The Hospital Readmission Reduction Program for Surgical Conditions: Impactful or Harmful?

Impact of Hospital Readmissions Reduction Program Penalties on Hip and Knee Replacement Readmissions: Comparison of Hospitals at Risk of Varying Penalty Amounts

BACKGROUND

Little is known about the impact of the U.S. Centers for Medicare & Medicaid Services' Hospital Readmissions Reduction Program (HRRP) expansion to include readmissions following elective primary total hip and knee replacements; the expansion was finalized in 2013 and was implemented in 2014. We examined whether hospitals at risk of relatively large penalties from this expansion experienced greater declines in joint replacement readmissions compared with hospitals at risk of smaller penalties.

METHODS

We used Medicare's 2009 to 2016 Hospital Compare data sets to examine the impact of the HRRP's expansion in the July 2013 to June 2016 period (post-expansion) compared with the July 2009 to June 2012 period (pre-expansion). The primary outcome was the hospital-level, 30-day, risk-standardized readmission rate (hereafter called the readmission rate) following joint replacement surgical procedures. We used the percentage of a hospital's total inpatient revenue attributed to Medicare (categorized into quartiles) to represent the risk of penalties. We used hierarchical linear regression models to examine the adjusted impact of the HRRP's expansion.

RESULTS

Our study cohort included 2,326 acute care hospitals. In the pre-HRRP expansion phase, the mean readmission rate was 5.36% among hospitals with the highest proportion of Medicare revenues (quartile 4) and 5.46% among hospitals with the lowest proportion of Medicare revenues (quartile 1). With the HRRP expansion, the readmission rate declined by 18.92% (1.01 percentage points) among quartile-4 hospitals and by 17.97% (0.98 percentage point) among quartile-1 hospitals (p = 0.45). This nonsignificant difference in readmission rate declines between quartiles persisted in multivariable analysis (a decline of 18.41% [0.98 percentage point] in quartile 4 and a decline of 17.35% [0.94 percentage point] in quartile 1; p = 0.35).

CONCLUSIONS

The HRRP's expansion to include joint replacements did not lead to greater reductions in postoperative readmissions among hospitals at risk of larger penalties in comparison with hospitals at risk of smaller penalties. Readmission rates were declining at similar rates among all hospitals, before and after the HRRP's expansion.

CLINICAL RELEVANCE

Readmissions and complications following joint replacements are measures of the quality of surgical care. These events have important clinical and economic implications for patients and providers. This study is clinically relevant because it examines whether policy interventions, such as the HRRP, have the potential to reduce these unintended consequences of surgical care.

Assessment of Between-Hospital Variation in Readmission and Mortality After Cancer Surgical Procedures

Importance

Although current federal quality improvement programs do not include cancer surgery, the Centers for Medicare & Medicaid Services and other payers are considering extending readmission reduction initiatives to include these and other common high-cost episodes.

Objectives

To quantify between-hospital variation in quality-related outcomes and identify hospital characteristics associated with high and low performance.

Design, Setting, and Participants

This retrospective cohort study obtained data through linkage of the California Cancer Registry to hospital discharge claims databases maintained by the California Office of Statewide Health Planning and Development. All 351 acute care hospitals in California at which 1 or more adults underwent curative intent surgery between January 1, 2007, and December 31, 2011, with analyses finalized July 15, 2018, were included. A total of 138 799 adults undergoing surgery for colorectal, breast, lung, prostate, bladder, thyroid, kidney, endometrial, pancreatic, liver, or esophageal cancer within 6 months of diagnosis, with an American Joint Committee on Cancer stage of I to III at diagnosis, were included.

Main Outcomes and Measures

Measures included adjusted odds ratios and variance components from hierarchical mixed-effects logistic regression analyses of in-hospital mortality, 90-day readmission, and 90-day mortality, as well as hospital-specific risk-adjusted rates and risk-adjusted standardized rate ratios for hospitals with a mean annual surgical volume of 10 or more.

Results

Across 138 799 patients at the 351 included hospitals, 8.9% were aged 18 to 44 years and 45.9% were aged 65 years or older, 57.4% were women, and 18.2% were nonwhite. Among these, 1240 patients (0.9%) died during the index admission. Among 137 559 patients discharged alive, 19 670 (14.3%) were readmitted and 1754 (1.3%) died within 90 days. After adjusting for patient case-mix differences, evidence of statistically significant variation in risk across hospitals was identified, as characterized by the variance of the random effects in the mixed model, for all 3 metrics (P < .001). In addition, substantial variation was observed in hospital performance profiles: across 260 hospitals with a mean annual surgical volume of 10 or more, 59 (22.7%) had lower-than-expected rates for all 3 metrics, 105 (40.4%) had higher-than-expected rates for 2 of the 3, and 19 (7.3%) had higher-than-expected rates for all 3 metrics.

Conclusions and Relevance

Accounting for patient case-mix differences, there appears to be substantial between-hospital variation in in-hospital mortality, 90-day readmission, and 90-day mortality after cancer surgical procedures. Recognizing the multifaceted nature of hospital performance through consideration of mortality and readmission simultaneously may help to prioritize strategies for improving surgical outcomes.

Defining Types and Determining Risk Factors for Vascular Surgery Readmissions

BACKGROUND

Vascular surgery patients typically have numerous comorbidities, which puts them at higher risk for postoperative readmissions. This study aims to investigate the risk factors for and appropriately categorize the various types of vascular surgery readmissions.

METHODS

Nine hundred seventy-two patients were retrospectively reviewed. Readmissions were classified into 3 separate groups: readmissions that occurred between 0 and 30 days (30-day readmissions), 31-90 days (3-month readmissions), and 91-365 days (1-year readmissions). Each readmission was then assigned to 1 of the 4 categories based on whether they were related to the index procedure and whether they were planned. Univariate tests were performed for demographic variables based on their type of readmission, and logistic regressions were then performed to identify predictors of each unplanned, related readmissions.

RESULTS

The overall 30-day readmission rate was 21.9% (n = 213). The unplanned, related readmission cohort (n = 83) had the highest readmission rate of 8.5%. The related, planned readmission rate was 5.9% (n = 58), while the unrelated, unplanned readmission rate was 5.6% (n = 55). In contrast, the overall 1-year readmission rate was 40.0% (n = 389), with the largest category being unplanned, unrelated readmissions at 19.7% (n = 191). The unplanned, related readmission rate was 8.7% (n = 85), whereas the planned, related readmission rate was 5.7% (n = 55). Compared with other types of readmissions, unplanned, related readmissions tended to affect patients who were younger, had poor glycemic control, and had higher body mass indexes (BMIs). Multivariate predictors of unplanned, related readmissions were poor glycemic control at 3 months (odds ratio [OR]: 2.16, P = 0.03), and BMI at 30 days (OR: 1.06, P = 0.04) and 1 year (OR: 1.05, P = 0.04).

CONCLUSIONS

Readmissions have varying risk factors depending on their category; targeting glycemic control and obesity may reduce unplanned, related readmissions.

Teenage Trauma Patients Are at Increased Risk for Readmission for Mental Diseases and Disorders

BACKGROUND

Most studies of readmission after trauma are limited to single institutions or single states. The purpose of this study was to determine the risk factors for readmission after trauma for mental illness including readmissions to different hospitals across the United States.

MATERIALS AND METHODS

The Nationwide Readmission Database for 2013 and 2014 was queried for all patients aged 13 to 64 y with a nonelective admission for trauma and a nonelective readmission within 30 d. Multivariable logistic regression was performed for readmission for mental diseases and disorders.

RESULTS

During the study period, 53,402 patients were readmitted within 30 d after trauma. The most common major diagnostic category on readmission was mental diseases and disorders (12.1%). The age group with the highest percentage of readmissions for mental diseases and disorders was 13 to 17 y (38%). On multivariable regression, the teenage group was also the most likely to be readmitted for mental diseases and disorders compared to 18-44 y (odds ratio [OR] 0.45, P < 0.01) and 45-64 y (OR 0.24, P < 0.01). Other high-risk comorbidities included HIV infection (OR 2.4, P < 0.01), psychosis (OR 2.2, P < 0.01), drug (OR 2.0, P < 0.01), and alcohol (OR 1.4, P < 0.01) abuse.

CONCLUSIONS

Teenage trauma patients are at increased risk for hospital readmission for mental illness. Efforts to reduce these admissions should be targeted toward individuals with high-risk comorbidities such as HIV infection, psychosis, and substance abuse.