Index Hospital Readmission Research Articles

Fragmentation of Care After Admissions for Burn: An Analysis of the National Readmissions Database

IntroductionBurn patients require complex longitudinal care guided by a team with specific clinical expertise. Burn patients are at increased need for readmission, which may result in Fragmentation of care (readmission to hospitals other than the index hospital), that can lead to increases in mortality, length of stay, and financial burden. In the already-complex burn patient, negative effects of care fragmentation may be magnified. We hypothesized that specific patient and demographic factors would be associated with burn readmissions and fragmentation of care. MethodsUtilizing the Nationwide Readmissions database, we identified patients admitted between January and September 2017 with a principal diagnosis of burn readmitted to any hospital within 90 d of index discharge. Demographics, total burn surface area, Elixhauser comorbidities, and index discharge disposition were collected. Primary outcome was readmission to the same hospital as the index admission. Penalized multivariable logistic regression assessed factors associated with index hospital readmission. ResultsOf 9987 burn patients who survived an index burn admission, 1082 (10.8%) were readmitted. Approximately half (51.6%, n = 558) of readmissions occurred at the index hospital. Factors associated with readmission to the same hospital were disposition to home with home health care (odds ratio: 1.42 [1.02-1.96], P = 0.037), private insurance (odds ratio: 1.63 [1.07-2.49], P = 0.022), and ZIP code income quartile >$74,000 (OR 1.87 [1.26-2.78], P = 0.002). ConclusionsDisposition home with home health care, private insurance, and a ZIP code income quartile greater than $74,000 were associated with a lower odds of having fragmentated care. Our data suggest that fragmentation of care may be potentiating a disparity for lower-income burn patients.

Development of a risk prediction model of potentially avoidable readmission for patients hospitalised with community-acquired pneumonia: study protocol and population

Introduction30-day readmission rate is considered an adverse outcome reflecting suboptimal quality of care during index hospitalisation for community-acquired pneumonia (CAP). However, potentially avoidable readmission would be a more relevant metric...

Comparison of Rates and Outcomes of Readmission to Index vs Non-index Hospitals After Intravenous Thrombolysis in Acute Stroke Patients.

National and regional systems of stroke care are designed to provide patients with widespread access to hospitals with thrombolytic capabilities. However, such triaging systems may contribute to fragmentation of care. This study aims to compare rates of readmission and outcomes between index and non-index hospitals for stroke patients following intravenous thrombolytic therapy (IVT). This study utilized a nationally representative sample of stroke patients with IVT from the Nationwide Readmissions Database from 2010 to 2014. Descriptive and regression analyses were performed for patient and hospital level factors that influenced 90-day readmissions and regression models were used to identify differences in mortality, complications, and repeat readmissions between patients readmitted to index (facility where IVT was administered) and non-index hospitals. In the study, 49415 stroke patients were treated with IVT, of whom 21.7% were readmitted within 90 days. Among readmissions, 79.4% of patients were readmitted to index hospitals and 20.6% to non-index hospitals. On multivariate logistic regression analysis, index hospital readmission was independently associated with lower frequency of second readmissions (non-index OR 1.09, 95%CI 1.07-1.11, p<0.0001) but not with increased mortality or major complications (p=ns). Approximately one-fifth of stroke patients treated with thrombolysis were readmitted within 90 days, one-fifth of whom were readmitted to non-index hospitals. Although readmission to index hospital was associated with lower frequency of subsequent readmissions, readmission to non-index hospital was not associated with increased mortality or major complications. This difference may be due to standardized algorithms, mature systems of care, and demanding metrics required of stroke centers.

Increased complication and mortality among non-index hospital readmissions after brain tumor resection is associated with low-volume readmitting hospitals.

Fragmentation of care following craniotomy for tumor resection is increasingly common with the regionalization of neurosurgery. Hospital readmission to a hospital (non-index) other than the one from which patients received their original care (index) has been associated with increases in both morbidity and mortality for cancer patients. The impact of non-index readmission after surgical management of brain tumors has not previously been evaluated. The authors set out to determine rates of non-index readmission following craniotomy for tumor resection and evaluated outcomes following index and non-index readmissions. Retrospective analyses of data from cases involving resection of a primary brain tumor were conducted using data from the Nationwide Readmissions Database (NRD) for 2010-2014. Multivariate logistic regression was used to evaluate the independent association of patient and hospital factors with readmission to an index versus non-index hospital. Further analysis evaluated association of non-index versus index hospital readmission with mortality and major complications during readmission. Effects of readmission hospital procedure volume on mortality and morbidity were evaluated in post hoc analysis. In a total of 17,459 unplanned readmissions, 84.4% patients were readmitted to index hospitals and 15.6% to non-index hospitals. Patient factors associated with increased likelihood of non-index readmission included older age (75+: OR 1.44, 95% CI 1.19-1.75), elective index admission (OR 1.19, 95% CI 1.08-1.30), increased Elixhauser comorbidity score ≥2 (OR 1.18, 95% CI 1.01-1.37), and malignant tumor diagnosis (OR 1.32, 95% CI 1.19-1.45) (all p < 0.04). Readmission to a non-index facility was associated with a 28% increase in major complications (OR 1.28, 95% CI 1.14-1.43, p < 0.001) and 21% increase in mortality (OR 1.21, 95% CI 1.02-1.44, p = 0.032) in initial analysis. Following a second multivariable logistic regression analysis including the readmitting hospital characteristics, low procedure volume of a readmitting facility was significantly associated with non-index readmission (p < 0.001). Readmission to a lower-procedure-volume facility was associated with a 46%-75% increase in mortality (OR 1.46-1.75, p < 0.005) and a 21%-35% increase in major complications (OR 1.21-1.34, p < 0.005). Following adjustment for volume at a readmitting facility, admission to a non-index facility was no longer associated with mortality (OR 0.90, 95% CI 0.71-1.14, p = 0.378) or major complications (OR 1.09, CI 0.94-1.26, p = 0.248). Of patient readmissions following brain tumor resection, 15.6% occur at a non-index facility. Low procedure volume is a confounder for non-index analysis and is associated with an increased likelihood of major complications and mortality, as compared to readmission to high-procedure-volume hospitals. Further studies should evaluate interventions targeting factors associated with unplanned readmission.

Characteristics and clinical outcomes of index versus non-index hospital readmissions in Australian hospitals: a cohort study.

Objective Risk factors and clinical outcomes of non-index hospital readmissions (readmissions to a hospital different from the previous admission) have not been studied in Australia. The present study compared characteristics and clinical outcomes between index and non-index hospital readmissions in the Australian healthcare setting. Methods This retrospective cohort study included medical admissions from 2012 to 2016 across all major public hospitals in South Australia. Readmissions within 30 day to all public hospitals were captured using electronic health information system. In-hospital mortality and readmission length of hospital stay (LOS) were compared, along with 30-day mortality and subsequent readmissions among patients readmitted to index or non-index hospitals. Results Of 114105 index admissions, there were 20539 (18.0%) readmissions. Of these, 17519 (85.3%) were index readmissions and 3020 (14.7%) were non-index readmissions. Compared with index readmissions, patients in the non-index readmissions group had a lower Charlson comorbidity index, shorter LOS and fewer complications during the index admission and were more likely to be readmitted with a different diagnosis to the index admission. No difference in in-hospital mortality was observed, but readmission LOS was shorter and 30-day mortality was higher among patients with non-index readmissions. Conclusion A substantial proportion of patients experienced non-index hospital readmissions. Non-index hospital readmitted patients had no immediate adverse outcomes, but experienced worse 30-day outcomes. What is known about the topic? A significant proportion of unplanned hospital readmissions occur to non-index hospitals. North American studies suggest that non-index hospital readmissions are associated with worse outcomes for patients due to discontinuity of care, medical reconciliation and delayed treatment. Limited studies have determined factors associated with non-index hospital readmissions in Australia, but whether such readmissions lead to adverse clinical outcomes is unknown. What does this paper add? In the Australian healthcare setting, 14.7% of patients were readmitted to non-index hospitals. Compared with index hospital readmissions, patients admitted to non-index hospitals had a lower Charlson comorbidity index, a shorter index LOS and fewer complications during the index admission. At the time of readmission there was no differences in discharge summary completion rates between the two groups. Unlike other studies, the present study found no immediate adverse outcomes for patients readmitted to non-index hospitals, but 30-day outcomes were worse than for patients who had an index hospital readmission. What are the implications for practitioners? Non-index hospital readmissions may not be totally preventable due to factors such as ambulance diversions stemming from emergency department overcrowding and prolonged emergency department waiting times. Patients should be advised to re-present to hospital in case they experience recurrence or relapse of a medical condition, and preferably should be readmitted to the same hospital to prevent discontinuity of care.

PC056. Hidden Readmissions After Carotid Endarterectomy and Stenting: A National Analysis

The unplanned readmission rate after carotid procedures is approximately 6%, but most prior studies are limited to only index hospital readmission. Up to one in three postoperative readmissions occur at a different hospital and are missed by current metrics. There are no national studies examining readmission after carotid endarterectomy and carotid artery stenting (CAS), including to different hospitals. The purpose of this study was to compare unplanned readmission incidence and risk factors between carotid endarterectomy and CAS, including readmissions to any hospital, in a nationally representative sample. The 2010 through 2014 Nationwide Readmissions Database was queried for all patients undergoing carotid endarterectomy or CAS. Outcomes included postprocedure stroke (cerebrovascular accident), 30-day readmission, and mortality during initial admission or 30-day readmission. A univariable analysis of 39 demographic, clinical, and hospital variables was conducted with significance set at P < .05. Significant variables were included in a multivariable logistic regression to identify risk factors for readmission. Results were weighted for national estimates. There were 527,622 patients who underwent carotid procedures and 13.1% (n = 69,187) who underwent CAS. The 30-day readmission rate was 6.8% (n = 35,782) and, of those, 24.8% (n = 8,862) were readmitted to a different hospital. When controlling for other factors, readmission to a different hospital increased mortality risk (odds ratio [OR], 1.45; 95% confidence interval [CI], 1.29-1.63; P < .01). Risk factors for 30-day readmission to a different hospital included elective initial admission (OR, 1.43; 95% CI, 1.36-1.51; P < .01), Medicaid (1.29; 95% CI, 1.13-1.47; P < .01), Medicare (1.21; 95% CI, 1.12-1.31; P < .01), and Charlson Comorbidity Index score of 2 (OR, 1.09; 95% CI, 1.02-1.16; P = .01). CAS was a risk factor for readmission (OR, 1.11; 95% CI, 1.07-1.14; P < .01) and readmission to a different hospital (OR, 1.38; 95% CI, 1.29-1.48; P < .01). CAS was also a risk factor for postoperative cerebrovascular accident on admission (OR, 1.29; 95% CI, 1.19-1.38; P < .01) and readmission (OR, 1.49; 95% CI, 1.37-1.62; P < .01). Previously unreported, one in four readmissions after carotid procedures occur at a different hospital. This fragmentation of care increases mortality risk. Readmission after CAS in particular is underestimated. Patient counseling using current risk data is possibly flawed. There are significant implications for clinical decision making, outcomes measurement, benchmarking, and quality assessment.

An informatics-based approach to reducing heart failure all-cause readmissions: the Stanford heart failure dashboard.

Reduction of 30-day all-cause readmissions for heart failure (HF) has become an important quality-of-care metric for health care systems. Many hospitals have implemented quality improvement programs designed to reduce 30-day all-cause readmissions for HF. Electronic medical record (EMR)-based measures have been employed to aid in these efforts, but their use has been largely adjunctive to, rather than integrated with, the overall effort. We hypothesized that a comprehensive EMR-based approach utilizing an HF dashboard in addition to an established HF readmission reduction program would further reduce 30-day all-cause index hospital readmission rates for HF. After establishing a quality improvement program to reduce 30-day HF readmission rates, we instituted EMR-based measures designed to improve cohort identification, intervention tracking, and readmission analysis, the latter 2 supported by an electronic HF dashboard. Our primary outcome measure was the 30-day index hospital readmission rate for HF, with secondary measures including the accuracy of identification of patients with HF and the percentage of patients receiving interventions designed to reduce all-cause readmissions for HF. The HF dashboard facilitated improved penetration of our interventions and reduced readmission rates by allowing the clinical team to easily identify cohorts with high readmission rates and/or low intervention rates. We significantly reduced 30-day index hospital all-cause HF readmission rates from 18.2% at baseline to 14% after implementation of our quality improvement program ( P = .045). Implementation of our EMR-based approach further significantly reduced 30-day index hospital readmission rates for HF to 10.1% ( P for trend = .0001). Daily time to screen patients decreased from 1 hour to 15 minutes, accuracy of cohort identification improved from 83% to 94.6% ( P = .0001), and the percentage of patients receiving our interventions, such as patient education, also improved significantly from 22% to 100% over time ( P < .0001). In an institution with a quality improvement program already in place to reduce 30-day readmission rates for HF, an EMR-based approach further significantly reduced 30-day index hospital readmission rates.

Clinical Interventions to Reduce Preventable Hospital Readmission After Percutaneous Coronary Intervention.

Hospital readmissions are common and costly and, in some cases, may be related to problems with care processes. We sought to reduce readmissions after percutaneous coronary intervention (PCI) in a large tertiary care facility through programs to target vulnerabilities predischarge, after discharge, and during re-presentation to the emergency department. During initial hospitalization, we assessed patients' readmission risk with a validated risk score and used a discharge checklist to ensure access to appropriate medications and close follow-up for high-risk patients. We also developed patient education videos about chest discomfort and heart failure. After discharge, we established a new follow-up clinic with cardiology fellows. A computerized system was developed to automatically notify cardiologists when patients presented to the emergency department within 30 days of PCI to enhance patient access to cardiology care in the emergency department. Early cardiologist assessment and assistance with triage was encouraged, and the emergency department used a risk stratification algorithm derived from a local database of patients to triage patients presenting with chest discomfort after PCI. We tracked the number of patients readmitted after PCI to our hospital. With our interventions, from 2011 to 2015, the index hospital readmission rate has declined from 9.6% to 5.3%. This program could provide tangible structural changes that can be implemented in other healthcare centers, both reducing the cost of care and improving the quality of care for patients with PCI.

Readmission destination and risk of mortality after major surgery: an observational cohort study

Hospital readmissions are common after major surgery, although it is unknown whether patients achieve improved outcomes when they are readmitted to, and receive care at, the index hospital where their surgical procedure was done. We examined the association between readmission destination and mortality risk in the USA in Medicare beneficiaries after a range of common operations. By use of claims data from Medicare beneficiaries in the USA between Jan 1, 2001, and Nov 15, 2011, we assessed patients who needed hospital readmission within 30 days after open abdominal aortic aneurysm repair, infrainguinal arterial bypass, aortobifemoral bypass, coronary artery bypass surgery, oesophagectomy, colectomy, pancreatectomy, cholecystectomy, ventral hernia repair, craniotomy, hip replacement, or knee replacement. We used logistic regression models incorporating inverse probability weighting and instrumental variable analysis to measure associations between readmission destination (index vs non-index hospital) and risk of 90 day mortality for patients who underwent surgery who needed hospital readmission. 9,440,503 patients underwent one of 12 major operations, and the number of patients readmitted or transferred back to the index hospital where their operation was done varied from 186,336 (65·8%) of 283,131 patients who were readmitted after coronary artery bypass grafting, to 142,142 (83·2%) of 170,789 patients who were readmitted after colectomy. Readmission was more likely to be to the index hospital than to a non-index hospital if the readmission was for a surgical complication (189,384 [23%] of 834,070 patients readmitted to index hospital vs 36,792 [13%] of 276,976 patients readmitted non-index hospital, p<0·0001). Readmission to the index hospital was associated with a 26% lower risk of 90 day mortality than was readmission to a non-index hospital, with inverse probability weighting used to control for selection bias (odds ratio [OR] 0·74, 95% CI 0·66-0·83). This effect was significant (p<0·0001) for all procedures in inverse probability-weighted models, and was largest for patients who were readmitted after pancreatectomy (OR 0·56, 95% CI 0·45-0·69) and aortobifemoral bypass (OR 0·69, 95% CI 0·61-0·77). By use of hospital-level variation among regional index hospital readmission rates as an instrument, instrumental variable analysis showed that the patients with the highest probability of returning to the index hospital had 8% lower risk of mortality (OR 0·92 95% CI 0·91-0·94) than did patients who were less likely to be readmitted to the index hospital. In the USA, patients who are readmitted to hospital after various major operations consistently achieve improved survival if they return to the hospital where their surgery took place. These findings might have important implications for cost-effectiveness-driven regional centralisation of surgical care. None.