Dataset Information

Determinants of patient choice for hospital readmission after township hospitalisation: a population-based retrospective study in China.

ABSTRACT: OBJECTIVE:The lack of coordinated and appropriate healthcare across sectors has produced more patients for county hospitals in China. This study examined differences in patient choice between township and county hospitals for readmission after a first township hospitalisation, and the determinants that influenced this choice. DESIGN:A retrospective study of readmissions across hospitals after a first admission in township hospital. A township-township (TT) inpatient group and a township-county (TC) inpatient group were compared. A two-level logistic regression model was used to examine the determinants of choice for hospital readmission. SETTING:Data were drawn from a population-based health utilisation database for Qianjiang District, China, from 1 January 2008 to 31 December 2013. PARTICIPANTS:This study focused on readmitted patients whose first admission was in a township hospital. Readmission cases were identified as the same diagnosis (International Classification of Diseases, Tenth Revision) in a subsequent hospitalisation within 30 days. In total, 6764 readmissions had first admissions in township hospitals. PRIMARY OUTCOME MEASURES:Patient choice for hospital readmission after a first township hospitalisation. RESULTS:The TT group accounted for 62.5% (4225) and the TC group for 37.5% (2539) of readmissions in 6 years, and the proportion of TC readmissions in total inpatients increased from 1.66% to 1.89%. Readmission rates varied among towns (p<0.001). Differences between the TC and TT groups included: length of stay (LOS) of first admission (6.96 days vs 9.23 days), average interval between admissions (6.03 days vs 14.95 days) and disease category. Admission year, age, travel time to county hospital, interval between admissions, first admission LOS and disease category were determinants of choice for hospital readmission. CONCLUSIONS:Patients whose first admission was in a township hospital were more likely to be readmitted to a county hospital. A combination of first LOS and interval between admissions may be an effective identification index for TC readmission. TRIAL REGISTRATION NUMBER:ChiCTR-OOR-14005563.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC6089325 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Determinants of patient choice for hospital readmission after township hospitalisation: a population-based retrospective study in China.

Zhang Yan Y Niu Yadong Y Zhang Liang L

BMJ open 20180808 8

<h4>Objective</h4>The lack of coordinated and appropriate healthcare across sectors has produced more patients for county hospitals in China. This study examined differences in patient choice between township and county hospitals for readmission after a first township hospitalisation, and the determinants that influenced this choice.<h4>Design</h4>A retrospective study of readmissions across hospitals after a first admission in township hospital. A township-township (TT) inpatient group and a to ...[more]

PMID: 30093517

Similar Datasets

Project description:BackgroundMedicare's Hospital Readmissions Reduction Program reports risk-standardized readmission rates for traditional Medicare but not Medicare Advantage beneficiaries.ObjectiveTo compare readmission rates between Medicare Advantage and traditional Medicare.DesignRetrospective cohort study linking the Medicare Provider Analysis and Review (MedPAR) file with the Healthcare Effectiveness Data and Information Set (HEDIS).Setting4748 U.S. acute care hospitals.PatientsPatients aged 65 years or older hospitalized for acute myocardial infarction (AMI) (n = 841 613), congestive heart failure (CHF) (n = 1 458 652), or pneumonia (n = 2 020 365) between 2011 and 2014.Measurements30-day readmissions.ResultsAmong admissions for AMI, CHF, and pneumonia identified in MedPAR, 29.2%, 38.0%, and 37.2%, respectively, did not have a corresponding record in HEDIS. Of these, 18.9% for AMI, 23.7% for CHF, and 18.3% for pneumonia resulted in a readmission that was identified in MedPAR. However, among index admissions appearing in HEDIS, 14.4% for AMI, 18.4% for CHF, and 13.9% for pneumonia resulted in a readmission. Patients in Medicare Advantage had lower unadjusted readmission rates than those in traditional Medicare for all 3 conditions (16.6% vs. 17.1% for AMI, 21.4% vs. 21.7% for CHF, and 16.3% vs. 16.4% for pneumonia). However, after standardization, patients in Medicare Advantage had higher readmission rates than patients in traditional Medicare for AMI (17.2% vs. 16.9%; difference, 0.3 percentage point [95% CI, 0.1 to 0.5 percentage point]), CHF (21.7% vs. 21.4%; difference, 0.3 percentage point [CI, 0.2 to 0.5 percentage point]), and pneumonia (16.5% vs. 16.0%; difference, 0.5 percentage point [95% CI, 0.4 to 0.6 percentage point]). Rate differences increased between 2011 and 2014.LimitationPotential unobserved differences between populations.ConclusionThe HEDIS data underreported hospital admissions for 3 common medical conditions, and readmission rates were higher among patients with underreported admissions. Medicare Advantage beneficiaries had higher risk-adjusted 30-day readmission rates than traditional Medicare beneficiaries.Primary funding sourceNational Institute on Aging.

Project description:To isolate hospital effects on risk-standardized hospital-readmission rates, we examined readmission outcomes among patients who had multiple admissions for a similar diagnosis at more than one hospital within a given year.We divided the Centers for Medicare and Medicaid Services hospital-wide readmission measure cohort from July 2014 through June 2015 into two random samples. All the patients in the cohort were Medicare recipients who were at least 65 years of age. We used the first sample to calculate the risk-standardized readmission rate within 30 days for each hospital, and we classified hospitals into performance quartiles, with a lower readmission rate indicating better performance (performance-classification sample). The study sample (identified from the second sample) included patients who had two admissions for similar diagnoses at different hospitals that occurred more than 1 month and less than 1 year apart, and we compared the observed readmission rates among patients who had been admitted to hospitals in different performance quartiles.In the performance-classification sample, the median risk-standardized readmission rate was 15.5% (interquartile range, 15.3 to 15.8). The study sample included 37,508 patients who had two admissions for similar diagnoses at a total of 4272 different hospitals. The observed readmission rate was consistently higher among patients admitted to hospitals in a worse-performing quartile than among those admitted to hospitals in a better-performing quartile, but the only significant difference was observed when the patients were admitted to hospitals in which one was in the best-performing quartile and the other was in the worst-performing quartile (absolute difference in readmission rate, 2.0 percentage points; 95% confidence interval, 0.4 to 3.5; P=0.001).When the same patients were admitted with similar diagnoses to hospitals in the best-performing quartile as compared with the worst-performing quartile of hospital readmission performance, there was a significant difference in rates of readmission within 30 days. The findings suggest that hospital quality contributes in part to readmission rates independent of factors involving patients. (Funded by Yale-New Haven Hospital Center for Outcomes Research and Evaluation and others.).

Project description:ObjectivesTo determine whether high performing hospitals with low 30 day risk standardized readmission rates have a lower proportion of readmissions from specific diagnoses and time periods after admission or instead have a similar distribution of readmission diagnoses and timing to lower performing institutions.DesignRetrospective cohort study.SettingMedicare beneficiaries in the United States.ParticipantsPatients aged 65 and older who were readmitted within 30 days after hospital admission for heart failure, acute myocardial infarction, or pneumonia in 2007-09.Main outcome measuresReadmission diagnoses were classified with a modified version of the Centers for Medicare and Medicaid Services' condition categories, and readmission timing was classified by day (0-30) after hospital discharge. Hospital 30 day risk standardized readmission rates over the three years of study were calculated with public reporting methods of the US federal government, and hospitals were categorized with bootstrap analysis as having high, average, or low readmission performance for each index condition. High and low performing hospitals had ≥ 95% probability of having an interval estimate respectively less than or greater than the national 30 day readmission rate over the three year period of study. All remaining hospitals were considered average performers.ResultsFor readmissions in the 30 days after the index admission, there were 320,003 after 1,291,211 admissions for heart failure (4041 hospitals), 102,536 after 517,827 admissions for acute myocardial infarction (2378 hospitals), and 208,438 after 1,135,932 admissions for pneumonia (4283 hospitals). The distribution of readmissions by diagnosis was similar across categories of hospital performance for all three conditions. High performing hospitals had fewer readmissions for all common diagnoses. Median time to readmission was similar by hospital performance for heart failure and acute myocardial infarction, though was 1.4 days longer among high versus low performing hospitals for pneumonia (P<0.001). Findings were unchanged after adjustment for other hospital characteristics potentially associated with readmission patterns.ConclusionsHigh performing hospitals have proportionately fewer 30 day readmissions without differences in readmission diagnoses and timing, suggesting the possible benefit of strategies that lower risk of readmission globally rather than for specific diagnoses or time periods after hospital stay.

Project description:Medicare penalizes hospitals with higher than expected readmission rates by up to 3% of annual inpatient payments. Expected rates are adjusted only for patients' age, sex, discharge diagnosis, and recent diagnoses.To assess the extent to which a comprehensive set of patient characteristics accounts for differences in hospital readmission rates.Using survey data from the nationally representative Health and Retirement Study (HRS) and linked Medicare claims for HRS participants enrolled in Medicare who were hospitalized from 2009 to 2012 (n = 8067 admissions), we assessed 29 patient characteristics from survey data and claims as potential predictors of 30-day readmission when added to standard Medicare adjustments of hospital readmission rates. We then compared the distribution of these characteristics between participants admitted to hospitals with higher vs lower hospital-wide readmission rates reported by Medicare. Finally, we estimated differences in the probability of readmission between these groups of participants before vs after adjusting for the additional patient characteristics.All-cause readmission within 30 days of discharge.Of the additional 29 patient characteristics assessed, 22 significantly predicted readmission beyond standard adjustments, and 17 of these were distributed differently between hospitals in the highest vs lowest quintiles of publicly reported hospital-wide readmission rates (P ? .04 for all comparisons). Almost all of these differences (16 of 17) indicated that participants admitted to hospitals in the highest quintile of readmission rates were more likely to have characteristics that were associated with a higher probability of readmission. The difference in the probability of readmission between participants admitted to hospitals in the highest vs lowest quintile of hospital-wide readmission rates was reduced by 48% from 4.41 percentage points with standard adjustments used by Medicare to 2.29 percentage points after adjustment for all patient characteristics assessed (reduction in difference: -2.12; 95% CI, -3.33 to -0.67; P =?.003).Patient characteristics not included in Medicare's current risk-adjustment methods explained much of the difference in readmission risk between patients admitted to hospitals with higher vs lower readmission rates. Hospitals with high readmission rates may be penalized to a large extent based on the patients they serve.

Project description:ObjectivesDevelop predictive models using an administrative healthcare database that provide information for Patient-Centred Medical Homes to proactively identify patients at risk of hospitalisation for conditions that may be impacted through improved patient care.DesignRetrospective healthcare utilisation analysis with multivariate logistic regression models.DataA population-based longitudinal database of residents served by the Emilia-Romagna, Italy, health service in the years 2004-2012 including demographic information and utilisation of health services by 3,726,380 people aged ≥18 years.Outcome measuresModels designed to predict risk of hospitalisation or death in 2012 for problems that are potentially avoidable were developed and evaluated using the area under the receiver operating curve C-statistic, in terms of their sensitivity, specificity and positive predictive value, and for calibration to assess performance across levels of predicted risk.ResultsAmong the 3,726,380 adult residents of Emilia-Romagna at the end of 2011, 449,163 (12.1%) were hospitalised in 2012; 4.2% were hospitalised for the selected conditions or died in 2012 (3.6% hospitalised, 1.3% died). The C-statistic for predicting 2012 outcomes was 0.856. The model was well calibrated across categories of predicted risk. For those patients in the highest predicted risk decile group, the average predicted risk was 23.9% and the actual prevalence of hospitalisation or death was 24.2%.ConclusionsWe have developed a population-based model using a longitudinal administrative database that identifies the risk of hospitalisation for residents of the Emilia-Romagna region with a level of performance as high as, or higher than, similar models. The results of this model, along with profiles of patients identified as high risk are being provided to the physicians and other healthcare professionals associated with the Patient Centred Medical Homes to aid in planning for care management and interventions that may reduce their patients' likelihood of a preventable, high-cost hospitalisation.

Project description:ObjectiveTo examine the association of hospital volume (a marker of quality of care) with hospital readmission rates.DesignRetrospective cross-sectional study.Setting4651 US acute care hospitals.Study data6,916,644 adult discharges, excluding patients receiving psychiatric or medical cancer treatment.Main outcome measuresWe used Medicare fee-for-service data from 1 July 2011 to 30 June 2012 to calculate observed-to-expected, unplanned, 30 day, standardized readmission rates for hospitals and for specialty cohorts medicine, surgery/gynecology, cardiorespiratory, cardiovascular, and neurology. We assessed the association of hospital volume by quintiles with 30 day, standardized readmission rates, with and without adjustment for hospital characteristics (safety net status, teaching status, geographic region, urban/rural status, nurse to bed ratio, ownership, and cardiac procedure capability. We also examined associations with the composite outcome of 30 day, standardized readmission or mortality rates.ResultsMean 30 day, standardized readmission rate among the fifth of hospitals with the lowest volume was 14.7 (standard deviation 5.3) compared with 15.9 (1.7) among the fifth of hospitals with the highest volume (P<0.001). We observed the same pattern of lower readmission rates in the lowest versus highest volume hospitals in the specialty cohorts for medicine (16.6 v 17.4, P<0.001), cardiorespiratory (18.5 v 20.5, P<0.001), and neurology (13.2 v 14.0, p=0.01) cohorts; the cardiovascular cohort, however, had an inverse association (14.6 v 13.7, P<0.001). These associations remained after adjustment for hospital characteristics except in the cardiovascular cohort, which became non-significant, and the surgery/gynecology cohort, in which the lowest volume fifth of hospitals had significantly higher standardized readmission rates than the highest volume fifth (difference 0.63 percentage points (95% confidence interval 0.10 to 1.17), P=0.02). Mean 30 day, standardized mortality or readmission rate was not significantly different between highest and lowest volume fifths (20.4 v 20.2, P=0.19) and was highest in the middle fifth of hospitals (range 20.6-20.8).ConclusionsStandardized readmission rates are lowest in the lowest volume hospitals-opposite from the typical association of greater hospital volume with better outcomes. This association was independent of hospital characteristics and was only partially attenuated by examining mortality and readmission together. Our findings suggest that readmissions are associated with different aspects of care than mortality or complications.

Dataset Information

Determinants of patient choice for hospital readmission after township hospitalisation: a population-based retrospective study in China.

Publications

Determinants of patient choice for hospital readmission after township hospitalisation: a population-based retrospective study in China.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets