Reliability of 30-Day Readmission Measures Used in the Hospital Readmission Reduction Program

Hospital variation of outcomes in status epilepticus

OBJECTIVE

Understanding factors driving variation in status epilepticus outcomes would be critical to improve care. We evaluated the degree to which patient and hospital characteristics explained hospital-to-hospital variability in intubation and postacute outcomes.

METHODS

This was a retrospective cohort study of Medicare beneficiaries admitted with status epilepticus between 2009 and 2019. Outcomes included intubation, discharge to a facility, and 30- and 90-day readmissions and mortality. Multilevel models calculated percent variation in each outcome due to hospital-to-hospital differences.

RESULTS

We included 29 150 beneficiaries. The median age was 68 years (interquartile range [IQR] = 57-78), and 18 084 (62%) were eligible for Medicare due to disability. The median (IQR) percentages of each outcome across hospitals were: 30-day mortality 25% (0%-38%), any 30-day readmission 14% (0%-25%), 30-day status epilepticus readmission 0% (0%-3%), 30-day facility stay 40% (25%-53%), and intubation 46% (20%-61%). However, after accounting for many hospitals with small sample size, hospital-to-hospital differences accounted for 2%-6% of variation in all unadjusted outcomes, and approximately 1%-5% (maximally 8% for 30-day readmission for status epilepticus) after adjusting for patient, hospitalization, and/or hospital characteristics. Although many characteristics significantly predicted outcomes, the largest effect size was cardiac arrest predicting death (odds ratio = 10.1, 95% confidence interval = 8.8-11.7), whereas hospital characteristics (e.g., staffing, accreditation, volume, setting, services) all had lesser effects.

SIGNIFICANCE

Hospital-to-hospital variation explained little variation in studied outcomes. Rather, certain patient characteristics (e.g., cardiac arrest) had greater effects. Interventions to improve outcomes after status epilepticus may be better focused on individual or prehospital factors, rather than at the inpatient systems level.

Annual Variation in 30-Day Risk-Adjusted Readmission Rates in U.S. Children's Hospitals

OBJECTIVE

Reducing pediatric readmissions has become a national priority; however, the use of readmission rates as a quality metric remains controversial. The goal of this study was to examine short-term stability and long-term changes in hospital readmission rates.

METHODS

Data from the Pediatric Health Information System were used to compare annual 30-day risk-adjusted readmission rates (RARRs) in 47 US children's hospitals from 2016 to 2017 (short-term) and 2016 to 2019 (long-term). Pearson correlation coefficients and weighted Cohen's Kappa statistics were used to measure correlation and agreement across years for hospital-level RARRs and performance quartiles.

RESULTS

Median (IQR) 30-day RARRs remained stable from 7.7% (7.0-8.3) in 2016 to 7.6% (7.0-8.1) in 2019. Individual hospital RARRs in 2016 were strongly correlated with the same hospital's 2017 rate (R2 = 0.89 [95% confidence interval (CI) 0.80-0.94]) and moderately correlated with those in 2019 (R2 = 0.49 [95%CI 0.23-0.68]). Short-term RARRs (2016 vs 2017) were more highly correlated for medical conditions than surgical conditions, but correlations between long-term medical and surgical RARRs (2016 vs 2019) were similar. Agreement between RARRs was higher when comparing short-term changes (0.73 [95%CI 0.59-0.86]) than long-term changes (0.45 [95%CI 0.27-0.63]). From 2016 to 2019, RARRs increased by ≥1% in 7 (15%) hospitals and decreased by ≥1% in 6 (13%) hospitals. Only 7 (15%) hospitals experienced reductions in RARRs over the short and long-term.

CONCLUSIONS

Hospital-level performance on RARRs remained stable with high agreement over the short-term suggesting stability of readmission measures. There was little evidence of sustained improvement in hospital-level performance over multiple years.

Development and implementation of a nurse-based remote patient monitoring program for ambulatory disease management

Introduction

Numerous factors are intersecting in healthcare resulting in an increased focus on new tools and methods for managing care in patients' homes. Remote patient monitoring (RPM) is an option to provide care at home and maintain a connection between patients and providers to address ongoing medical issues.

Methods

Mayo Clinic developed a nurse-led RPM program for disease and post-procedural management to improve patient experience, clinical outcomes, and reduce health care utilization by more directly engaging patients in their health care. Enrolled patients are sent a technology package that includes a digital tablet and peripheral devices for the collection of symptoms and vital signs. The data are transmitted from to a hub integrated within the electronic health record. Care team members coordinate patient needs, respond to vital sign alerts, and utilize the data to inform and provide individualized patient assessment, patient education, medication management, goal setting, and clinical care planning.

Results

Since its inception, the RPM program has supported nearly 22,000 patients across 17 programs. Patients who engaged in the COVID-19 RPM program experienced a significantly lower rate of 30-day, all-cause hospitalization (13.7% vs. 18.0%, P = 0.01), prolonged hospitalization >7 days (3.5% vs. 6.7%, P = 0.001), intensive care unit (ICU) admission (2.3% vs. 4.2%, P = 0.01), and mortality (0.5% vs. 1.7%, P = 0.01) when compared with those enrolled and unengaged with the technology. Patients with chronic conditions who were monitored with RPM upon hospital discharge were significantly less likely to experience 30-day readmissions (18.2% vs. 23.7%, P = 0.03) compared with those unmonitored. Ninety-five percent of patients strongly agreed or agreed they were likely to recommend RPM to a friend or family member.

Conclusions

The Mayo Clinic RPM program has generated positive clinical outcomes and is satisfying for patients. As technology advances, there are greater opportunities to enhance this clinical care model and it should be extended and expanded to support patients across a broader spectrum of needs. This report can serve as a framework for health care organizations to implement and enhance their RPM programs in addition to identifying areas for further evolution and exploration in developing RPM programs of the future.

Combined impact of Medicare's hospital pay for performance programs on quality and safety outcomes is mixed

BACKGROUND

Three major hospital pay for performance (P4P) programs were introduced by the Affordable Care Act and intended to improve the quality, safety and efficiency of care provided to Medicare beneficiaries. The financial risk to hospitals associated with Medicare's P4P programs is substantial. Evidence on the positive impact of these programs, however, has been mixed, and no study has assessed their combined impact. In this study, we examined the combined impact of Medicare's P4P programs on clinical areas and populations targeted by the programs, as well as those outside their focus.

METHODS

We used 2007-2016 Healthcare Cost and Utilization Project State Inpatient Databases for 14 states to identify hospital-level inpatient quality indicators (IQIs) and patient safety indicators (PSIs), by quarter and payer (Medicare vs. non-Medicare). IQIs and PSIs are standardized, evidence-based measures that can be used to track hospital quality of care and patient safety over time using hospital administrative data. The study period of 2007-2016 was selected to capture multiple years before and after introduction of program metrics. Interrupted time series was used to analyze the impact of the P4P programs on study outcomes targeted and not targeted by the programs. In sensitivity analyses, we examined the impact of these programs on care for non-Medicare patients.

RESULTS

Medicare P4P programs were not associated with consistent improvements in targeted or non-targeted quality and safety measures. Moreover, mortality rates across targeted and untargeted conditions were generally getting worse after the introduction of Medicare's P4P programs. Trends in PSIs were extremely mixed, with five outcomes trending in an expected (improving) direction, five trending in an unexpected (deteriorating) direction, and three with insignificant changes over time. Sensitivity analyses did not substantially alter these results.

CONCLUSIONS

Consistent with previous studies for individual programs, we detect minimal, if any, effect of Medicare's hospital P4P programs on quality and safety. Given the growing evidence of limited impact, the administrative cost of monitoring and enforcing penalties, and potential increase in mortality, CMS should consider redesigning their P4P programs before continuing to expand them.

Luck of the draw: Role of chance in the assignment of medicare readmissions penalties

Pay-for-performance programs are one strategy used by health plans to improve the efficiency and quality of care delivered to beneficiaries. Under such programs, providers are often compared against their peers in order to win bonuses or face penalties in payment. Yet luck has the potential to affect performance assessment through randomness in the sorting of patients among providers or through random events during the evaluation period. To investigate the impact luck can have on the assessment of performance, we investigated its role in assigning penalties under Medicare's Hospital Readmissions Reduction Policy (HRRP), a program that penalizes hospitals with excess readmissions. We performed simulations that estimated program hospitals' 2015 readmission penalties in 1,000 different hypothetical fiscal years. These hypothetical fiscal years were created by: (a) randomly varying which patients were admitted to each hospital and (b) randomly varying the readmission status of discharged patients. We found significant differences in penalty sizes and probability of penalty across hypothetical fiscal years, signifying the importance of luck in readmission performance under the HRRP. Nearly all of the impact from luck arose from events occurring after hospital discharge. Luck played a smaller role in determining penalties for hospitals with more beds, teaching hospitals, and safety-net hospitals.

Center Variability in Medicare Claims-Based Publicly Reported Transcatheter Aortic Valve Replacement Outcome Measures

Background

Public reporting of transcatheter aortic valve replacement (TAVR) claims–based outcome measures is used to identify high‐ and low‐performing centers. Whether claims‐based TAVR outcomes can reliably be used for center‐level comparisons is unknown. In this study, we sought to evaluate center variability in claims‐based TAVR outcomes used in public reporting.

Methods and Results

The study sample included 119 554 Medicare beneficiaries undergoing TAVR between January 2014 and October 2018 based on procedure codes in 100% Medicare inpatient claims. Multivariable hierarchical logistic regression was used to estimate center‐specific adjusted rates and reliability (R) of 30‐day mortality, discharge not to home/self‐care, 30‐day stroke, and 30‐day readmission. Reliability was defined as the ratio of between‐hospital variation to the sum of the between‐ and within‐hospital variation. The median (interquartile range [IQR]) center‐level adjusted outcome rates were 3.1% (2.9%–3.4%) for 30‐day mortality, 41.4% (31.3%–53.4%) for discharge not to home, 2.5% (2.3%–2.7%) for 30‐day stroke, and 14.9% (14.4%–15.5%) for 30‐day readmission. Median reliability was highest for the discharge not to home measure (R=0.95; IQR, 0.94–0.97), followed by the 30‐day stroke (R=0.92; IQR, 0.87–0.94), 30‐day mortality (R=0.86; IQR, 0.81–0.91), and 30‐day readmission measures (R=0.42; IQR, 0.35–0.51). Across outcomes, there was an inverse relationship between center volume and measure reliability.

Conclusions

Claims‐based TAVR outcome measures for mortality, discharge not to home, and stroke were reliable measures for center‐level comparisons, but readmission measures were unreliable. Stakeholders should consider these findings when evaluating claims‐based measures to compare center‐level TAVR performance.

Care Strategies for Reducing Hospital Readmissions Using Stochastic Programming

A hospital readmission occurs when a patient has an unplanned admission to a hospital within a specific time period of discharge from an earlier or initial hospital stay. Preventable readmissions have turned into a critical challenge for the healthcare system globally, and hospitals seek care strategies that reduce the readmission burden. Some countries have developed hospital readmission reduction policies, and in some cases, these policies impose financial penalties for hospitals with high readmission rates. Decision models are needed to help hospitals identify care strategies that avoid financial penalties, yet maintain balance among quality of care, the cost of care, and the hospital’s readmission reduction goals. We develop a multi-condition care strategy model to help hospitals prioritize treatment plans and allocate resources. The stochastic programming model has probabilistic constraints to control the expected readmission probability for a set of patients. The model determines which care strategies will be the most cost-effective and the extent to which resources should be allocated to those initiatives to reach the desired readmission reduction targets and maintain high quality of care. A sensitivity analysis was conducted to explore the value of the model for low- and high-performing hospitals and multiple health conditions. Model outputs are valuable to hospitals as they examine the expected cost of hitting its target and the expected improvement to its readmission rates.

Characteristics and Trends of the Hospital Standardized Readmission Ratios for Pneumonia: A Retrospective Observational Study Using Japanese Administrative Claims Data from 2010 to 2018

Previous studies indicated that optimal care for pneumonia during hospitalization might reduce the risk of in-hospital mortality and subsequent readmission. This study was a retrospective observational study using Japanese administrative claims data from April 2010 to March 2019. We analyzed data from 167,120 inpatients with pneumonia ≥15 years old in the benchmarking project managed by All Japan Hospital Association. Hospital-level risk-adjusted ratios of 30-day readmission for pneumonia were calculated using multivariable logistic regression analyses. The Spearman’s correlation coefficient was used to assess the correlation in each consecutive period. In the analysis using complete 9-year data including 54,756 inpatients, the hospital standardized readmission ratios (HSRRs) showed wide variation among hospitals and improvement trend (r = −0.18, p = 0.03). In the analyses of trends in each consecutive period, the HSRRS were positively correlated between ‘2010–2012’ and ‘2013–2015’ (r = 0.255, p = 0.010), and ‘2013–2015’ and ‘2016–2018’ (r = 0.603, p < 0.001). This study denoted the HSRRs for pneumonia could be calculated using Japanese administrative claims data. The HSRRs significantly varied among hospitals with comparable case-mix, and could relatively evaluate the quality of preventing readmission including long-term trends. The HSRRs can be used as yet another measure to help improve quality of care over time if other indicators are examined in parallel.

Readmission Rates for Chronic Obstructive Pulmonary Disease Under the Hospital Readmissions Reduction Program: an Interrupted Time Series Analysis

BACKGROUND

Hospital readmission rates decreased for myocardial infarction (AMI), heart failure (CHF), and pneumonia with implementation of the first phase of the Hospital Readmissions Reduction Program (HRRP). It is not established whether readmissions fell for chronic obstructive pulmonary disease (COPD), an HRRP condition added in 2014.

OBJECTIVE

We sought to determine whether HRRP penalties influenced COPD readmissions among Medicare, Medicaid, or privately insured patients.

DESIGN

We analyzed a retrospective cohort, evaluating readmissions across implementation periods for HRRP penalties ("pre-HRRP" January 2010-April 2011, "implementation" May 2011-September 2012, "partial penalty" October 2012-September 2014, and "full penalty" October 2014-December 2016).

PATIENTS

We assessed discharged patients ≥ 40 years old with COPD versus those with HRRP Phase 1 conditions (AMI, CHF, and pneumonia) or non-HRRP residual diagnoses in the Nationwide Readmissions Database.

INTERVENTIONS

HRRP was announced and implemented during this period, forming a natural experiment.

MEASUREMENTS

We calculated differences-in-differences (DID) for 30-day COPD versus HRRP Phase 1 and non-HRRP readmissions.

KEY RESULTS

COPD discharges for 1.2 million Medicare enrollees were compared with 22 million non-HRRP and 3.4 million HRRP Phase 1 discharges. COPD readmissions decreased from 19 to 17% over the study. This reduction was significantly greater than non-HRRP conditions (DID - 0.41%), but not HRRP Phase 1 (DID + 0.02%). A parallel trend was observed in the privately insured, with significant reduction compared with non-HRRP (DID - 0.83%), but not HRRP Phase 1 conditions (DID - 0.45%). Non-significant reductions occurred in Medicaid (DID - 0.52% vs. non-HRRP and - 0.21% vs. Phase 1 conditions).

CONCLUSIONS

In Medicare, HRRP implementation was associated with reductions in COPD readmissions compared with non-HRRP controls but not versus other HRRP conditions. Parallel findings were observed in commercial insurance, but not in Medicaid. Condition-specific penalties may not reduce readmissions further than existing HRRP trends.

Relative Effects of the Hospital Readmissions Reduction Program on Hospitals That Serve Poorer Patients

IMPORTANCE

Hospitals that serve poorer populations have higher readmission rates. It is unknown whether these hospitals effectively lowered readmission rates in response to the Hospital Readmissions Reduction Program (HRRP).

OBJECTIVE

To compare pre-post differences in readmission rates among hospitals with different proportion of dual-eligible patients both generally and among the most highly penalized (ie, low performing) hospitals.

DESIGN

Retrospective cohort study using piecewise linear model with estimated hospital-level risk-standardized readmission rates (RSRRs) as the dependent variable and a change point at HRRP passage (2010). Economic burden was assessed by proportion of dual-eligibles served.

SETTING

Acute care hospitals within the United States.

PARTICIPANTS

Medicare fee-for-service beneficiaries aged 65 years or older discharged alive from January 1, 2003 to November 30, 2014 with a principal discharge diagnosis of acute myocardial infarction (AMI), congestive heart failure (CHF), and pneumonia.

MAIN OUTCOME AND MEASURE

Decrease in hospital-level RSRRs in the post-law period, after controlling for the pre-law trend.

RESULTS

For AMI, the pre-post difference between hospitals that service high and low proportion of dual-eligibles was not significant (-65 vs. -64 risk-standardized readmissions per 10000 discharges per year, P=0.0678). For CHF, RSRRs declined more at high than low dual-eligible hospitals (-79 vs. -75 risk-standardized readmissions per 10000 discharges per year, P=0.0006). For pneumonia, RSRRs declined less at high than low dual-eligible hospitals (-44 vs. -47 risk-standardized readmissions per 10000 discharges per year, P=0.0003). Among the 742 highest penalized hospitals and all conditions, the pre-post decline in rate of change of RSRRs was less for high dual-eligible hospitals than low dual-eligible hospitals (-68 vs. -74 risk-standardized readmissions per 10000 discharges per year for AMI, -88 vs. -97 for CHF, and -47 vs. -56 for pneumonia, P<0.0001 for all).

CONCLUSIONS AND RELEVANCE

For all hospitals, differences in pre-post trends in RSRRs varied with disease conditions. However, for the highest-penalized hospitals, the pre-post decline in RSRRs was greater for low than high dual-eligible hospitals for all penalized conditions. These results suggest that high penalty, high dual-eligible hospitals may be less able to improve performance on readmission metrics.

Evidence of the Linkage Between Hospital-based Care Coordination Strategies and Hospital Overall (Star) Ratings

BACKGROUND

In the new era of value-based payment models and pay for performance, hospitals are in search of the silver bullet strategy or bundle of strategies capable of improving their performance on quality measures.

OBJECTIVES

To determine whether there is an association between adoption of hospital-based care coordination strategies and Centers for Medicare and Medicaid Services overall hospital quality (star) ratings and readmission rates.

RESEARCH DESIGN

We used survey data from the American Hospital Association (AHA) and categorized respondents by the number of care coordination strategies that they reported having widely implemented. We used multiple logistic regression models to examine the association between the number of strategies and hospital overall rating performance and disease-specific 30-day excess readmission ratios, while controlling for hospital and county characteristics and state-fixed effects.

SUBJECTS

A total of 710 general acute care noncritical access hospitals that received star ratings and responded to the 2015 AHA Care Systems and Payment Survey.

MEASURES

Centers for Medicare and Medicaid Services overall hospital ratings, 30-day excess readmission ratios.

RESULTS

As compared with hospitals with 0-2 strategies, hospitals with 3 to 4 strategies (P=0.007), 5-7 strategies (P=0.002), or 8-12 strategies (P=0.002) had approximately 2.5× the odds of receiving a top rating (4 or 5 stars). Care coordination strategies were positively associated with lower 30-day readmission ratios for patients with chronic medical conditions, but not for surgical patients. Medication reconciliation, visit summaries, outreach after discharge, discharge care plans, and disease management programs were each individually associated with top ratings.

CONCLUSIONS

Care coordination strategies are associated with high overall hospital ratings.

Comorbidity and thirty-day hospital readmission odds in chronic obstructive pulmonary disease: a comparison of the Charlson and Elixhauser comorbidity indices

Background

Readmissions following exacerbations of chronic obstructive pulmonary disease (COPD) are prevalent and costly. Multimorbidity is common in COPD and understanding how comorbidity influences readmission risk will enable health systems to manage these complex patients.

Objectives

We compared two commonly used comorbidity indices published by Charlson and Elixhauser regarding their ability to estimate readmission odds in COPD and determine which one provided a superior model.

Methods

We analyzed discharge records for COPD from the Nationwide Readmissions Database spanning 2010 to 2016. Inclusion and readmission criteria from the Hospital Readmissions Reduction Program were utilized. Elixhauser and Charlson Comorbidity Index scores were calculated from published methodology. A mixed-effects logistic regression model with random intercepts for hospital clusters was fit for each comorbidity index, including year, patient-level, and hospital-level covariates to estimate odds of thirty-day readmissions. Sensitivity analyses included testing age inclusion thresholds and model stability across time.

Results

In analysis of 1.6 million COPD discharges, readmission odds increased by 9% for each half standard deviation increase of Charlson Index scores and 13% per half standard deviation increase of Elixhauser Index scores. Model fit was slightly better for the Elixhauser Index using information criteria. Model parameters were stable in our sensitivity analyses.

Conclusions

Both comorbidity indices provide meaningful information in prediction readmission odds in COPD with slightly better model fit in the Elixhauser model. Incorporation of comorbidity information into risk prediction models and hospital discharge planning may be informative to mitigate readmissions.

Hospital Variation in Spending for Lung Cancer Resection in Medicare Beneficiaries

BACKGROUND

As cancer payment models transition from fee for service toward payment "bundles" based on episodes of care, a deeper understanding of the costs associated with stage I lung cancer treatment becomes increasingly relevant. To better understand costs in early lung cancer care, we sought to characterize hospital-level variation in Medicare expenditure after lobectomy for stage I non-small cell lung carcinoma.

METHODS

Patients who were diagnosed with stage I non-small cell lung carcinoma from 2006 through 2011 and undergoing lobectomy were selected from the Surveillance, Epidemiology and End Results-Medicare linked database. We used Medicare claims to estimate costs of care in the 90 days after initial surgical hospitalization. Hospitals were grouped into quintiles of mean excess cost, calculated as the mean difference between observed costs and risk-adjusted predicted costs. The association between hospital factors and mean excess cost were compared across hospitals, including complication rates and hospital volume.

RESULTS

A total of 3530 patients underwent lobectomy at 156 hospitals. Hospitals in the lowest cost quintile had index hospitalizations $6226 less costly than predicted. Conversely, the most expensive hospital quintile had index hospital costs that were $6151 costlier than predicted. Increased costs were positively associated with the number of complications per patient (P < .001), but not hospital volume (P = .85).

CONCLUSIONS

Among Medicare beneficiaries undergoing lobectomy for stage I non-small cell lung carcinoma, the cost of perioperative care varied substantially across hospitals and was strongly associated with complication rate, but not hospital volume.

Impact of hospitals' Referral Region racial and ethnic diversity on 30-day readmission rates of older adults

Background: The Hospital Readmissions Reduction Program (HRRP) began decreasing Medicare payments to hospitals reporting high readmission rates for individuals over 65. Thus, financially incentivizing hospitals to improve quality performance on preventable readmissions. Well-established research indicates that minorities are more frequently readmitted to hospitals, but it is unknown if community diversity is associated with 30-day readmission rates. Objectives: To investigate the association between racial/ethnic diversity and hospitals' 30-day readmission rates. Methods: We linked the 2017 HRRP, American Hospital Association (AHA) database, Area Health Resource File, US Census Bureau Current Population Survey, and the Dartmouth Atlas HRR dataset to examine 30-day readmission rate for heart failure (HF), pneumonia (PN), acute myocardial infarction (AMI), and hip replacement (HR) surgery of 4,299 hospitals across 306 HRRs. Results: Our findings indicate a statistically significant negative relationship between diversity and 30-day readmission rates for HF, PN, AMI, and HR with a hospital referral region (HRR). Thus, hospitals located in HRRs with diverse populations are more likely to have higher 30-day readmission rates for all conditions under Medicare's HRRP Conclusion: Better discharge follow-up, interventions, and use of support staff aimed at meeting needs associated with differences in communities and cultures are likely to prove more fruitful than traditional one-size fits all approaches to care.

Predictors of 30-Day Readmission After Total Knee Arthroplasty: Analysis of 566,323 Procedures in the United Kingdom

BACKGROUND

All-cause 30-day readmission after total knee arthroplasty (TKA) is currently used as a measure of hospital performance in the United States and elsewhere. Readmissions from surgical causes may more accurately reflect preventability and costs. However, little is known about whether predictors of each type of readmission differ.

METHODS

All primary TKAs recorded in England's National Health Service administrative database from 2006 to 2015 were included. Multilevel logistic regression analysis was used to describe the effects of patient-related factors on 30-day readmission risk using 3 different readmission metrics: all-cause, surgical (defined using International Classification of Disease-10 primary admission diagnoses), and those resulting in return to theater (RTT).

RESULTS

In total, 566,323 procedures were recorded. The comorbidity with the highest odds ratio (OR) for all types of readmission was psychoses (RTT OR 2.52, P < .001). Obesity was a strong independent predictor of RTT (OR 1.36, P < .001) and had the highest population attributable fraction of any comorbidity (4.7%). Unicompartmental arthroplasty was associated with a significantly lower risk of all types of readmission when compared with TKA, with the effect being most pronounced for surgical readmission (OR 0.66, P < .001). RTT in the index episode increased the risk of RTT readmission (OR 2.80, P < .001), as did any emergency admission to hospital in the preceding 12 months (for >2 emergency admissions, all-cause OR 2.38, P < .001). Length of stay either more than or less than 2 days was associated with an increased risk of all-cause and surgical readmission but not RTT readmission.

CONCLUSION

Patient-related predictors of surgical and RTT readmission following TKA differ from those for all-cause readmission, but only the latter metric is in widespread use.

Does level of minority presence and hospital reimbursement policy influence hospital referral region health rankings in the United States

The shift from a fee-for-service payment to a value-based payment scheme, sparked by the Patient Protection and Affordable Care Act, introduced pay-for-performance programs such Hospital Value Based Purchasing. Previous inquiry has not considered how local community factors may affect hospital system performance. This study investigated the association between local health performance and minority population in a hospital referral region (HRR). The primary objective was to ascertain whether community diversity levels are significantly associated to local health performance guided by the ecological model. Secondary data analysis collected from the 2016 American Hospital Association, Area Health Resource File, Commonwealth Fund Scorecard on Local Health System Performance, and the Dartmouth Atlas HRR dataset was used. Our primary findings show that the more diverse a HRR is, the more likely it is to be associated with lower ranking for access and affordability prevention and treatment avoidable hospital use and cost as well as healthy lives. Total performance score was significantly related to a better health ranking on prevention and treatment, hospital use, and cost, as well as healthy lives. This research supports the assertion that communities, particularly minorities in those communities, affect local health care performance in a variety of ways.

Comparison of 3 Types of Readmission Rates for Measuring Hospital and Surgeon Performance After Primary Total Hip and Knee Arthroplasty

BACKGROUND

All-cause 30-day hospital readmission is in widespread use for monitoring and incentivizing hospital performance for patients undergoing total hip arthroplasty (THA) and total knee arthroplasty (TKA). However, little is known on the extent to which all-cause readmission is influenced by hospital or surgeon performance and whether alternative measures may be more valid.

METHODS

This is an observational study using multilevel modeling on English administrative data to determine the interhospital and intersurgeon variation for 3 readmission metrics: all-cause, surgical, and return-to-theater. Power calculations estimated the likelihood of identifying whether the readmission rate for a surgeon or hospital differed from the national average by a factor of 1.25, 1.5, 2, or 3 times, for both average and high-volume providers.

RESULTS

259,980 THAs and 311,033 TKAs were analyzed. Variations by both surgeons and hospitals were smaller for the all-cause measure than for the surgical or return-to-theater metrics, although statistical power to detect differences was higher. Statistical power to detect surgeon-level rates of 1.25 or 1.5 times the average was consistently low. However, at the hospital level, the surgical readmission measure showed more variation by hospital while maintaining excellent power to detect differences in rates between hospitals performing the average number of THA or TKA cases per year in England. In practice, more outliers than expected from purely random variation were found for all-cause and surgical readmissions, especially at hospital level.

CONCLUSION

The 30-day surgical readmission rate should be considered as an adjunctive measure to 30-day all-cause readmission rate when assessing hospital performance.

Most Hospitals Received Annual Penalties For Excess Readmissions, But Some Fared Better Than Others

The Hospital Readmissions Reduction Program (HRRP) initiated by the Affordable Care Act levies financial penalties against hospitals with excess thirty-day Medicare readmissions. We sought to understand the penalty burden over the program's first five years, focusing on characteristics of hospitals that received penalties during all five years, how penalties changed over time, and the relationship between baseline and subsequent performance. More than half of participating hospitals were penalized by the Centers for Medicare and Medicaid Services in all five years of the program. From fiscal years 2013 to 2017, the growth in average penalties was modest, doubling from 0.29 percent to 0.60 percent, despite increasing opportunities for penalization. The penalty burden was greater in hospitals that were urban, major teaching, large, or for-profit and that treated larger shares of Medicare or socioeconomically disadvantaged patients. Surprisingly, hospitals treating greater proportions of medically complex Medicare patients had a lower cumulative penalty burden compared to those treating fewer proportions of these patients. Lastly, we found that hospitals with high baseline penalties in the first year continued to receive significantly higher penalties in subsequent years. For many hospitals, the HRRP leads to persistent penalization and limited capacity to reduce penalty burden. Alternative structures might avoid persistent penalization, while still motivating reductions in hospital readmissions.

Should Emergency Department Attendances be Used With or Instead of Readmission Rates as a Performance Metric?: Comparison of Statistical Properties Using National Data

BACKGROUND

Hospital readmissions are common and are viewed as unfavorable. They are commonly used as a measure of quality of care and, in the United States and England, are associated with financial penalties. Readmissions are not the only possible return-to-acute-care metric; patients may also attend emergency departments (EDs).

OBJECTIVE

To assess hospital-level return-to-acute-care metrics using statistical criteria.

RESEARCH DESIGN

Patient readmissions and/or ED attendances were aggregated to produce risk-standardized hospital rates. Return-to-acute-care rates at 7, 30, 90, and 365 days were assessed using key statistical properties: (i) variability between hospitals; (ii) the relative contribution of patient and nonpatient factors to variation; and (iii) the statistical power to detect performance differences.

SUBJECTS

We had pseudonymized administrative data on all inpatient hospital admissions and ED attendances in National Health Service hospitals in England between April 2009 and March 2011. Patients with an inpatient stay for chronic obstructive pulmonary disorder or heart failure were eligible for inclusion.

MEASURES

ED attendances and readmissions for patients discharged from an inpatient stay for chronic obstructive pulmonary disorder or heart failure.

RESULTS

Interhospital variation was greatest for ED attendance; in addition, readmission was more strongly determined by patient characteristics than was ED attendance or both combined. Because of smaller numbers, the statistical power to detect differences in rates at 7 days for any indicator was limited.

CONCLUSIONS

Despite the current emphasis on readmissions, we found that ED attendance within 30 days has more desirable statistical properties and therefore the potential to be a useful metric when comparing hospitals.

Will Hospital Peer Grouping by Patient Socioeconomic Status Fix the Medicare Hospital Readmission Reduction Program or Create New Problems?

BACKGROUND

In 2016 the U.S. Congress directed the Centers for Medicare & Medicaid Services (CMS) to implement the 21st Century Cures Act to fix a flaw in the Hospital Readmissions Reduction Program (HRRP). One section of the Act is intended to remove bias in calculating penalties for hospitals treating large percentages of low socioeconomic status (SES) patients. A study was conducted to analyze the effect of the introduction of SES hospital peer groups on the number and distribution of the hospitals being penalized.

METHODS

The CMS analysis files for the fiscal year 2017 HRRP final rule and Disproportionate Share Hospital adjustments were used to assign hospital peer groups. The median excess readmission ratios for hospital peer groups were calculated, and the resulting pattern of hospital penalties within peer groups was analyzed.

RESULTS

The findings suggest that because CMS assigns individual HRRP penalties on six clinical conditions but proposes to assign hospitals to a single SES peer group based on all admissions, it will ignore substantial differences in the distribution of peer group medians across these conditions. For surgical cases, as expected, hospitals with fewer patients had higher readmission rates, while for medical cases, hospitals with fewer patients had fewer readmissions. These findings may result in distortion of the peer group adjustment intended to correct for SES.

CONCLUSION

Hospital peer groups may create unintended redistributions of penalties through distortion of peer group medians. An observed relationship between lower-volume hospitals and fewer readmissions for medical conditions requires additional research to establish its basis.

Factors Associated With 30-Day Readmission After Primary Total Hip Arthroplasty: Analysis of 514 455 Procedures in the UK National Health Service

Importance

Thirty-day readmission to hospital after total hip arthroplasty (THA) has significant direct costs and is used as a marker of hospital performance. All-cause readmission is the only metric in current use, and risk factors for surgical readmission and those resulting in return to theater (RTT) are poorly understood.

Objective

To determine whether patient-related predictors of all-cause, surgical, and RTT readmission after THA differ and which predictors are most significant.

Design, Setting, and Participants

Analysis of all primary THAs recorded in the National Health Service (NHS) Hospital Episode Statistics database from 2006 to 2015. The effect of patient-related factors on 30-day readmission risk was evaluated by multilevel logistic regression analysis. The analysis comprised all acute NHS hospitals in England and all patients receiving primary THA.

Main Outcomes and Measures

Thirty-day readmission rate for all-cause, surgical (defined using International Statistical Classification of Diseases and Related Health Problems, Tenth Revision primary admission diagnoses), and readmissions resulting in RTT.

Results

Across all hospitals, 514 455 procedures were recorded. Seventy-nine percent of patients were older than 60 years, 40.3% were men, and 59.7% were women. There were 30 489 all-cause readmissions (5.9%), 16 499 surgical readmissions (3.2%), and 4286 RTT readmissions (0.8%); 54.1% of readmissions were for surgical causes. Comorbidities with the highest odds ratios (ORs) of RTT included those likely to affect patient behavior: drug abuse (OR, 2.22; 95% CI, 1.34-3.67; P = .002), psychoses (OR, 1.83; 95% CI, 1.16-2.87; P = .009), dementia (OR, 1.57; 95% CI, 1.11-2.22; P = .01), and depression (OR, 1.52; 95% CI, 1.31-1.76; P < .001). Obesity had a strong independent association with RTT (OR, 1.46; 95% CI, 4.45-6.43; P < .001), with one of the highest population attributable fractions of the comorbidities (3.4%). Return to theater in the index episode was associated with a significantly increased risk of RTT readmission (OR, 5.35; 95% CI, 4.45-6.43; P < .001). Emergency readmission to the hospital in the preceding 12 months increased the risk of readmission significantly, with the association being most pronounced for all-cause readmission (for >2 emergency readmissions, OR, 2.33; 95% CI, 2.11-2.57; P < .001). Hip resurfacing was associated with a lower risk of RTT when compared with cemented implants (OR, 0.69; 95% CI, 0.54-0.88; P = .002) but for other types of readmission, implant type had no significant association with readmission risk. Increasing age and length of stay were strongly associated with all-cause readmission.

Conclusions and Relevance

Many patient-related risk factors for surgical and RTT readmission differ from those for all-cause readmission despite the latter being the only measure in widespread use. Clinicians and policy makers should consider these alternative readmission metrics in strategies for risk reduction and cost savings.

National estimates of 30-day readmissions among children hospitalized for asthma in the United States

OBJECTIVE

Previous single-center studies have reported that up to 40% of children hospitalized for asthma will be readmitted. The study objectives are to investigate the prevalence and timing of 30-day readmissions in children hospitalized with asthma, and to identify factors associated with 30-day readmissions.

METHODS

Data (n = 12,842) for children aged 6-18 years hospitalized for asthma were obtained from the 2013 Nationwide Readmission Database (NRD). The primary study outcome was time to readmission within 30 days after discharge attributable to any cause. Several predictors associated with the risk of admission were included: patient (age, sex, median household income, insurance type, county location, and pediatric chronic complex condition), admission (type, day, emergency services utilization, length of stay (LOS), and discharge disposition), and hospital (ownership, bed size, and teaching status). Cox's proportional hazards model was used to identify predictors.

RESULTS

Of 12,842 asthma-related index hospitalizations, 2.5% were readmitted within 30-days post-discharge. Time to event models identified significantly higher risk of readmission among asthmatic children aged 12-18 years, those who resided in micropolitan counties, those with >4-days LOS during index hospitalization, those who were hospitalized in an urban hospital, who had unfavorable discharge (hazard ratio 2.53, 95% confidence interval 1.33-4.79), and those who were diagnosed with a pediatric complex chronic condition, respectively, than children in respective referent categories.

CONCLUSION

A multi-dimensional approach including effective asthma discharge action plans and follow-up processes, home-based asthma education, and neighborhood/community-level efforts to address disparities should be integrated into the routine clinical care of asthma children.

Impact of the Hospital Readmission Reduction Program on Surgical Readmissions Among Medicare Beneficiaries

OBJECTIVE

To understand the impact of the Hospital Readmission Reduction Program on both future targeted and nontargeted surgical procedures.

BACKGROUND

The Hospital Readmission Reduction Program, established under the Affordable Care Act in March of 2010, placed financial penalties on hospitals with higher than expected rates of readmission beginning in 2012 for targeted medical conditions. Multiple studies have suggested a "spill-over" effect into other conditions, but the extent of that effect for specific surgical procedures is unknown.

METHODS

A retrospective review 5,122,240 Medicare beneficiaries who underwent future targeted procedures (total hip replacement, total knee replacements) or nontargeted procedures (colectomy, lung resection, abdominal aortic aneurysm repair, coronary artery bypass graft, aortic valve replacement, mitral valve repair) using an interrupted time series model to assess the rates of readmission before the Hospital Readmission Reduction Program was announced (2008-2010), whereas the program was being implemented (2010-2012) and after penalties were initiated (2012-2014). We also explored if the change in readmission rates were correlated with changes in index length of stay, use of observation status, or discharge to a skilled nursing facility.

RESULTS

From 2008 to 2014 rates of readmission declined for both target conditions (6.8%-4.8%; slope change -0.07 to -0.10, P < 0.001) and nontarget conditions (17.1%-13.4%; slope change -0.04 to -0.11, P < 0.001). The rate of reduction was most prominent after announcement of the program between 2010 and 2012 for both targeted and nontargeted conditions. During the same time period, mean hospital length of stay decreased; nontargeted conditions (10.4-8.4 days) and targeted conditions (3.6-2.8 days). There was no correlation between hospital reduction in readmissions and use of observation-only admissions (Pearson correlation coefficient = 0.01) or discharge to a skilled nursing facility (Pearson correlation coefficient = 0.05).

CONCLUSIONS

Trends in readmissions after inpatient surgery are consistent with hospitals responding to financial incentives announced in the Hospital Readmission Reduction Program. There appears to be both an anticipatory effect (future targeted procedures reducing readmission before payments implemented) and a spillover effect (nontargeted procedures also reducing readmissions).

Decreasing Hospital Readmission in Ileostomy Patients: Results of Novel Pilot Program

BACKGROUND

Nearly 30% of patients with newly formed ileostomies require hospital readmission from severe dehydration or associated complications. This contributes to significant morbidity and rising healthcare costs associated with this procedure. Our aim was to design and pilot a novel program to decrease readmissions in this patient population.

STUDY DESIGN

An agreement was established with Visiting Nurse Health System (VNHS) in March 2015 that incorporated regular home visits with clinical triggers to institute surgeon-supervised corrective measures aimed at preventing patient decompensation associated with hospital readmissions. Thirty-day readmission data for patients managed with and without VNHS support for 10.5 months before and after implementation of this new program were collected.

RESULTS

Of 833 patients with small bowel procedures, 162 were ileostomies with 47 in the VNHS and 115 in the non-VNHS group. Before program implementation, VNHS (n = 24) and non-VNHS patients (n = 54) had similar readmission rates (20.8% vs 16.7%). After implementation, VNHS patients (n = 23) had a 58% reduction in hospital readmission (8.7%) and non-VNHS patient hospital readmissions (n = 61) increased slightly (24.5%). Total cost of readmissions per patient in the cohort decreased by >80% in the pilot VNHS group.

CONCLUSIONS

Implementation of a novel program reduced the 30-day readmission rate by 58% and cost of readmissions per patient by >80% in a high risk for readmission patient population with newly created ileostomies. Future efforts will expand this program to a greater number of patients, both institutionally and systemically, to reduce the readmission-rate and healthcare costs for this high-risk patient population.