Variation In Readmission Rates Research Articles

Hospital, Patient, and Clinical Factors Influence 30- and 90-Day Readmission After Primary Total Hip Arthroplasty

BackgroundThe purpose of this study was to analyze the hospital, clinical, and patient factors associated with inpatient readmission after total hip arthroplasty (THA) in the Medicare population and to understand the primary reasons for readmission. MethodsThe Medicare 100% national hospital claims database was used to identify 442,333 older patients (65+) with a primary THA in 3730 hospitals between 2010 and 2013. A multilevel logistic regression analysis with a clustered data structure was used to investigate the risk of all-cause 30- and 90-day readmission, incorporating hospital, clinical, and patient factors. ResultsAt 30 days, 5.8% (median) of the patients were readmitted, whereas at 90 days, 10.5% (median) were readmitted. Geographic census region, hospital procedure volume, and nonprofit ownership were the only significant hospital factors among those we studied. Overall, clinical factors explained more of the variation in readmission rates than general hospital factors. Use of a perioperative transfusion was associated with 14% greater risk, patients discharged to home had 28% lower risk, and surgeon volume and length of stay were also significant risk factors. The top 5 most frequently reported primary reasons for 30-day readmission in THA were procedure related: dislocation (5.9%), deep infection (5.1%), wound infection (4.8%), periprosthetic fracture (4.4%), or hematoma (3.4%). ConclusionThese findings support further optimization of the delivery of care—both intraoperative and postoperative—to reduce the broad variation in hospital readmissions.

National characteristics and predictors of neurologic 30-day readmissions.

To examine national 30-day readmission rates for patients hospitalized with neurologic disorders. Using the University HealthSystem Consortium (UHC) database, we identified 554,399 index neurologic admissions from October 2011 through January 2015. We collected information regarding age, race, insurance payer, Medicare severity diagnosis-related group, and severity of illness. We examined readmission by diagnosis and performed multivariable logistic regression to determine predictors of readmission. The unplanned readmission rate was 11.0%. Rates of unplanned readmission were highest for patients with peripheral nerve disorders (21.9%), CNS neoplasms (21.0%), nonhypertensive encephalopathy (15.5%), arterial stenosis (15.4%), and bacterial CNS infections (14.5%). In multivariable analysis, higher severity of illness and public health insurance coverage predicted higher rates of 30-day readmission. We found significant variation in readmission rates for different neurologic disorders. These data provide insight into management of neurologic disease nationally, offering policymakers realistic goals for standards of care and challenging health care providers to develop systems-based solutions that will improve transitions of care for those at highest risk of readmission with neurologic disease.

Abstract 19872: 30-day Readmission Rate and Center Variability for US Children Hospitalized With Heart Failure

Background: The 30-day readmission rate is a well-established performance benchmark for adult heart failure programs. Little is known about the 30-day readmission rate for pediatric heart failure programs or the variability among centers. We sought to determine the 30-day readmission rate for children admitted with acute decompensated heart failure (ADHF) to support benchmarking efforts in pediatrics. Methods: The Pediatric Health Information System (PHIS) database was queried to identify all children ≤21 years of age admitted between January 2009 and March 2014 with a primary or secondary diagnosis of heart failure. All-cause 30-day readmission rates were calculated, as was mean length of stay. Results: Over the 5-year study period, a total of 39,150 hospitalizations at 47 children’s hospitals met the inclusion criteria. Overall, 56% were infants, 25% age 1-12 years, 9% adolescents, and 9% adults, 48% were female, 72% had congenital heart disease, 28% had cardiomyopathy/myocarditis. The mean hospital length of stay was 25 days (range 11,41 days; SD 7 days). In-hospital mortality was 8.4%. Overall 30-day readmission rate was 20% (range 10%, 30%; SD 5%) and varied considerably by center (Figure). Conclusion: A substantial portion of children (one in five) admitted to the hospital for heart failure is re-admitted within 30 days. There is considerable center-based variation in re-admission rates. The mean hospital length of stay exceeds 3 week and also varies considerably by center.

Hospital Readmission Rates in U.S. States: Are Readmissions Higher Where More Patients with Multiple Chronic Conditions Cluster?

This study examines small area variations in readmission rates to assess whether higher readmission rate in an area is associated with higher clusters of patients with multiple chronic conditions. The study uses hospital discharge data of adult (18+) patients in 6 U.S. states for 2009 from the Healthcare Cost and Utilization Project of the Agency for Healthcare Research and Quality, linked to contextual and provider data from Health Resources and Services Administration. A multivariate cross sectional design at primary care service area (PCSA) level is used. Adjusting for area characteristics, the readmission rates were significantly higher in PCSAs having higher proportions of patients with 2-3 chronic conditions and those with 4+ chronic conditions, compared with areas with a higher concentration of patients with 0-1 chronic conditions. Using small area analysis, the study shows that areas with higher concentration of patients with increased comorbid conditions are more likely to have higher readmission rates.

Variation in readmission rates by emergency departments and emergency department providers caring for patients after discharge.

The role of the emergency department (ED) provider and ED facility in readmissions of recently discharged patients who visit the ED has not been studied. To determine the variation in readmission rates by ED facility and ED providers caring for patients after discharge. Retrospective cohort study using multilevel, multivariable models of 100% Texas Medicare claims data from the years 2007 to 2011. Texas acute-care hospitals and ED facilities. Medicare beneficiaries who visited an ED within 30 days of discharge from a hospital. None. Readmission after an ED visit within 30 days of discharge from an initial hospitalization defined as a hospitalization starting the day of or the day following the ED visit. The mean readmission rate following an ED visit was 52.67%. In 2-level models, 14.2% of ED providers readmitted significantly more patients (mean readmission rate of 67.2%) than the mean; 14.7% of ED providers readmitted significantly fewer patients (mean readmission rate of 36.8%) than the mean. After accounting for the ED facility in 3-level models, the variance for the ED providers decreased 65% from 0.2532 to 0.0893. The risk of readmission varies by ED provider caring for patients after discharge. A large part of this variation is explained by the ED facility in which the ED providers practice. Thus, ED provider practices patterns and ED facility systems of care may be a target for interventions to reduce readmissions.

Variation by center and economic burden of readmissions after liver transplantation.

The rate and causes of hospital readmissions after liver transplantation (LT) remain largely unknown in the United States. Adult patients (n = 11,937; 43.1% of all LT cases) undergoing LT from 2007 to 2011 were examined with a linkage of the University HealthSystem Consortium and Scientific Registry of Transplant Recipients databases to determine the incidence and risk factors for 30-day readmissions and utilization metrics 90 days after LT. The overall 30-day hospital readmission rate after LT was 37.9%, with half of patients admitted within 7 days after discharge. Readmitted patients had worse overall graft and patient survival with a 2-year follow-up. Multivariate analysis identified risk factors associated with 30-day hospital readmission, including a higher Model for End-Stage Liver Disease score, diabetes at LT, dialysis dependence, a high donor risk index allograft, and discharge to a rehabilitation facility. After adjustments for donor, recipient, and geographic factors in a hierarchical model, we found significant variation in readmission rates among hospitals ranging from 26.3% to 50.8% (odds ratio, 0.53-1.90). In the 90-day analysis after LT, readmissions accounted for $43,785 of added costs in comparison with patients who were not readmitted in the first 90 days. This is the first national report showing that more than one-third of LT recipients are readmitted to their center within 30 days and that readmissions are associated with center variation and increased resource utilization.

Factors associated with 30-day readmission after restorative proctocolectomy with IPAA: a national study.

Hospital readmission has been identified by many payers as a surrogate for surgical quality. The 30-day readmission rate and factors associated with hospital readmission after restorative proctocolectomy with IPAA have not been well studied. The purpose of this work was to identify the rate of and factors associated with hospital readmission within 30 days of restorative proctocolectomy with IPAA. A retrospective review of patients undergoing IPAA from 2009 to 2012 in the University HealthSystem Consortium database was performed. Hospitals were stratified into quartiles according to the number of cases performed annually. This study was conducted using a national database of university hospitals. A total of 4952 patients within the 4-year study period were included in the analysis. The primary outcome measured was readmission within 30 days of discharge. The 30-day readmission rate was 22.8% overall, although high-volume centers performed significantly better than low-volume centers (high vs low volume: 19.7% vs 28.2%; p < 0.001). When controlling for confounding variables, multivariate analysis identified female sex (OR, 1.191; p = 0.02), government-based (vs private) insurance (OR, 1.364; p < 0.001), and higher preoperative severity of illness (OR, 1.491; p = 0.001) to be associated with readmission. In addition, a significant volume-dependent relationship on 30-day readmission was identified, wherein undergoing operation at the higher-volume hospitals was protective for predicting readmission. Hierarchical regression modeling indicated that 31% of the variation in readmission rates among individual hospitals was accounted for by hospital volume. This study was limited by its retrospective nature and limited postoperative complication data. The national 30-day readmission after IPAA creation was 22.8%, at least double that of other colorectal procedures. This high rate of readmission was mitigated by centers performing the highest volume of cases. Avoidance of referral to centers performing very few of these procedures annually may improve perioperative outcomes and reduce associated morbidity.

PS-200 Hospital-level Variation In Radmission Rates Of Neonatal Intensive Care (nicu) Patients: A Potential Quality Measure

<h3>Background</h3> Variation in readmission rates may assess the quality of a provider through the quality of inpatient care or transitions from inpatient to outpatient providers. The aim of this project was to validate readmission rates as a measure of NICU quality. <h3>Methods</h3> Using birth certificates linked to maternal and infant hospital discharge records, a cohort was constructed of 23–34 week gestation infants who survived to hospital discharge at a California hospital discharging over 50 such infants per year between 1995–2009 (N = 296,509 at 141 hospitals). Unadjusted variations in hospital readmission rates within 7, 14, 30, 90, and 365 days after discharge were compared to rates adjusted for hospital casemix, including patient gestational age, insurance status, race/ethnicity, and maternal education, and BPD, IVH, NEC, and ROP as measures of chronic health conditions. <h3>Results</h3> Unadjusted readmission rates varied significantly between hospitals and across geographic regions, ranging from 2.2–28.4% 7–14 days after discharge to 2.7–34.4% 365 days after discharge. Some of this variation was explained by hospital casemix. However, after risk adjustment, there remained a 7.9–11.5 fold difference in readmission rates between hospitals with the lowest and highest rates across the five time frames that did not change when complications of preterm birth were included in the risk-adjustment model. <h3>Conclusions</h3> There is substantial variation in readmission rates of premature infants that is only partially explained by gestational age and social factors. Readmission rates may provide a measure of the quality of NICU care and the integration of services within a geographic area.

Challenges to Measuring Variation in Readmission Rates of Neonatal Intensive Care Patients

ObjectiveTo examine the viability of a hospital readmission quality metric for infants requiring neonatal intensive care. MethodsTwo cohorts were constructed. First, a cohort was constructed from infants born in California from 1995 to 2009 at 23 to 34 weeks' gestation, using birth certificates linked to maternal and infant inpatient records (N = 343,625). Second, the Medicaid Analytic eXtract (MAX) identified Medicaid-enrolled infants admitted to the neonatal intensive care unit (NICU) during their birth hospitalization in 18 states during 2006 to 2008 (N = 254,722). Hospital and state-level unadjusted readmission rates and rates adjusted for gestational age, birth weight, insurance status, gender, and common complications of preterm birth were calculated. ResultsWithin California, there were wide variations in hospital-level readmission rates that were not completely explained through risk adjustment. Similar unadjusted variation was seen between states using MAX data, but risk adjustment and calculation of hospital-level rates were not possible because of missing gestational age, birth weight, and birth hospital data. ConclusionsThe California cohort shows significant variation in hospital-level readmission rates after risk adjustment, supporting the premise that readmission rates of prematurely born infants may reflect care quality. However, state data do not include term and early term infants requiring neonatal intensive care. MAX allows for multistate comparisons of all infants requiring NICU care. However, there were extensive missing data in the few states with sufficient information on managed care patients to calculate state-level measures. Constructing a valid readmission measure for NICU care across diverse states and regions requires improved data collection, including potential linkage between MAX data and vital statistics records.

Variations in 30-day hospital readmission rates across primary care clinics within a tertiary referral center.

Reducing hospital readmissions is a national healthcare priority. Little is known about how readmission rates vary across unique primary care practices. To calculate all-cause 30-day hospital readmission rates at the level of individual primary care practices and identify factors associated with variations in these rates. Retrospective analysis Seven primary care clinics affiliated with the University of California, San Francisco (UCSF). Adults ≥18 years old with a primary care provider (PCP) at UCSF MEASUREMENTS: All-cause 30-day readmission rates were calculated for primary care clinics for discharges between July 1, 2009 and June 30, 2012. We built a model to identify demographic, clinical, and hospital factors associated with variation in rates. There were 12,564 discharges for patients belonging to the 7 clinics, with 8685 index discharges and 1032 readmissions. Readmission rates varied across practices, from 14.9% in Human Immunodeficiency Virus primary care and 7.7% in women's health. In multivariable analyses, factors associated with variation in readmission rates included: male gender (odds ratio [OR]: 1.21, 95% confidence interval [CI]: 1.05-1.40), Medicare insurance (OR: 1.31, 95% CI: 1.05, 1.64; Ref = private), Medicare-Medicaid dual eligible (OR: 1.26, 95% CI: 1.01-1.56), multiple comorbidities, and admitting services. Patients with a departed PCP awaiting transfer assignment to a new PCP had an OR of 1.59 (95% CI: 1.16-2.17) compared with having a current faculty PCP. Primary care practices are important partners in improving care transitions and reducing hospital readmissions, and this study introduces a new way to view readmission rates. PCP turnover may be an important risk factor for hospital readmissions.

Adding socioeconomic data to hospital readmissions calculations may produce more useful results.

To better understand the degree to which risk-standardized thirty-day readmission rates may be influenced by social factors, we compared results for hospitals in Missouri under two types of models. The first type of model is currently used by the Centers for Medicare and Medicaid Services for public reporting of condition-specific hospital readmission rates of Medicare patients. The second type of model is an "enriched" version of the first type of model with census tract-level socioeconomic data, such as poverty rate, educational attainment, and housing vacancy rate. We found that the inclusion of these factors had a pronounced effect on calculated hospital readmission rates for patients admitted with acute myocardial infarction, heart failure, and pneumonia. Specifically, the models including socioeconomic data narrowed the range of observed variation in readmission rates for the above conditions, in percentage points, from 6.5 to 1.8, 14.0 to 7.4, and 7.4 to 3.7, respectively. Interestingly, the average readmission rates for the three conditions did not change significantly between the two types of models. The results of our exploratory analysis suggest that further work to characterize and report the effects of socioeconomic factors on standardized readmission measures may assist efforts to improve care quality and deliver more equitable care on the part of hospitals, payers, and other stakeholders.

Reliability of hospital readmission rates in vascular surgery

The Center for Medicare and Medicaid Services recently began assessing financial penalties to hospitals with high readmission rates for a narrow set of medical conditions. Because these penalties will be extended to surgical conditions in the near future, we sought to determine whether readmissions are a reliable predictor of hospital performance with vascular surgery. We examined 4 years of national Medicare claims data from 1576 hospitals on beneficiaries undergoing three common vascular procedures: open or endovascular abdominal aortic aneurysm repair (n = 81,520) or lower extremity arterial bypass (n = 57,190). First, we divided our population into two groups on the basis of operative date (2005-2006 and 2007-2008) and generated hospital risk- and reliability-adjusted readmission rates for each time period. We evaluated reliability through the use of the "test-retest" method; highly reliable measures will show little variation in rates over time. Specifically, we evaluated the year-to-year reliability of readmissions by calculating Spearman rank correlation and weighted κ tests for readmission rates between the two time periods. The Spearman coefficient between 2005-2006 readmissions rankings and 2007-2008 readmissions rankings was 0.57 (P < .001) and weighted κ was 0.42 (P < .001), indicating a moderate correlation. However, only 32% of the variation in hospital readmission rates in 2007-2008 was explained by readmissions during the 2 prior years. There were major reclassifications of hospital rankings between years, with 63% of hospitals migrating among performance quintiles between 2005-2006 and 2007-2008. Risk-adjusted readmission rates for vascular surgery vary substantially year to year; this implies that much of the observed variation in readmission rates is either random or caused by unmeasured factors and not caused by changes in hospital quality that may be captured by administrative data.

Reliability of Readmission Rates as a Hospital Quality Measure in Cardiac Surgery

Recent policy interventions have reduced payments to hospitals with higher-than-predicted risk-adjusted readmission rates. However, whether readmission rates reliably discriminate deficiencies in hospital quality is uncertain. We sought to determine the reliability of 30-day readmission rates after cardiac operations as a measure of hospital performance and evaluate the effect of hospital caseload on reliability. We examined national Medicare beneficiaries undergoing coronary artery bypass graft operations for 2006 to 2008 (n=244,874 patients, n=1,210 hospitals). First, we performed multivariable logistic regression examining patient factors to calculate a risk-adjusted readmission rate for each hospital. We then used hierarchical modeling to estimate the reliability of this quality measure for each hospital. Finally, we determined the proportion of total variation attributable to three factors: true signal, statistical noise, and patient factors. A median of 151 (25% to 75% interquartile range, 79 to 265) coronary artery bypasses were performed per hospital during the 3-year period. The median risk-adjusted 30-day readmission rate was 17.6% (25% to 75% interquartile range, 14.4% to 20.8%). Of the variation in readmission rates, 55% was explained by measurement noise, 4% could be attributed to patient characteristics, and the remaining 41% represented true signal in readmission rates. Only 53 hospitals (4.4%) achieved a proficient level of reliability exceeding 0.70. To achieve this reliability, 599 cases were required during the 3-year period. In 33.7% of hospitals, a moderate degree of reliability exceeding 0.5 was achieved, which required 218 cases. The vast majority of hospitals do not achieve a minimum acceptable level of reliability for 30-day readmission rates. Despite recent enthusiasm, readmission rates are not a reliable measure of hospital quality in cardiac surgery.

The Ninety-Nine Percent: Focusing on the Patient to Reduce Readmissions

The Ninety-Nine Percent: Focusing on the Patient to Reduce Readmissions

Early Readmission of Newborns in a Large Health Care System

Early readmissions of apparently healthy newborns after discharge from well baby nurseries (WBN) may reflect an inadequate assessment of the newborn's readiness for discharge. To determine the frequency, causes, costs, and variations in rates of early rehospitalization of newborns discharged from 21 WBNs in 1 health care system. We queried the Enterprise Data Warehouse of Intermountain Healthcare (IH), a large Utah health care system, to identify newborns with gestational ages of 34 to 42 weeks discharged from an IH WBN between 2000 and 2010. We identified all newborns admitted to an IH hospital within 28 days of discharge and recorded their birth hospital, age, reason(s) for admission, length of stay, and inpatient costs. During the study period, 296 114 infants were discharged from IH hospital WBNs. Of these, 5308 (17.9/1000) were readmitted within 28 days of discharge. Of the 5308 infants who were readmitted, 41% had feeding problems, 35% had jaundice, and 33% had respiratory distress. The majority of newborns with feeding problems and jaundice were admitted in their first 2 weeks of life. Late preterm and early term newborns had higher rates of readmission than term infants. There were significant variations in readmission rates of newborns born at the 21 hospitals in the IH system. Potentially preventable conditions, including feeding problems and jaundice, account for most early readmissions of newborns. Late preterm and early term newborns have higher rates of readmission and should be assessed for other factors associated with early readmission.

Abstract WP392: Reducing Readmissions through Secondary Stroke Prevention Clinics

Introduction: Secondary Stroke Prevention Clinics (SPCs) were established in Ontario to provide the opportunity for appropriate patients to access best practice stroke care approaches to reduce the risk of recurrent stroke/TIA. Previous evaluations have shown that SPCs provide high quality care based on evidence-based performance measures; however the impact of SPCs on readmission rates has not been examined. Information on the proportion of patients who are readmitted to hospital for stroke/TIA after being seen at an SPC is important to understanding the role of SPCs in preventing stroke/TIA in Ontario. Methods: The Ontario Stroke Registry’s SPIRIT-SPC database was used to identify patients with presumed stroke/TIA referred to 13 SPCs between January 2007 and April 2011. Patients with a first clinic visit and an index stroke/TIA event between January 2007 and September 2010 were linked to Canadian Institute of Health Information (CIHI) inpatient database to calculate stroke-related readmission rates. Patients with elective admissions were excluded. The rates were based on the four years of data and were age- and sex- adjusted. Results: The study included 15,163 patients from SPIRIT-SPC seen at 13 SPCs in Ontario. The mean age was 66 years (±14) and 49.3% of patients were female. Seventy-eight percent (n= 11,777) of patients had an index stroke/TIA event reported on the SPC referral and of these only 60% (n = 9,040) had an ED visit or inpatient admission record in the CIHI databases capturing the index stroke/TIA event. The median time from referral to initial visit was 11 days. The stroke/TIA 30-, 90- and 180-day readmission rates following the SPC first visit were 0.7%, 1.3% and 1.8%, respectively. Variation in readmission rates across 13 SPCs ranged from 0 - 6%. Conclusion: Stroke/TIA-related readmission rates among patients seen at the 13 SPCs in Ontario patients are lower than what is reported in the literature and lower than observed among patients discharged from acute hospitals following a stroke/TIA ED visit or in-hospital stay. The lower rates observed at the 13 clinics may reflect less severe patients being seen at SPCs. Risk-adjusted models will be taken into consideration for future studies.

Abstract 12: Impact of Community Factors on Readmission Rates

Objective Readmissions in the Medicare population are common and expensive, and have thus become a major target for intervention by federal and state policymakers. Starting in 2012, the Centers for Medicare and Medicaid Services will penalize hospitals with high readmission rates, in hopes of reducing readmissions nationally. However, many worry that variations in readmission rates are driven less by the quality of the hospital’s care and more by community-level factors such as socioeconomic status and availability of primary care. Empirical data here would be immensely helpful. Therefore, we sought to understand to what extent variations in readmission rates across communities are driven by the quality of hospital care in those communities versus socioeconomic factors and supply-side factors. Methods We used national Medicare data and Dartmouth Atlas data to calculate hospital referral region (HRR)-level readmission rates for congestive heart failure (CHF). We examined the impact of community poverty levels, racial makeup, and supply of physicians and hospital beds on readmission rates using linear regression models. We also examined the impact of patient case-mix and hospital quality performance on readmission rates. Finally, using partial r-square values from multivariable models, we examined the degree to which community-level variations in readmission rates are driven by variation in the sickness of the population, the quality of the hospital care, the supply of physicians and hospital beds, and the socioeconomic makeup of the population. Results Readmission rates across communities (as defined by HRRs) ranged from 10% to 32% for CHF. In univariate analysis, HRRs with higher poverty rates, a higher proportion of black patients, more physicians and hospital beds in the community, and a sicker population had higher readmission rates. In multivariate models that examined the degree to which variation in readmission rates for CHF between HRRs was explained by each of these different community-level factors, we found that supply-side variables (physician and bed supply in the community) were most important (explaining 17% of the variation) followed by socioeconomic characteristics of the community (poverty rate and racial makeup) at 9%. Differences in hospital quality explained 5% of the variation in readmission rates and differences in case mix explained 4%. Conclusions and Implications Community-level socioeconomic variables and supply-side variables play a much larger role in explaining variations in readmissions than the quality of hospital care or underlying sickness of the population. These findings suggest that the current federal initiatives to penalize hospitals may not be optimally targeted. Focusing on community-level factors such as the supply and mix of physicians and targeting efforts towards poor and minority communities may be more fruitful approaches to reducing readmissions. Otherwise, we risk targeting the providers that care for disenfranchised patients in ways that are likely to worsen existing disparities in care.

The Relationship between Hospital Admission Rates and Rehospitalizations

Efforts to reduce hospital readmissions have focused primarily on improving transitional care. Yet variation in readmission rates may more closely reflect variation in the underlying hospitalization rates than differences in the quality of care during and after discharge. We used national Medicare data to calculate, for each local hospital referral region (HRR), the 30-day, 60-day, and 90-day readmission rates among patients discharged with congestive heart failure or pneumonia. We also calculated population-based all-cause admission rates among Medicare enrollees in each HRR. We examined the variation in HRR readmission rates that was explained by overall hospitalization rates versus differences in patients' coexisting conditions, quality of discharge planning, physician supply, and bed supply. HRR readmission rates ranged from 11 to 32% for congestive heart failure and from 8 to 27% for pneumonia. In univariate analyses, all-cause admission rates accounted for the highest proportion of regional variation in readmission rates for congestive heart failure (28%, 34%, and 37% at 30, 60, and 90 days, respectively); the next highest proportions were explained by case mix (11%, 15%, and 18%) and the number of cardiologists per capita (12%, 14%, and 15%). Results for pneumonia were similar, except that the number of pulmonologists per capita accounted for a lower proportion of the variation (6%, 8%, and 7%, respectively). In multivariate analyses, admission rates accounted for 16 to 24% of the variation for congestive heart failure and 11 to 20% for pneumonia; no other factor accounted for more than 6%. We found a substantial association between regional rates of rehospitalization and overall admission rates. Programs directed at shared savings from lower utilization of hospital services might be more successful in reducing readmissions than programs initiated to date. (Funded by the Commonwealth Fund.).

Unplanned readmission rates, length of hospital stay, mortality, and medical costs of ten common medical conditions: a retrospective analysis of Hong Kong hospital data

BackgroundStudies on readmissions attributed to particular medical conditions, especially heart failure, have generally not addressed the factors associated with readmissions and the implications for health outcomes and costs. This study aimed to investigate the factors associated with 30-day unplanned readmission for 10 common conditions and to determine the cost implications.MethodsThis population-based retrospective cohort study included patients admitted to all public hospitals in Hong Kong in 2007. The sample consisted of 337,694 hospitalizations in Internal Medicine. The disease-specific risk-adjusted odd ratio (OR), length of stay (LOS), mortality and attributable medical costs for the year were examined for unplanned readmissions for 10 medical conditions, namely malignant neoplasms, heart diseases, cerebrovascular diseases, pneumonia, injury and poisoning, nephritis and nephrosis, diabetes mellitus, chronic liver disease and cirrhosis, septicaemia, and aortic aneurysm.ResultsThe overall unplanned readmission rate was 16.7%. Chronic liver disease and cirrhosis had the highest OR (1.62, 95% confidence interval (CI) 1.39-1.87). Patients with cerebrovascular disease had the longest LOS, with mean acute and rehabilitation stays of 6.9 and 3.0 days, respectively. Malignant neoplasms had the highest mortality rate (30.8%) followed by aortic aneurysm and pneumonia. The attributed medical cost of readmission was highest for heart disease (US$3 199 418, 95% CI US$2 579 443-803 393).ConclusionsOur findings showed variations in readmission rates and mortality for different medical conditions which may suggest differences in the quality of care provided for various medical conditions. In-hospital care, comprehensive discharge planning, and post-discharge community support for patients need to be reviewed to improve the quality of care and patient health outcomes.

Thirty‐day readmission rates following hospitalization for pediatric sickle cell crisis at freestanding children's hospitals: Risk factors and hospital variation

Readmission within 30 days after hospitalization for sickle cell crisis was developed by The National Association of Children's Hospitals (NACHRI) to improve hospital quality, however, there have been few studies validating this. We performed a retrospective examination of 12,104 hospitalizations for sickle crisis from July 1, 2006 and December 31, 2008 at 33 freestanding children's hospitals in the Pediatric Health Information System (PHIS) database. Hospitalizations met NACHRI criteria; inpatient admission, APR DRG code 662, age < 18, discharge home, and length of stay within 2 SD of the mean. We describe 30-day readmission rates, identify factors associated with readmission accounting for patient-level clustering and compare unadjusted versus adjusted variation in readmission rates. We identified 4,762 patients with 12,104 qualifying hospitalizations (1-30 per patient). Two thousand seventy-four (17%) hospitalizations resulted in a readmission within 30 days. Significant factors associated with readmission were age (OR 1.06/year, P < 0.0001), inpatient use of steroids (OR 1.48, P = 0.01) admission for pain without other sickle complications (OR 1.52, P < 0.0001) and simple transfusion (OR 0.58, P = 0.0002). There was significant variation in readmission rates between hospitals, even after accounting for clustering by patient and hospital case mix. In a sample of free-standing children's hospitals, 17% of hospitalizations for sickle cell crisis result in readmission within 30 days. Older patients, those treated with steroids and those admitted for pain are more likely to be readmitted; simple transfusion is protective. Even after adjusting for case mix substantial hospital variation remains, but specific hospital to hospital comparisons differ depending on the exact methods used.