Readmissions To Different Hospitals Research Articles

Readmission outcomes following infectious hospitalization: same-care unit performed better than different-care unit

BackgroundPrevious studies showed that same-hospital readmission is associated with better outcomes than different-hospital readmission. However, little is known about whether readmission to the same care unit (same-care unit readmission) after infectious hospitalization performs better than readmission to a different care unit at the same hospital (different-care unit readmission).MethodsThis retrospective study screened patients rehospitalized within 30 days following admission to two acute medical wards for infectious diseases from 2013 to 2015 and included only those readmitted for unplanned medical reasons. Outcomes of interest included hospital mortality and length of stay of readmitted patients.ResultsThree hundred and fifteen patients were included; of those, 149(47%) and 166(53%) were classified as same-care unit and different-care unit readmissions, respectively. Same-care unit patients were more likely to be older(76 years vs. 70 years; P = 0.001), have comorbid chronic kidney disease(20% vs. 9%; P = 0.008), and have a shorter time to readmission(13 days vs. 16 days; P = 0.020) than different-care unit patients. Univariate analysis showed that same-care unit patients had a shorter length of stay than different-care unit patients(13 days vs. 18 days; P = 0.001), but had similar hospital mortality(20% vs. 24%; P = 0.385). The multivariable linear regression model indicated that same-care unit readmission was associated with a 5-day shorter hospital stay than different-care unit readmission(P = 0.002).ConclusionAmong patients readmitted within 30 days after hospitalization for infectious diseases, same-care unit readmission was associated with a shorter length of hospital stay than different-care unit readmission. Whenever feasible, it is encouraged to allocate a readmitted patient to the same care unit in hope of pursuing continuity and quality of care.

A Significant Number of Venous Thromboembolisms after Liver Transplantation are Missed: A National Analysis

Introduction: Prior studies of VTE after liver transplant have been limited to single institutions.We hypothesized that different-hospital readmission accounted for a significant number of readmissions with VTE after liver transplant and that predictive factors would be different for same- and different-hospital readmissions. Methods: The Nationwide Readmissions Database (2010-2014) was queried for all hospitalizations with liver transplantation.The primary outcome was VTE on index admission.The secondary outcomes were readmission within 180 days with VTE to any hospital and readmission to a different hospital within 180 days with VTE.Univariable with chi-squared and multivariable with logistic regression analyses identified risk factors. Results: Of the 31,889 patients during the study period, 784(2.5%) were diagnosed with a VTE during the index admission.There were 14,617 patients meeting inclusion criteria for the readmission outcomes.There were 298(2.0%) patients who were readmitted within 180 days with a VTE. Of these, 31(10.4%) were readmitted to a different hospital.After controlling for confounding factors through multivariable logistic regression, the strongest risk factors for VTE during the index admission were comorbidities of lymphoma (OR 7.22 [3.65-14.27]) and metastatic cancer (OR 4.45[2.32-8.52]).The strongest risk for readmission within 180 days with VTE was rheumatoid arthritis/collagen vascular diseases (OR 3.62 [1.67-7.85]) and chronic pulmonary disease (OR 2.61[1.90-3.60]). Conclusions: A significant percentage of patients develop VTE after liver transplant.Studies limited to transplant hospitals or single institutions may miss a portion of these patients that present to different hospitals for readmission.This study identifies the risk factors for VTE after liver transplant, this has important implications for prevention and benchmarking.

Readmission for venous thromboembolism after emergency general surgery is underreported and influenced by insurance status.

Prior studies of venous thromboembolism (VTE) after emergency general surgery (EGS) are not nationally representative nor do they fully capture readmissions to different hospitals. We hypothesized that different-hospital readmission accounted for a significant number of readmissions with VTE after EGS and that predictive factors would be different for same- and different-hospital readmissions. The 2014 Nationwide Readmissions Database was queried for nonelective EGS hospitalizations. The outcomes were readmission to the index or different hospitals within 180 days with VTE. Multivariate logistic regressions identified risk factors for readmission to index and different hospitals with VTE, reported as odds ratios with their 95% confidence intervals. Patients were excluded if during the index admission they expired, developed a VTE, had a vena cava filter placed, or did not have at least 180 days of follow-up. Of 1,584,605 patients meeting inclusion criteria, 1.3% (n = 20,963) of patients were readmitted within 180 days with a VTE. Of these, 28% (n = 5,866) were readmitted to a different hospital. Predictors overall for readmission with VTE were malignancy, prolonged hospitalization, age, and being publicly insured. However, predictors for readmission to a different hospital are based on hospital characteristics, including for-profit status, or procedure type. Nearly one in three readmissions with VTE after EGS occurs at a different hospital and may be missed by current quality metrics that only capture same-hospital readmission. Such metrics may underestimate for-profit hospital postoperative VTE rates relative to public and nonprofit hospitals, potentially affecting benchmarking and reimbursement. Postdischarge VTE rate is associated with insurance status. These findings have implications for policy and prevention programming design. Epidemiological study, level III.

Heart failure: Same-hospital vs. different-hospital readmission outcomes

BackgroundHeart Failure (HF) is a major driver of the readmissions/penalties in the US. Although extensive literature on rehospitalization attributed to HF, studies to compare outcomes for same-hospital vs. different-hospital readmissions are sparse. MethodsNationwide Readmission Database from 2010 to 14 utilized for HF-related hospitalization using appropriate ICD-9-CM diagnostic codes. 30-day readmissions were classified into two groups: same-hospital and different-hospital. A comparative analysis was conducted focusing on: in-hospital mortality, length of stay (LOS) and hospitalization cost. Hierarchical two-level modeling and propensity score matching utilized to adjust confounders. Results715,993 HF readmissions were identified, of which 21.3% were readmitted to different-hospital. Elderly, females, patients with higher co-morbidities and higher median household income were less likely to be readmitted to different-hospital. Index hospitalizations in a teaching hospital and/or larger hospital were associated with reduced different-hospital readmissions. Readmissions to the different hospital were associated with higher in-hospital mortality (7.7% vs. 6.6%, p < 0.001), higher resource utilization (LOS:7.5 days vs. 6.1 days, p < 0.001 and Cost: $22,602 vs. $13,740, p < 0.001) after adjusting for propensity score match. Similar results were observed with propensity score matching of multiple high-risk subgroups. ConclusionResources should be directed towards minimizing different-hospital HF readmissions to improve patient outcomes by identifying the vulnerable subgroup and further tailoring in-hospital and post-discharge care.

A comprehensive framework identifying readmission risk factors using the CHAID algorithm: a prospective cohort study.

To identify frequency of readmission after discharge from internal-medicine wards, readmission risk factors, and reasons and costs of readmission. Prospective cohort study. A tertiary-care hospital in Turkey. 2622 adult patients discharged from internal-medicine wards of the hospital between 1 February 2015 and 31 January 2016. Thirty day all-cause readmission rates, reasons and costs of readmission. To identify readmission risk factors Chi-square Automatic Interaction Detector (CHAID) analysis was conducted. The same hospital readmission rate was 17.9%, while the same hospital or different-hospital readmission rate was 21.3%. Receiver operating characteristic (ROC) curve analysis showed that the predictive performance of the CHAID algorithm was high. According to the CHAID algorithm, the most significant readmission risk factor was the main diagnosis of neoplasm at the index admission. In other diagnosis groups, higher Charlson comorbidity score, higher level of education, having a regular physician, and three dimensions of Readiness for Hospital Discharge Scale were significant risk factors for readmission. The most frequent reason for readmission was neoplasm, and the total cost of readmissions was ~$900 000. The CHAID algorithm for readmissions had a high predictive strength and provided details that aid physicians in decision-making. Measures must be taken from initial diagnosis to post-discharge follow-up, to minimize readmissions, especially in patients with neoplasm.

Risk factors and costs associated with nationwide nonelective readmission after trauma

Most prior studies of readmission after trauma have been limited to single institutions, whereas multi-institutional studies have been limited to single states and an inability to distinguish between elective and nonelective readmissions. The purpose of this study was to identify the risk factors and costs associated with nonelective readmission after trauma across the United States. The Nationwide Readmission Database was queried for all patients with nonelective admissions in 2013 and 2014 with a primary diagnosis of trauma. Univariate and multivariate logistic regression identified risk factors for 30-day nonelective same- and different-hospital readmission. The diagnosis groups on readmission were evaluated, and the total cost of readmissions was calculated. There were 1,180,144 patients admitted for trauma, the 30-day readmission rate was 9.4%, and 26.4% of readmissions occurred at a different hospital. The median readmission cost for patients readmitted to the same hospital was $8,298 (interquartile range, $4,899-$14,911), whereas the median readmission cost for patients readmitted to a different hospital was $8,568 (interquartile range, $4,935-$16,078; p < 0.01). Multivariate regression revealed that patients discharged against medical advice were at increased risk of readmission (odds ratio, 2.79; p < 0.01) and readmission to a different facility (odds ratio, 1.58; p < 0.01). Home health care was associated with a decreased risk of readmission to a different hospital (odds ratio, 0.74; p < 0.01). Septicemia and disseminated infections were the most common diagnoses on readmission (8.4%) and readmission to a different hospital (8.6%). A significant portion of US readmissions occur at different hospitals with implications for continuity of care, quality metrics, cost, and resource allocation. Home health care reduces the likelihood of nonelective readmission to a different hospital. Infection was the most common reason for readmission, with ramifications for outcomes research and quality improvement. Care management/epidimeological, level IV.

The costs of acute readmissions to a different hospital – Does the effect vary across provider types?

Treatment costs are found to vary substantially and systematically within DRGs. Several factors have been shown to contribute to the variation in costs within DRGs. We argue that readmissions might also explain part of the observed variation in costs. A substantial number of all readmissions occur to a different hospital. The change in hospital indicates that a progression of the illness has occurred since the initial hospitalisation. As a result, different-hospital readmissions might be more costly compared to same-hospital admissions.The aim of this paper is twofold. Firstly, to analyse differences in costs between different-hospital readmissions and same-hospital readmissions within the same DRG. Secondly, to investigate whether the effect of different-hospital readmission on costs vary depending of provider type (general versus teaching hospital).We use a rich Danish patient-level administrative data set covering inpatient stays in the period 2008–2010. We exploit the fact that some patients are readmitted within the same DRG and that some of these readmissions occur at different hospitals in a propensity score difference-in-difference design. The estimates are based on a restricted sample of n = 328 patients.Our results show that the costs of different-hospital readmissions are significantly higher relative to the costs of same-hospital readmission (approx. €777). Furthermore, the cost difference is found to be almost twice the size for patients readmitted to a teaching hospital (approx. €1016) (P < 0.10) compared to patients readmitted to a different general hospital (approx. €511) (P < 0.10). The results suggest that hospitals in general face a potential risk by treating different-hospital readmissions, and that the financial consequences are highest among teaching hospitals. If teaching hospitals are not compensated for the additional costs of treating different-hospital readmission patients, they might be unfairly funded under a DRG-based payment scheme.

Predictors and outcomes of unplanned readmission to a different hospital.

To examine patient, hospital and market factors and outcomes associated with readmission to a different hospital compared with the same hospital. A population-based, secondary analysis using multilevel causal modeling. Acute care hospitals in California in the USA. In total, 509 775 patients aged 50 or older who were discharged alive from acute care hospitals (index hospitalizations), and 59 566 who had a rehospitalization within 30 days following their index discharge. No intervention. Thirty-day unplanned readmissions to a different hospital compared with the same hospital and also the costs and health outcomes of the readmissions. Twenty-one percent of patients with a rehospitalization had a different-hospital readmission. Compared with the same-hospital readmission group, the different-hospital readmission group was more likely to be younger, male and have a lower income. The index hospitals of the different-hospital readmission group were more likely to be smaller, for-profit hospitals, which were also more likely to be located in counties with higher competition. The different-hospital readmission group had higher odds for in-hospital death (8.1 vs. 6.7%; P < 0.0001) and greater readmission hospital costs ($15 671.8 vs. $14 286.4; P < 0.001) than the same-hospital readmission group. Patient, hospital and market characteristics predicted different-hospital readmissions compared with same-hospital readmissions. Mortality and cost outcomes were worse among patients with different-hospital readmissions. Strategies for better care coordination targeting people at risk for different-hospital readmissions are necessary.

Same-Hospital Readmission Rates as a Measure of Pediatric Quality of Care.

Health care systems, payers, and hospitals use hospital readmission rates as a measure of quality. Although hospitals can track readmissions back to themselves (hospital A to hospital A), they lack information when their patients are readmitted to different hospitals (hospital A to hospital B). Because hospitals lack different-hospital readmission (DHR) data, they may underestimate all-hospital readmission (AHR) rates (hospital A to hospital A or B). To determine the prevalence of 30-day pediatric DHRs; to assess the effect of DHR on readmission performance; and to identify patient and hospital characteristics associated with DHR. We analyzed all-payer inpatient claims for 701,263 pediatric discharges (patients aged 0-17 years) from 177 acute care hospitals in New York State from January 1, 2005, through November 30, 2009, to identify 30-day same-hospital readmissions (SHRs), DHRs, and AHRs. Data analysis was performed from March 12, 2013, through April 6, 2015. We compared excess readmission ratios (calculated per the Medicare formula) using SHRs and AHRs to determine what might happen if the federal formula were applied to a specific state and to evaluate how often hospitals might accurately anticipate-using data available to them--whether they would incur penalties (excess readmission ratio >1) for readmissions. Using multivariate logistic regression, we identified patient- and hospital-level predictors of DHR vs SHR. The proportion of DHRs vs SHRs, AHR and SHR rates, and excess readmissions. Different-hospital readmissions constituted 13.9% of 31,325 AHRs. At the individual hospital level, the median (interquartile range) percentage of DHRs was 21.6% (12.8%-39.1%). The median (interquartile range) adjusted AHR rate was 3.4% (3.0%-4.1%), 38.9% higher than the median adjusted SHR rate of 2.5% (2.0%-3.4%) (P < .001). Excess readmission ratios using SHRs inaccurately anticipated penalties (changed from >1 to ≤ 1 or vice versa) for 20 of the 177 hospitals (11.3%); all were nonchildren's hospitals and 18 of 20 (90.0%) were nonteaching hospitals. Characteristics associated with higher odds ratios (ORs) (reported with 95% CIs) of DHR in multivariate analyses included being younger (compared with age <1 year, ORs [95% CIs] for the other age categories ranged from 0.76 [0.66-0.88] to 0.85 [0.73-0.99]); being white (ORs [95% CIs] for nonwhite race/ethnicity ranged from 0.74 [0.65-0.84] to 0.88 [0.79-0.99]); having private insurance (1.14 [1.04-1.24]); having a chronic condition indicator for a mental disorder (1.33 [1.13-1.56]) or a disease of the nervous system (1.37 [1.20-1.57]) or circulatory system (1.20 [1.00-1.43]); and admission to a nonchildren's (1.62 [1.01-2.60]), urban (ORs for nonurban hospitals ranged from 0.35 [0.24-0.52] to 0.36 [0.21-0.64]), or lower-volume (0.73 [0.64-0.84]) hospital (P < .05 for each). Different-hospital readmissions differentially affect hospitals' pediatric readmission rates and anticipated performance, making SHRs an incomplete surrogate for AHRs-particularly for certain hospital types. Failing to incorporate DHRs into readmission measurement may impede quality assessment, anticipation of penalties, and quality improvement.