Effect of hospital-SNF referral linkages on rehospitalization.
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ABSTRACT: Objective
To determine whether the rate of rehospitalization is lower among patients discharged to skilled nursing facilities (SNFs) with which a hospital has a strong linkage.Data sources/collection
We used national Medicare enrollment, claims, and the Minimum Data Set to examine 2.8 million newly discharged patients to 15,063 SNFs from 2,477 general hospitals between 2004 and 2006.Study design
We examined the relationship between the proportion of discharges from a hospital and alternative SNFs on the rehospitalization of patients treated by that hospital-SNF pair using an instrumental variable approach. We used distances to alternative SNFs from residence of the patients of the originating hospital as the instrument.Principal findings
Our estimates suggest that if the proportion of a hospital's discharges to an SNF was to increase by 10 percentage points, the likelihood of patients treated by that hospital-SNF pair to be rehospitalized within 30 days would decline by 1.2 percentage points, largely driven by fewer rehospitalizations within a week of hospital discharge.Conclusions
Stronger hospital-SNF linkages, independent of hospital ownership, were found to reduce rehospitalization rates. As hospitals are held accountable for patients' outcomes postdischarge under the Affordable Care Act, hospitals may steer their patients preferentially to fewer SNFs.
SUBMITTER: Rahman M
PROVIDER: S-EPMC3844283 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundEmergency department visits and rehospitalization are common after hospital discharge.ObjectiveTo test the effects of an intervention designed to minimize hospital utilization after discharge.DesignRandomized trial using block randomization of 6 and 8. Randomly arranged index cards were placed in opaque envelopes labeled consecutively with study numbers, and participants were assigned a study group by revealing the index card.SettingGeneral medical service at an urban, academic, safety-net hospital.Patients749 English-speaking hospitalized adults (mean age, 49.9 years).InterventionA nurse discharge advocate worked with patients during their hospital stay to arrange follow-up appointments, confirm medication reconciliation, and conduct patient education with an individualized instruction booklet that was sent to their primary care provider. A clinical pharmacist called patients 2 to 4 days after discharge to reinforce the discharge plan and review medications. Participants and providers were not blinded to treatment assignment.MeasurementsPrimary outcomes were emergency department visits and hospitalizations within 30 days of discharge. Secondary outcomes were self-reported preparedness for discharge and frequency of primary care providers' follow-up within 30 days of discharge. Research staff doing follow-up were blinded to study group assignment.ResultsParticipants in the intervention group (n = 370) had a lower rate of hospital utilization than those receiving usual care (n = 368) (0.314 vs. 0.451 visit per person per month; incidence rate ratio, 0.695 [95% CI, 0.515 to 0.937]; P = 0.009). The intervention was most effective among participants with hospital utilization in the 6 months before index admission (P = 0.014). Adverse events were not assessed; these data were collected but are still being analyzed.LimitationThis was a single-center study in which not all potentially eligible patients could be enrolled, and outcome assessment sometimes relied on participant report.ConclusionA package of discharge services reduced hospital utilization within 30 days of discharge.FundingAgency for Healthcare Research and Quality and National Heart, Lung, and Blood Institute, National Institutes of Health.
| S-EPMC2738592 | biostudies-literature
Project description: Not available
| S-EPMC6902762 | biostudies-literature
Project description:BackgroundClose to 1 in 5 patients admitted to a skilled nursing facility (SNF) are readmitted to the acute hospital within 30 days, and a substantial percentage are readmitted within 2 days of the SNF admission. These rapid returns to the hospital may provide insights for improving care transitions between the acute hospital and the SNF.ObjectivesTo describe the characteristics of SNF to hospital transfers that occur within 48 hours and 30 days of SNF admission based on root cause analyses (RCAs) performed by SNF staff, and identify potential areas of focus for improving transitions between hospitals and SNFs.DesignTrained staff from SNFs enrolled in a randomized, controlled clinical trial of the INTERACT (Interventions to Reduce Acute Care Transfers) quality improvement program performed retrospective RCAs on hospital transfers during a 12-month implementation period.SettingSNFs from across the United States.Participants64 of 88 SNFs randomized to the intervention group submitted RCAs.InterventionsSNFs were implementing the INTERACT quality improvement program.MeasuresData were abstracted from the INTERACT Quality Improvement (QI) tool, a structured, retrospective RCA on hospital transfers.ResultsAmong 4658 transfers for which data on the time between SNF admission and hospital transfer were available, 353 (8%) occurred within 48 hours of SNF admission, 524 (11%) 3 to 6 days after SNF admission, 1450 (31%) 7 to 29 days after SNF admission, and 2331 (50%) occurred 30 days or longer after admission. Comparisons between transfers that occurred within 48 hours and within 30 days of SNF admission to transfers that occurred 30 days or longer after SNF admission revealed several statistically significant differences between patient risk factors for transfer, symptoms and signs precipitating the transfers, and other characteristics of the transfers. Hospitalization in the last 30 days and year was significantly more common among those with rapid returns to the hospital. Shortness of breath was significantly more common among those transferred within 48 hours or 30 days, and falls, functional decline, suspected respiratory infection, and new urinary incontinence less common. SNF staff rated a higher proportion of transfers within 30 days versus 30 days or longer as potentially preventable (25.1% vs 21.5%, P = .005). Case descriptions derived from the QI tools of transfers back to the hospital within 48 hours of SNF admission illustrate several factors underlying these rapid returns to the hospital.ConclusionRCAs on transfers back to the hospital shortly after SNF admission provide insights into strategies that both hospitals and SNFs can consider in collaborative efforts to reduce potentially avoidable hospital readmissions.
| S-EPMC5063303 | biostudies-literature
Project description:To evaluate whether Medicare-style bundled payments are lower or higher for beneficiaries discharged from hospitals with postacute care (PAC) referrals concentrated among fewer PAC providers.Medicare Part A and Part B claim (2008-2012) for all beneficiaries residing in any of 17 market areas: the Provider of Service file, the Healthcare Cost Report Information System, and the Dartmouth Atlas.An observational study in which hospitals were distinguished according to PAC referral concentration, which is the tendency to utilize fewer rather than more PAC providers. We tested the hypothesis that higher referral concentration would be associated with total Medicare bundled payments.The data represent a convenience sample of market areas that were defined by the locations of grantees from the ONC Beacon Community Program.The four most-used PAC providers accounted for an average of 60 percent of patients discharged from hospitals in the sample. Regression analysis suggested that higher referral concentration was associated with lower Medicare costs per bundle.Hospitals that tend to use fewer PAC providers may lead to lower costs for payers such as Medicare. The study results reinforce the importance of limited networks for PAC services under bundling arrangements for hospital and PAC payments.
| S-EPMC5682136 | biostudies-literature
Project description:OBJECTIVES:To investigate whether sending patients a copy of their referral letter can reduce non-attendance at outpatient departments. DESIGN:Blinded randomised controlled trial. SETTING:13 general practices in Exeter, Devon. SUBJECTS:2078 new consultant referrals from 26 doctors. MAIN OUTCOME MEASURES:Non-attendance at outpatient departments. RESULTS:The doctors excluded 117 (5.6%) referrals, and 100 (4.8%) received no appointment. Attendance data were available for 1857 of the 1861 patients sent an appointment (99.8%). The receipt of a copy letter had no effect on the non-attendance rate: copy 50/912 (5.5%) versus control 50/945 (5.3%). CONCLUSION:Copy letters are ineffective in reducing non-attendance at outpatient departments.
| S-EPMC27884 | biostudies-literature
Project description:Ethiopia has one of the world's poorest neonatal mortality rates (1). As a British paediatrician working for one year, the main aim was to improve neonatal care in a referral hospital's neonatal unit. An initial project looking at all admissions to the unit over the month of October 2012, revealed the death rate within the unit was 21% of all admissions. Of the 55 admissions only 43 (75%) had a temperature recorded at admission. 29 (67%) of these were hypothermic, and of these 29 initially hypothermic babies, 19 (65%) remained hypothermic or did not have another temperature documented during their stay. Only 33 (56%) had a heart rate and respiratory rate recorded on admission and only 11 (19%) had any further vital signs recorded during their stay. 19 of the admitted infants had a diagnosis of low birth weight and 10 (53%) of these either died or left against medical advice. With this information regular basic neonatal care teaching sessions for doctors and nursing staff were initiated. Vital signs charts were introduced and unit specific protocols were written. Continuous Positive Airway Pressure (CPAP) was trialled and found to be effective, and CPAP training was given to all nurses and doctors on the unit. A room was set aside to be used solely as Kangaroo Mother Care (KMC). The nurses were taken to a very effective neonatal unit as an 'experience sharing' trip to help with motivation and a grant was also obtained for basic equipment. After 6 months the project was repeated for all the admissions during February 2013. The death rate had progressively fallen and was 10% in February. 54 patients (98%) had a temperature taken on admission and of these, 27 (49%) were hypothermic. 25 (93%) of these hypothermic infants had subsequent temperatures documented which were normal. 54 (98%) had heart rate and respiratory rate recorded on admission and 50 (90%) had them recorded at least daily during their stay. Of the 22 babies with low birth weight (40%), only 4 (18%) died/left against medical advice. 4 received CPAP and 6 were treated with KMC.
| S-EPMC4663849 | biostudies-literature
Project description:OBJECTIVE:To develop an automated, data-driven, and scale-flexible method to delineate hospital service areas (HSAs) and hospital referral regions (HRRs) that are up-to-date, representative of all patients, and have the optimal localization of hospital visits. DATA SOURCES:The 2011 state inpatient database in Florida from the Healthcare Cost and Utilization Project. STUDY DESIGN:A network optimization method was used to redefine HSAs and HRRs by maximizing patient-to-hospital flows within each HSA/HRR while minimizing flows between them. We first constructed as many HSAs/HRRs as existing Dartmouth units in Florida, and then compared the two by various metrics. Next, we sought to derive the optimal numbers and configurations of HSAs/HRRs that best reflect the modularity of hospitalization patterns in Florida. PRINCIPAL FINDINGS:The HSAs/HRRs by our method are favored over the Dartmouth units in balance of region size and market structure, shape, and most important, local hospitalization. CONCLUSIONS:The new method is automated, scale-flexible, and effective in capturing the natural structure of the health care system. It has great potential for applications in delineating other health care service areas or in larger geographic regions.
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Project description:Predicting unplanned rehospitalizations has traditionally employed logistic regression models. Machine learning (ML) methods have been introduced in health service research and may improve the prediction of health outcomes. The objective of this work was to develop a ML model to predict 30-day all-cause rehospitalizations based on the French hospital medico-administrative database.This was a retrospective cohort study of all discharges in the year 2015 from acute-care inpatient hospitalizations in a tertiary-care university center comprising 4 French hospitals. The study endpoint was unplanned 30-day all-cause rehospitalization. Logistic regression (LR), classification and regression trees (CART), random forest (RF), gradient boosting (GB), and neural networks (NN) were applied to the collected data. The predictive performance of the models was evaluated using the H-measure and the area under the ROC curve (AUC).Our analysis included 118,650 hospitalizations, of which 4127 (3.5%) led to rehospitalizations via emergency departments. The RF model was the most performant model according to the H-measure (0.29) and the AUC (0.79). The performances of the RF, GB and NN models (H-measures ranged from 0.18 to 0. 29, AUC ranged from 0.74 to 0.79) were better than those of the LR model (H-measure = 0.18, AUC = 0.74); all P values <.001. In contrast, LR was superior to CART (H-measure = 0.16, AUC = 0.70), P < .0001.The use of ML may be an alternative to regression models to predict health outcomes. The integration of ML, particularly the RF algorithm, in the prediction of unplanned rehospitalization may help health service providers target patients at high risk of rehospitalizations and propose effective interventions at the hospital level.
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Project description:The pattern of dermatological admissions based on retrospective study of 1578 Armed Forces personnel who were hospitalised during period of Jan 1984 to Dec 1994 at Command Hospital Southern Command, Pune were analysed. Average yearly admission was 144. Eczema was the most common skin disease requiring admission and accounted for 29.5 per cent cases followed by infections and infestations 25.5, papulo-squamous disorders 21.4, urticaria 4.2, connective tissue disorders 4.2, bullous disorders 2.8, and others 12.4 per cent.
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Project description:IntroductionInjury accounts for more than 5.8 million deaths globally with an increasing burden in the developing world. In Kenya, trauma is one of the top 10 leading causes of death. However, no formal continuous injury surveillance systems are in place to inform injury prevention, pre-hospital care or emergency department management. The aim of this study was to implement a hospital-based trauma registry to characterize high acuity injuries presenting to a private tertiary, teaching and referral hospital in Kenya.MethodsFrom January to December 2015, data was prospectively collected at a private tertiary, teaching and referral hospital in Nairobi, Kenya. Patients presenting with a traumatic injury for the first time who were admitted to the hospital for at least 48 hours were included in the study. Basic information pertaining to demographics, details of the injury, pre-hospital care and transport, hospital-based management, length of stay and disposition were collected. An injury severity score (ISS) was calculated on each patient and stratified by the mechanism of injury. Descriptive statistics and multivariate logistic regression were used to analyze data and assess risk factors associated with injury severity.ResultsThere were 101 patients included in the study, the majority of whom were 30 to 39 years of age and male (63%). Seventy-one per cent of patients had a preexisting medical condition with hypertension (26%) and diabetes (13%) being the most common. The most common mechanism of injury was fall (46%) followed by road traffic incidents (RTI) (32%). Most injuries took place at home (43%). Most RTI were caused by cars (63%), with the driver being the most frequently injured (38%). The most common mode of arrival to the emergency department was by private car (72%). The median time between the accident and arrival at the emergency department was 1hr 10 minutes. The majority of the patients had injuries to one area (83%) with the extremities/bony pelvis (72%) being the most common. The median Injury Severity Score was 5 (range 1-34) with the majority (90%) classified as minor injuries (ISS<12). The highest severity of injury as determined by ISS was seen in gunshot wounds.ConclusionsInjured patients in Kenya showed concordance with prior studies looking at injury prevalence in the developing world when looking at demographics and place of injury. However, differences were found when looking at the mechanism of injury, with falls surpassing road traffic incidents. A delayed presentation to the hospital was also noted in this patient population. Given the rate of traumatic injuries in Kenya and their contribution to morbidity, mortality and overall healthcare costs, there is a need to implement formal trauma registries in all major hospitals in Kenya to generate more data that can be used to improve injury prevention, the overall trauma system and enhance training and preparedness.
| S-EPMC6655740 | biostudies-literature