based on data driven solutions.

METHODS

In this study, we analyzed the readmission data of patients for six major diseases including acute myocardial infarction (AMI), heart failure (HF), coronary artery bypass graft (CABG), pneumonia (PN), chronic obstructive pulmonary disease (COPD), and total hip arthroplasty and/or total knee arthroplasty (THA/TKA) from the Center for Medicare and Medicaid Readmissions Reduction Program (HRRP) program for the period 2012-2015 in context with the ownership structure of the hospitals.

RESULTS

Our analysis demonstrates that the readmission rates of patients were statistically higher in proprietary (for profit) hospitals compared to the government and non-profit hospitals which was independent of their geographical distribution across all six major diseases.

CONCLUSION

This finding we believe has strong implications for policy makers to mitigate any potential risks in the quality of patient care arising from unintended revenue pressure in healthcare institutions.

Differential Impact of Hospital and Community Factors on Medicare Readmission Penalties

OBJECTIVE

To identify hospital/county characteristics and sources of regional heterogeneity associated with readmission penalties.

DATA SOURCES/STUDY SETTING

Acute care hospitals under the Hospital Readmissions Reduction Program from fiscal years 2013 to 2018 were linked to data from the Annual Hospital Association, Centers for Medicare and Medicaid Services, Medicare claims, Hospital Compare, Nursing Home Compare, Area Resource File, Health Inequity Project, and Long-term Care Focus. The final sample contained 3,156 hospitals in 1,504 counties.

DATA COLLECTION/EXTRACTION METHODS

Data sources were combined using Medicare hospital identifiers or Federal Information Processing Standard codes.

STUDY DESIGN

A two-level hierarchical model with correlated random effects, also known as the Mundlak correction, was employed with hospitals nested within counties.

PRINCIPAL FINDINGS

Over a third of the variation in readmission penalties was attributed to the county level. Patient sociodemographics and the surrounding access to and quality of care were significantly associated with penalties. Hospital measures of Medicare volume, percentage dual-eligible and Black patients, and patient experience were correlated with unobserved area-level factors that also impact penalties.

CONCLUSIONS

As the readmission risk adjustment does not include any community-level characteristics or geographic controls, the resulting endogeneity bias has the potential to disparately penalize certain hospitals.

Incidence and predictors of 30-day readmissions in patients hospitalized with chronic pancreatitis: A nationwide analysis

BACKGROUND

Patients with chronic pancreatitis are prone to frequent readmissions. The aim of this study is to evaluate the rate and predictors of 30-day readmissions in patients with chronic pancreatitis using the Nationwide Readmission Database (NRD).

METHODS

We performed a retrospective analysis of all adult patients with the principal discharge diagnosis of chronic pancreatitis from 2010 through 2014. We excluded patients who died during the hospitalization. Multivariate Cox proportional hazard regression was performed to identify demographic, clinical, and hospital factors that associated with 30-day unplanned readmissions.

RESULTS

During the study period, 25,259 patients had the principal discharge diagnosis of chronic pancreatitis and survived the index hospitalization. Of these, 6477 (26.7%) were readmitted within 30 days. Younger age group, males, length of stay >5 days, admission to a large, metropolitan hospital, and several comorbidities (renal failure, rheumatic disease, chronic anemia, heart failure, depression, drug abuse, psychosis, and diabetes) were independently associated with increased risk of 30-day readmission. ERCP, pancreatic surgery, and obesity were associated with lower risk. The most common reasons for readmissions were acute pancreatitis (30%), chronic pancreatitis (17%), pseudocyst (2%), and abdominal pain (6%).

CONCLUSIONS

One in four patients with chronic pancreatitis is readmitted within 30 days (26.7%). Pancreatic disease accounts for at least half of all readmissions. Several baseline comorbidities and characteristics are associated with 30-day readmission risk after index admission. Knowledge of these predictors can help design interventions to target high-risk patients and reduce readmissions and costs of care.

Applicability of Publicly Reported Hospital Readmission Measures to Unreported Conditions and Other Patient Populations: A Cross-sectional All-Payer Study

BACKGROUND

Readmission rates after hospitalizations for heart failure (HF), acute myocardial infarction (AMI), and pneumonia among Medicare beneficiaries are used to assess quality and determine reimbursement. Whether these measures reflect readmission rates for other conditions or insurance groups is unknown.

OBJECTIVE

To investigate whether hospital-level 30-day readmission measures for publicly reported conditions (HF, AMI, and pneumonia) among Medicare patients reflect those for Medicare patients hospitalized for unreported conditions or non-Medicare patients hospitalized with HF, AMI, or pneumonia.

DESIGN

Cross-sectional.

SETTING

Population-based.

PARTICIPANTS

Hospitals in the all-payer Nationwide Readmissions Database in 2013 and 2014.

MEASUREMENTS

Hospital-level 30-day all-cause risk-standardized excess readmission ratios (ERRs) were compared for 3 groups of patients: Medicare beneficiaries admitted for HF, AMI, or pneumonia (Medicare reported group); Medicare beneficiaries admitted for other conditions (Medicare unreported group); and non-Medicare beneficiaries admitted for HF, AMI, or pneumonia (non-Medicare group).

RESULTS

Within-hospital differences in ERRs varied widely among groups. Medicare reported ratios differed from Medicare unreported ratios by more than 0.1 for 29% of hospitals and from non-Medicare ratios by more than 0.1 for 46% of hospitals. Among hospitals with higher readmission ratios, ERRs for the Medicare reported group tended to overestimate ERRs for the non-Medicare group but underestimate those for the Medicare unreported group.

LIMITATION

Medicare groups and risk adjustment differed slightly from those used by the Centers for Medicare & Medicaid Services.

CONCLUSION

Hospital ERRs, as estimated by Medicare to determine financial penalties, have poor agreement with corresponding measures for populations and conditions not tied to financial penalties. Current publicly reported measures may not be good surrogates for overall hospital quality related to 30-day readmissions.

PRIMARY FUNDING SOURCE

Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology.

Is Profit Status of Inpatient Rehabilitation Facilities Independently Associated With 30-Day Unplanned Hospital Readmission for Medicare Beneficiaries?

OBJECTIVE

To investigate the effects of facility-level factors on 30-day unplanned risk-adjusted hospital readmission after discharge from inpatient rehabilitation facilities (IRFs).

DESIGN

Study using 100% Medicare claims data, covering 269,306 discharges from 1094 IRFs between October 2010 and September 2011.

SETTING

IRFs with at least 30 discharges.

PARTICIPANTS

A total number of 1094 IRFs (N=269,306) serving Medicare fee-for-service beneficiaries.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Risk-standardized readmission rate (RSRR) for 30-day hospital readmission.

RESULTS

Profit status was the only provider-level IRF characteristic significantly associated with unplanned readmissions. For-profit IRFs had a significantly higher RSRR (13.26±0.51) than did nonprofit IRFs (13.15±0.47) (P<.001). After controlling for all other facility characteristics (except for accreditation status because of its collinearity with facility type), for-profit IRFs had a 0.1% point higher RSRR than did nonprofit IRFs, and census region was the only significant region-level characteristic, with the South showing the highest RSRR of all regions (type III test, P=.005 for both).

CONCLUSIONS

Our findings support the inclusion of profit status on the IRF Compare website (a platform including IRF comparators to indicate quality of services). For-profit IRFs had a higher RSRR than did nonprofit IRFs for Medicare beneficiaries. The South had a higher RSRR than did other regions. The RSRR difference between for-profit and nonprofit IRFs could be due to the combined effects of organizational and regional factors.