Variation In Readmission Rates Research Articles

Abstract 283: Readmission Rates and Etiologies among Patients with Primary Angiitis of the Central Nervous System

Introduction Primary Angiitis of the Central Nervous System (PACNS) is a rare, relapsing inflammatory disorder of blood vessels in the brain and spinal cord presenting clinically with a spectrum of manifestations including strokes, seizures, and neurologic deficits. Limited data are available on the modifiable and nonmodifiable predictors of readmissions for patients diagnosed with PACNS, which hinders the ability of healthcare systems to risk‐stratify patients based on their specific demographics, comorbidities, and hospital status. Methods We identified patients with PACNS (ICD‐10 code: I67.7) admitted from 2016‐2019 using the Nationwide Readmission Database (NRD). The difference in days between the index admission and the readmission was used to identify patients who were readmitted within 30 and 90 days. The primary diagnosis of the readmission was used to identify the etiology of readmission. Data was weighted to allow for representative nationwide estimates. Results We identified 1,398 patients admitted with a primary diagnosis of PACNS between 2016 and 2019. The mean age was 52.81 years, with 59.20% (n=828) being female. The 30‐day readmission rate was 10.52% (n=147) and the 90‐day readmission rate was 19.60% (n=274). Nearly half of 90‐day readmissions occurred within the first 30 days. Annual variations in readmission rates were insignificant. Age and sex distribution were not statistically different between patients not requiring readmission, those readmitted within 30 days, and those readmitted within 90 days. Compared to those not requiring readmission, patients readmitted within 30 days had a higher prevalence of diabetes (38.07% vs 29.06%; p=0.023) and hypertension (65.66% vs 56.88%; p=0.035), while those readmitted within 90 days had a higher prevalence of diabetes (41.56% vs 27.19%, p<0.001), dyslipidemia (48.49% vs 39.77%, p=0.009), obstructive sleep apnea (10.79% vs 6.73%, p=0.028), and ischemic stroke (32.56% vs 25.18%, p=0.013). The most common etiologies of 30‐ and 90‐day readmissions were cardiovascular (47.05% and 47.81%), neurological (26.80% and 20.61%), and infectious (9.07% and 7.58%). Conclusion The rates of 30‐ and 90‐day readmissions among patients with PACNS have remained stable between 2016 and 2019. Patients with PACNS requiring readmission had a higher incidence of vascular risk factors. Cardiovascular, neurological, and infectious conditions were the most common etiologies for 30‐ and 90‐day readmissions. While our analysis suggests a role for vascular risk factor modification to prevent readmissions in patients diagnosed with PACNS, further research is required to validate this hypothesis and identify other modifiable risk factors.

Variation in Condition-Specific Readmission Rates Across US Children's Hospitals.

Despite extensive efforts, overall readmission rates at US children's hospitals have not materially declined over the past decade, raising questions about how to direct future efforts. Using measures of prevalence and performance variation we describe readmission rates by condition and identify priority conditions for future intervention. Retrospective cohort study of 49 US children's hospitals in the Pediatric Health Information System in 2017. Conditions were classified using All Patients Refined Diagnosis Related Groups. 30-day unadjusted and risk-adjusted readmission rates were calculated for each hospital/condition using the Pediatric All Cause Readmission measure. We ranked the highest volume conditions by rate variation (RV, interquartile range divided by the median) for each condition across hospitals. The sample included 811,434 index hospitalizations with 50,196 (6.2%) 30-day readmissions. The RV across hospitals/conditions was between 0 and 2.8 (median=0.7). Common reasons for admission had low RVs across hospitals, for example, bronchiolitis (readmission rate=5.6%, RV=0.4), seizure (readmission rate=6.6%, RV=0.3), and asthma (readmission rate=3.1%, RV=0.4). We identified 33 conditions with high variation in readmission rates across hospitals, which accounted for 18% of all discharges and 11% of all pediatric readmissions. These conditions may serve as candidates for future readmission reduction activities. Many common childhood conditions have little variation in readmission rates across children's hospitals, suggesting limited future improvement opportunities. Conditions with high rate variation may provide opportunities for quality improvement; however, these conditions account for a relatively small share of total discharges suggesting modest potential impacts on national rates.

Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina.

Readmissions constitute a major health care burden among peripheral artery disease (PAD) patients. This study aimed to 1) estimate the zip code tabulation area (ZCTA)-level prevalence of readmission among PAD patients and characterize the effect of covariates on readmissions; and (2) identify hotspots of PAD based on estimated prevalence of readmission. Thirty-day readmissions among PAD patients were identified from the South Carolina Revenue and Fiscal Affairs Office All Payers Database (2010–2018). Bayesian spatial hierarchical modeling was conducted to identify areas of high risk, while controlling for confounders. We mapped the estimated readmission rates and identified hotspots using local Getis Ord (G*) statistics. Of the 232,731 individuals admitted to a hospital or outpatient surgery facility with PAD diagnosis, 30,366 (13.1%) experienced an unplanned readmission to a hospital within 30 days. Fitted readmission rates ranged from 35.3 per 1000 patients to 370.7 per 1000 patients and the risk of having a readmission was significantly associated with the percentage of patients who are 65 and older (0.992, 95%CI: 0.985–0.999), have Medicare insurance (1.013, 1.005–1.020), and have hypertension (1.014, 1.005–1.023). Geographic analysis found significant variation in readmission rates across the state and identified priority areas for targeted interventions to reduce readmissions.

Abstract 49: Building a Bridge From Hospital to Home Reduces Readmissions After Ischemic Stroke

Background: Readmission within 30 days after hospitalization due to ischemic stroke (IS) is associated with increased healthcare costs and higher risk of death and disability. Efforts to decrease hospital readmissions following IS may improve quality and cost of care. Methods: A multi-component improvement intervention to bridge the transition from hospital to home through increased certified home health agency (CHHA) referrals, post-discharge telephone calls, and enhanced communication with outpatient providers was developed. The project was implemented in July, 2019, and evaluated for six months following initiation. The primary outcome measure was same-hospital, 30-day readmission rate in the project-specific population of IS patients discharged to home from a stroke unit, and the overall IS population during the six-month baseline period compared to the six-month intervention period. Statistical significance was determined using chi square analysis ( p<.05 ) and clinical significance with Cohen’s alpha ( d >1.0). Results: Following implementation, a decrease in month to month variation in readmission rates (Figure 1) and a statistically and clinically significant decrease in 30-day, all-cause, same-hospital readmission rates from 7.389% to 2.791% (62.23% decrease, p = .010, d = 1.43) in the project population and from 6.557% to 2.995% (54.32% decrease, p = .031, d = 1.61) in the overall IS population, was seen. Improvement was seen in all process measures, with a statistically significant increase in CHHA referrals from 44% to 56% (28% increase, p =.04). Conclusions: Implementation of an evidence-based, bundled improvement intervention, which contributed to a significant increase in CHHA referrals for IS patients discharged to home, was associated with a meaningful decrease in readmission rates.

Hospital variation of 30-day readmission rate following transcatheter aortic valve implantation

ObjectivesThirty-day readmission rate is one of the hospital quality metrics. Outcomes of transcatheter aortic valve implantation (TAVI) have improved significantly, but it remains unclear whether hospital-level variance in 30-day readmission...

Variations in Cost and Readmissions of Patients in the Bundled Payment for Care Improvement Bundle for Hip and Femur Fractures.

IntroductionThe Bundled Payment for Care Improvement (BPCI) for hip and femur fractures is an effort to increase care quality and coordination at a lower cost. The bundle includes all patients undergoing an operative fixation of a hip or femur fracture (diagnosis-related group codes 480-482). This study aims to investigate variance in the hospital cost and readmission rates for patients within the bundle.Materials and MethodsThe study is a retrospective analysis of patients ≥65 years old billed for a diagnosis-related groups 480-482 in 2016 in the National Readmission Database. Cost of admission and length of stay were compared between patients who were or were not readmitted. Regression analysis was used to determine the effects of the primary procedure code and anatomical location of the femur fracture on costs, length of stay, and readmission rates.ResultsPatients that were readmitted within 90 days of surgery had an increased cost on initial admission ($18,427 vs $16,844, P < .0001), and an increased length of stay (6.24 vs 5.42, P < .0001). When stratified by procedure, patients varied in readmission rates (20.7% vs 19.6% vs 21.8%), initial cost, and length of stay (LOS). Stratification by anatomical location also led to variation in readmission rates (20.7% vs 18.3% vs 20.6%), initial cost, and LOS.ConclusionThe hip and femur fractures bundle includes a great number of procedures with variance in cost, readmission, and length of stay. This amount of variation may make standardization difficult and may put the hospital at potential financial risk.

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

BackgroundPatients 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. MethodsWe 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. ResultsWe 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.

Care coordination for veterans with COPD: a positive deviance study.

Improving chronic obstructive pulmonary disease (COPD) care and reducing hospital readmissions is an urgent healthcare system priority. However, little is known about the organizational factors that underlie intersite variation in readmission rates. Evidence from other chronic diseases points to care coordination as one such factor. To understand whether intersite differences in care coordination may be one of the organizational factors contributing to the variation in readmission rates, we examined provider perspectives on COPD care at Veterans Affairs (VA) sites. In this mixed-methods positive deviance study, we selected 3 VA sites in the lowest quartile and 3 in the highest quartile for 2016 risk-adjusted COPD readmission rates. During June to October 2017, we conducted semistructured interviews with primary and specialty care providers involved in COPD care at VA sites with low (n = 14) and high (n = 11) readmission rates. Although providers at all sites referenced ongoing readmission reduction initiatives, only providers at low-readmission sites described practice environments characterized by high relational coordination (ie, high-quality work relationships and high-quality communication). They also reported fewer significant structural barriers to collaboration in areas like patient volume. The most notable differences between high- and low-readmission sites were related to the quality of relational coordination and the presence of structural barriers to coordination, rather than specific readmission reduction initiatives. Implementing organizational reforms aimed at enhancing relational coordination and removing structural barriers would enhance care for COPD and may improve quality of care for other chronic conditions.

Variation Among Primary Care Physicians in 30-Day Readmissions.

Whether readmission rates vary by primary care physician (PCP) is unknown, although federal policy holds PCPs accountable for reducing readmissions. To determine whether 30-day readmission rates vary by PCP. Retrospective cohort study using marginal models and multilevel logistic regression with 100% of data on Texas Medicare claims from 2008 to 2015. Texas. Patients discharged alive between 1 January 2008 and 30 November 2015 who had a PCP in the prior year and whose PCP had at least 50 admissions in the study period. Readmission within 30 days of discharge. Follow-up visits with a PCP within 7 days of discharge were also measured. Between 2012 and 2015, the mean risk-standardized rate of 30-day readmissions was 12.9%. Of 4230 PCPs, 1 had a readmission rate that was significantly higher than the mean and none had a significantly lower rate. The 10th and 90th percentiles of PCP readmission rates were 12.4% and 13.4%, respectively, each only 0.5 percentage point different from the mean. The 99th percentile of PCP readmission rates was 14.0%, 1.1 percentage points higher than the mean. Detecting a 1.1-percentage point difference from the mean adjusted readmission rate would require more than 3500 admissions per PCP per year. Only fee-for-service Medicare patients in a single state were included. The authors could not account for confounders not included in Medicare databases or classify readmissions as avoidable. Variation in readmission rates among PCPs is very low. Programs holding PCPs accountable for readmissions may prove ineffective. National Institutes of Health.

Achievable Benchmarks of Care for Pediatric Readmissions.

Most inpatient care for children occurs outside tertiary children's hospitals, yet these facilities often dictate quality metrics. Our objective was to calculate the mean readmission rates and the Achievable Benchmarks of Care (ABCs) for pediatric diagnoses by different hospital types: metropolitan teaching, metropolitan nonteaching, and nonmetropolitan hospitals. We used a cross-sectional retrospective study of 30-day, all-cause, same-hospital readmission of patients less than 18 years old using the 2014 Healthcare Utilization Project National Readmission Database. For each hospital type, we calculated the mean readmission rates and corresponding ABCs for the 17 most common readmission diagnoses. We define outlier as any hospital whose readmission rate fell outside the 95% CI for an ABC within their hospital type. We analyzed 690,949 discharges at 525 metropolitan teaching hospitals (550,039 discharges), 552 metropolitan nonteaching hospitals (97,207 discharges), and 587 nonmetropolitan hospitals (43,703 discharges). Variation in readmission rates existed among hospital types; however, sickle cell disease (SCD) had the highest readmission rate and ABC across all hospital types: metropolitan teaching hospitals 15.7% (ABC 7.0%), metropolitan nonteaching 14.7% (ABC 2.6%), and nonmetropolitan 12.8% (ABC not calculated). For diagnoses in which ABCs were available, outliers were prominent in bipolar disorders, major depressive disorders, and SCD. ABCs based on hospital type may serve as a better metric to explain case-mix variation among different hospital types in pediatric inpatient care. The mean rates and ABCs for SCD and mental health disorders were much higher and with more outlier hospitals, which indicate high-value targets for quality improvement.

The Validity of All-Cause 30-Day Readmission Rate as a Hospital Performance Metric After Primary Total Hip and Knee Arthroplasty: A Systematic Review

BackgroundRisk-adjusted all-cause 30-day readmission rate (ACRR) following total hip arthroplasty (THA) and total knee arthroplasty (TKA) is currently used as a metric of hospital performance as part of the Hospital Readmissions Reduction Program. However, the extent to which it is determined by hospital-related factors and is therefore a fair method of determining reimbursement remains unclear. MethodsOur aim was to systematically review the available literature pertaining to whether ACRR is a valid metric of hospital performance after elective primary THA or TKA as determined by (1) its association with other performance metrics, (2) the extent to which variation in ACRR can be explained by between-hospital variation, and (3) the relative importance of hospital-related versus surgeon- or patient-related factors in determining ACRR. The MEDLINE, EMBASE, and Health Management Information Consortium databases were searched from inception to November 2018 and reference lists of selected articles scanned. The final list of articles was determined by consensus. ResultsEight articles were included. Correlation of ACRR with established composite metrics of both outcome and process measures was poor. There was a weak positive correlation between ACRR and mortality. Only 1.5% of the variation in readmission rates for THA and TKA was found to be attributable to hospital-level factors, with patient-related factors such as age and comorbidities having much greater influence. Use of composite outcome metrics, for example, combining readmission and mortality, or considering the “surgical” readmission rate, improved the sensitivity to detect important between-hospital variation. ConclusionThere is insufficient evidence in the current literature to justify the use of ACRR following elective THA and TKA for financially penalizing hospitals. Further work is needed to define what is acceptable variation. The use of a composite metric or surgical readmission rate may improve the ability to detect between-hospital variation.

Readmission in the First Year Following Pediatric Heart Transplantation: Incidence, Indications, and Risk Factors

Purpose Readmission is common following pediatric heart transplantation (HT), based on existing single center data. The nationwide incidence of rehospitalization after HT has not been reported. We aimed to assess the incidence, risk factors, and indications for rehospitalization in pediatric HT recipients using a nationwide multicenter linked database. Methods The Scientific Registry of Transplant Recipients and Pediatric Health Information System databases were linked at the patient level using indirect identifiers (2002-2016). All pediatric HT recipients who survived to discharge were included. The frequency, timing of, and indication for all-cause readmissions in the first year following initial discharge were assessed. Multivariable logistic regression examined risk factors for readmission with standard error estimates adjusted for clustering within centers. Results A total of 2870 HT recipients from 29 centers were included. Of these, 1835 (63.9%) were readmitted within the first post-HT year (total of 5428 admissions). Readmission rates varied across transplant centers from 46% (1 center) to 100% (3 centers). For patients who were rehospitalized, the median number of readmissions was 2 (IQR 1-4) and the median time to first readmission was 29 days (IQR 9-99 days). Risk factors independently associated with readmission included age 30 days (AOR 1.4, 95%CI 1.1-1.9). Cardiac diagnoses, including rejection and/or concern for rejection, were the most common indications for readmission (N=2213, 40.8%), followed by infection/fever (N=1222, 22.5%), and respiratory diagnoses (N=393, 7.2%). Conclusion Rehospitalization is common in the first year following pediatric HT, with considerable variation in readmission rates across centers. Younger age, congenital heart disease, and a more complicated post-HT course increase the risk for subsequent readmission. Risk stratification based on these factors may serve to guide decision-making in determining discharge readiness and planning.

To what degree can variations in readmission rates be explained on the level of the hospital? a multilevel study using a large Dutch database.

BackgroundIt is not clear which part of the variation in hospital readmissions can be attributed to the standard of care hospitals provide. This is in spite of their widespread use as an indicator of a lower quality of care. The aim of this study is to assess the variation in readmissions on the hospital level after adjusting for case-mix factors.MethodsWe performed multilevel logistic regression analyses with a random intercept for the factor ‘hospital’ to estimate the variance on the hospital level after adjustment for case-mix variables. We used administrative data from 53 Dutch hospitals from 2010 to 2012 (58% of all Dutch hospitals; 2,577,053 admissions). We calculated models for the top ten diagnosis groups with the highest number of readmissions after an index admission for a surgical procedure. We calculated intraclass correlation coefficients (ICC) per diagnosis group in order to explore the variation in readmissions between hospitals. Furthermore, we determined C-statistics for the models with and without a random effect on the hospital level to determine the discriminative ability.ResultsThe ICCs on the hospital level ranged from 0.48 to 2.70% per diagnosis group. The C-statistics of the models with a random effect on the hospital level ranged from 0.58 to 0.65 for the different diagnosis groups. The C-statistics of the models that included the hospital level were higher compared to the models without this level.ConclusionsFor some diagnosis groups, a small part of the explained variation in readmissions was found on the hospital level, after adjusting for case-mix variables. However, the C-statistics of the prediction models are moderate, so the discriminative ability is limited. Readmission indicators might be useful for identifying areas for improving quality within hospitals on the level of diagnosis or specialty.

Predicting Discharge Location among Low-Energy Hip Fracture Patients Using the Score for Trauma Triage in the Geriatric and Middle-Aged (STTGMA).

Patterns of discharge location may be evident based on the “sickness” profile of the patient. This study sought to evaluate the ability of the STTGMA tool, a validated mortality risk index for middle-aged and geriatric trauma patients, to predict discharge location in a cohort of low-energy elderly hip fracture patients, with successful discharge planning measured by readmission rates. Low-energy hip fracture patients aged 55 years and older were prospectively followed throughout their hospitalization. On initial evaluation in the Emergency Department, each patient's age, comorbidities, injury severity, and functional status were utilized to calculate a STTGMA score. Discharge location was recorded with the primary outcome measure of an unsuccessful discharge being readmission within 30 days. Patients were risk stratified into minimal-, low-, moderate-, and high-risk STTGMA cohorts. A p-value of <0.05 was considered significant for all statistical tests. 408 low-energy hip fractures were enrolled in the study with a mean age of 81.3±10.6 years. There were 214 (52.5%) intertrochanteric fractures, 167 (40.9%) femoral neck fractures, and 27 (6.6%) subtrochanteric femur fractures. There was no difference in readmission rates within STTGMA risk cohorts with respect to discharge location; however, among individual discharge locations there was significant variation in readmission rates when patients were risk stratified. Overall, STTGMA risk cohorts appeared to adequately risk-stratify readmission with 3.5% of minimal-risk patients experiencing readmission compared to 24.5% of moderate-risk patients. Specific cohorts deemed high-risk for readmission were adequately identified. The STTGMA tool allows for prediction of unfavorable discharge location in hip fracture patients. Based on observations made via the STTGMA tool, improvements in discharge planning can be undertaken to increase home discharge and to more closely track “high-risk” discharges to help prevent readmissions.

Variation in readmission rates among hospitals following admission for traumatic injury

IntroductionReadmission following hospital discharge is both common and costly. The Hospital Readmission Reduction Program (HRRP) financially penalizes hospitals for readmission following admission for some conditions, but this approach may not be appropriate for all conditions. We wished to determine if hospitals differed in their adjusted readmission rates following an index hospital admission for traumatic injury. Patients and MethodsWe extracted from the AHRQ National Readmission Dataset (NRD) all non-elderly adult patients hospitalized following traumatic injury in 2014. We estimated hierarchal logistic regression models to predicted readmission within 30 days. Models included either patient level predictors, hospital level predictors, or both. We quantified the extent of hospital variability in readmissions using the median odds ratio. Additionally, we computed hospital specific risk-adjusted rates of readmission and number of excess readmissions. ResultsOf the 177,322 patients admitted for traumatic injury 11,940 (6.7%) were readmitted within 30 days. Unadjusted hospital readmission rates for the 637 hospitals in our study varied from 0% to 20%. After controlling for sources of variability the range for hospital readmission rates was between 5.5% and 8.5%. Only 2% of hospitals had a random intercept coefficient significantly different from zero, suggesting that their readmission rates differed from the mean level of all hospitals. We also estimated that in 2014 only 11% of hospitals had more than 2 excess readmissions. Our multilevel model discriminated patients who were readmitted from those not readmitted at an acceptable level (C = 0.74). ConclusionsWe found little evidence that hospitals differ in their readmission rates following an index admission for traumatic injury. There is little justification for penalizing hospitals based on readmissions after traumatic injury.

Patient- and Hospital-Level Factors Associated With Readmission for Malignant Pleural Effusion.

Readmission after hospitalization for malignant pleural effusion (MPE) may represent gaps in the quality of health care delivery. The goal of this study was to determine the frequency of 30-day readmission for MPE and identify clinical factors associated with rehospitalization. A retrospective cohort of adults hospitalized for MPE from 2009 to 2011 was analyzed using an administrative database. The primary outcome was all-cause 30-day readmission rate. Hierarchic mixed-effects logistic regression models were used to examine associations between patient- and hospital-level factors and 30-day readmission and assess variation in readmission rates across hospitals. The 7-, 14-, 30-, 60-, and 90-day readmission rates for MPE were 16.1%, 25.9%, 38.3%, 52.5%, and 63.8%, respectively. The most common primary diagnoses for 30-day readmission were MPE (69.5%) and other clinical issues related to malignancy (21.1%). Clinical factors associated with 30-day readmission were female sex (odds ratio [OR], 0.78; 95% CI, 0.63 to 0.95), greater number of medical comorbidities (OR, 1.51; 95% CI, 1.15 to 1.99), and having a do-not-resuscitate order (OR, 1.37; 95% CI, 1.03 to 1.84). Hospitals in the 90th percentile were only 1.1 times more likely to have a 30-day readmission for MPE than those in the lowest 10th percentile (40.9% v 37%). Readmission for MPE is common and frequently results from progression of malignancy. Readmission rates were similar across all hospitals, suggesting they are unlikely to be mutable using conventional approaches to reduce rehospitalizations. Instead, interventions may need to focus on addressing care planning at the end of life.

Variations in 30-day readmissions and length of stay among spine surgeons: a national study of elective spine surgery among US Medicare beneficiaries.

OBJECTIVE Pay-for-performance programs are targeting hospital readmissions. These programs have an underlying assumption that readmissions are due to provider practice patterns that can be modified by a reduction in reimbursement. However, there are limited data to support the role of providers in influencing readmissions. To study this, the authors examined variations in readmission rates by spine surgeon within 30 days among Medicare beneficiaries undergoing elective lumbar spine surgery for degenerative conditions. METHODS The authors applied validated ICD-9-CM algorithms to 2003-2007 Medicare data to select beneficiaries undergoing elective inpatient lumbar spine surgery for degenerative conditions. Mixed models, adjusting for patient demographics, comorbidities, and surgery type, were used to estimate risk of 30-day readmission by the surgeon. Length of stay (LOS) was also studied using these same models. RESULTS A total of 39,884 beneficiaries were operated on by 3987 spine surgeons. The mean readmission rate was 7.2%. The mean LOS was 3.1 days. After adjusting for patient characteristics and surgery type, 1 surgeon had readmission rates significantly below the mean, and only 5 surgeons had readmission rates significantly above the mean. In contrast, for LOS, the patients of 288 surgeons (7.2%) had LOS significantly lower than the mean, and the patients of 397 surgeons (10.0%) had LOS significantly above the mean. These findings were robust to adjustments for surgeon characteristics and clustering by hospital. Similarly, hospital characteristics were not significantly associated with readmission rates, but LOS was associated with hospital for-profit status and size. CONCLUSIONS The authors found almost no variations in readmission rates by surgeon. These findings suggest that surgeon practice patterns do not affect the risk of readmission. Likewise, no significant variation in readmission rates by hospital characteristics were found. Strategies to reduce readmissions would be better targeted at factors other than providers.

Readmission After Pediatric Mental Health Admissions

Reducing readmissions is a major health care system goal. There is a gap in our understanding of pediatric readmission patterns after mental health (MH) admissions. With this study, we aimed to characterize the prevalence of readmissions after MH admissions, to identify patient-level factors and costs associated with readmissions, and to assess variation in readmission rates across hospitals. Using the 2014 Healthcare Cost and Utilization Project all-payer Nationwide Readmissions Database, we conducted a retrospective cohort analysis of 253 309 admissions for 5- to 17-year-olds at acute-care hospitals in 22 states. We calculated 30-day unplanned readmission rates, lengths of stay, and costs by primary admission diagnosis. We used hierarchical regression models to assess differences in readmission rates by patient characteristics, primary diagnoses, and comorbid chronic conditions, and to estimate the variation in case mix-adjusted rates across hospitals. MH stays accounted for 18.7% (n = 47 397) of index admissions. The 30-day readmission rate for MH admissions was higher than for non-MH admissions (8.0% vs 6.2%; P < .001). Children who were ≤14 years old, had non-MH chronic conditions, and/or had public insurance were more likely to be readmitted than their peers (P < .001 for each). Adjusted rates varied across hospitals (P < .001) and were 97.9% greater for hospitals 1 SD above versus below (11.2% vs 5.6%) the mean. Adjusted readmission rates, lengths of stay, and costs differed by diagnosis (P < .001). The 30-day readmission rate was significantly higher after MH than non-MH admissions. Adjusted MH readmission rates varied substantially among hospitals, suggesting potential room for improvement.

Variation in Postsepsis Readmission Patterns: A Cohort Study of Veterans Affairs Beneficiaries.

Rehospitalization is common after sepsis, but little is known about the variation in readmission patterns across patient groups and care locations. To examine the variation in postsepsis readmission rates and diagnoses by patient age, nursing facility use, admission year, and hospital among U.S. Veterans Affairs (VA) beneficiaries. Observational cohort study of VA beneficiaries who survived a sepsis hospitalization (2009-2011) at 114 VA hospitals, stratified by age (<65 vs. ≥65 yr), nursing home usage (none, chronic, or acute), year of admission (2009, 2010, 2011), and hospital. In the primary analysis, sepsis hospitalizations were identified using a previously validated method. Sensitivity analyses were performed using alternative definitions with explicit International Classification of Diseases, Ninth Revision, Clinical Modification, codes for sepsis, and separately for severe sepsis and septic shock. The primary outcomes were rate of 90-day all-cause hospital readmission after sepsis hospitalization and proportion of readmissions resulting from specific diagnoses, including the proportion of "potentially preventable" readmissions. Readmission diagnoses were similar from 2009 to 2011, with little variation in readmission rates across hospitals. The top six readmission diagnoses (heart failure, pneumonia, sepsis, urinary tract infection, acute renal failure, and chronic obstructive pulmonary disease) accounted for 30% of all readmissions. Although about one in five readmissions had a principal diagnosis for infection, 58% of all readmissions received early systemic antibiotics. Infection accounted for a greater proportion of readmissions among patients discharged to nursing facilities compared with patients discharged to home (25.0-27.1% vs. 16.8%) and among older vs. younger patients (22.2% vs. 15.8%). Potentially preventable readmissions accounted for a quarter of readmissions overall and were more common among older patients and patients discharged to nursing facilities. Hospital readmission rates after sepsis were similar by site and admission year. Heart failure, pneumonia, sepsis, and urinary tract infection were common readmission diagnoses across all patient groups. Readmission for infection and potentially preventable diagnoses were more common in older patients and patients discharged to nursing facilities.

Variation in 30-Day Readmission Rates Among Medically Complex Patients at Inpatient Rehabilitation Facilities and Contributing Factors

ObjectiveFew studies have focused on 30-day readmission rates in inpatient rehabilitation facilities (IRFs) and factors that contribute to this. The purpose of this study was to examine the variation in 30-day readmission rates among medically complex patients at IRFs nationally and explore how patient and facility characteristics are associated with this variation. DesignRetrospective review of an administrative database. SettingIRFs throughout the United States. ParticipantsPatients from the medically complex impairment group treated at IRFs nationwide represented in the Uniform Data System for Medical Rehabilitation (UDSMR) from 2002 to 2011. MeasurementsUsing multilevel logistic regression analysis, 16 patient and facility characteristics were examined to identify and adjust for variables with a significant effect on readmission rates. Unadjusted and adjusted readmission rates were plotted by facility rank. Facilities were grouped by decile, based on readmission rates, and the mean unadjusted and adjusted rates for the lowest and highest deciles were compared to quantify the variation due to adjustment. Patient and facility characteristics for the highest and lowest deciles were compared. ResultsA total of 117,156 medically complex patients from 682 IRFs nationwide were included. Patients were 46% male and 84% white with an average age of 71.5 years. Nine of 16 characteristics were identified that significantly increased the odds of readmission, including older age, male gender, poorer admission motor function, longer duration of impairment, higher Elixhauser comorbidity index, unemployed or retired status, larger facility size, lower mean facility admission motor function, and eastern or western geographic area.The average unadjusted and adjusted readmission rates for all facilities were 15.63% and 15.86%, respectively. The unadjusted readmission rates for the lowest and highest deciles were 6.71% and 26.48%, respectively. After adjustment, this narrowed to 10.33% and 21.91%, respectively. Patient and facility characteristics accounted for 41% of the variation seen in the readmission rates for these groups.Facilities with the highest readmission rates (highest decile) more commonly cared for patients at highest risk for readmission: unemployed, male patients with higher comorbidity index and poorer motor function on admission. ConclusionThis study shows significant variation in readmission rates for medically complex patients across IRFs. However, nearly half (41.4%) of this variation was attributed to 9 patient and facility characteristics, suggesting the need for risk adjustment if readmission rates are to be used as a quality indicator for IRFs.