Dataset Information

The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns.

ABSTRACT: BACKGROUND:High-risk patients are most vulnerable during transitions of care. Due to the high burden of resource allocation for such patients, we propose that segmentation of this heterogeneous population into distinct subgroups will enable improved healthcare resource planning. In this study, we segmented a high-risk population with the aim to identify and characterize a patient subgroup with the highest 30-day and 90-day hospital readmission and mortality. METHODS:We extracted data from our transitional care program (TCP), a Hospital-to-Home program launched by the Singapore Ministry of Health, from June to November 2018. Latent class analysis (LCA) was used to determine the optimal number and characteristics of latent subgroups, assessed based on model fit and clinical interpretability. Regression analysis was performed to assess the association of class membership on 30- and 90-day all-cause readmission and mortality. RESULTS:Among 752 patients, a 3-class best fit model was selected: Class 1 "Frail, cognitively impaired and physically dependent", Class 2 "Pre-frail, but largely physically independent" and Class 3 "Physically independent". The 3 classes have distinct demographics, medical and socioeconomic characteristics (p?

SUBMITTER: Ng SCW

PROVIDER: S-EPMC6894210 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns.

Ng Shawn Choon Wee SCW Kwan Yu Heng YH Yan Shi S Tan Chuen Seng CS Low Lian Leng LL

BMC health services research 20191204 1

<h4>Background</h4>High-risk patients are most vulnerable during transitions of care. Due to the high burden of resource allocation for such patients, we propose that segmentation of this heterogeneous population into distinct subgroups will enable improved healthcare resource planning. In this study, we segmented a high-risk population with the aim to identify and characterize a patient subgroup with the highest 30-day and 90-day hospital readmission and mortality.<h4>Methods</h4>We extracted d ...[more]

PMID: 31801537

Similar Datasets

Project description:BackgroundHeterogeneity of population health needs and the resultant difficulty in health care resources planning are challenges faced by primary care systems globally. To address this challenge in population health management, it is critical to have a better understanding of primary care utilizers' heterogeneous health profiles. We aimed to segment a population of primary care utilizers into classes with unique disease patterns, and to report the 1 year follow up healthcare utilizations and all-cause mortality across the classes.MethodsUsing de-identified administrative data, we included all adult Singapore citizens or permanent residents who utilized Singapore Health Services (SingHealth) primary care services in 2012. Latent class analysis was used to identify patient subgroups having unique disease patterns in the population. The models were assessed by Bayesian Information Criterion and clinical interpretability. We compared healthcare utilizations in 2013 and one-year all-cause mortality across classes and performed regression analysis to assess predictive ability of class membership on healthcare utilizations and mortality.ResultsWe included 100,747 patients in total. The best model (k = 6) revealed the following classes of patients: Class 1 "Relatively healthy" (n = 58,213), Class 2 "Stable metabolic disease" (n = 26,309), Class 3 "Metabolic disease with vascular complications" (n = 2964), Class 4 "High respiratory disease burden" (n = 1104), Class 5 "High metabolic disease without complication" (n = 11,122), and Class 6 "Metabolic disease with multi-organ complication" (n = 1035). The six derived classes had different disease patterns in 2012 and 1 year follow up healthcare utilizations and mortality in 2013. "Metabolic disease with multiple organ complications" class had the highest healthcare utilization (e.g. incidence rate ratio = 19.68 for hospital admissions) and highest one-year all-cause mortality (hazard ratio = 27.97).ConclusionsPrimary care utilizers are heterogeneous and can be segmented by latent class analysis into classes with unique disease patterns, healthcare utilizations and all-cause mortality. This information is critical to population level health resource planning and population health policy formulation.

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:ImportanceThe Affordable Care Act has led to US national reductions in hospital 30-day readmission rates for heart failure (HF), acute myocardial infarction (AMI), and pneumonia. Whether readmission reductions have had the unintended consequence of increasing mortality after hospitalization is unknown.ObjectiveTo examine the correlation of paired trends in hospital 30-day readmission rates and hospital 30-day mortality rates after discharge.Design, setting, and participantsRetrospective study of Medicare fee-for-service beneficiaries aged 65 years or older hospitalized with HF, AMI, or pneumonia from January 1, 2008, through December 31, 2014.ExposureThirty-day risk-adjusted readmission rate (RARR).Main outcomes and measuresThirty-day RARRs and 30-day risk-adjusted mortality rates (RAMRs) after discharge were calculated for each condition in each month at each hospital in 2008 through 2014. Monthly trends in each hospital's 30-day RARRs and 30-day RAMRs after discharge were examined for each condition. The weighted Pearson correlation coefficient was calculated for hospitals' paired monthly trends in 30-day RARRs and 30-day RAMRs after discharge for each condition.ResultsIn 2008 through 2014, 2 962 554 hospitalizations for HF, 1 229 939 for AMI, and 2 544 530 for pneumonia were identified at 5016, 4772, and 5057 hospitals, respectively. In January 2008, mean hospital 30-day RARRs and 30-day RAMRs after discharge were 24.6% and 8.4% for HF, 19.3% and 7.6% for AMI, and 18.3% and 8.5% for pneumonia. Hospital 30-day RARRs declined in the aggregate across hospitals from 2008 through 2014; monthly changes in RARRs were -0.053% (95% CI, -0.055% to -0.051%) for HF, -0.044% (95% CI, -0.047% to -0.041%) for AMI, and -0.033% (95% CI, -0.035% to -0.031%) for pneumonia. In contrast, monthly aggregate changes across hospitals in hospital 30-day RAMRs after discharge varied by condition: HF, 0.008% (95% CI, 0.007% to 0.010%); AMI, -0.003% (95% CI, -0.005% to -0.001%); and pneumonia, 0.001% (95% CI, -0.001% to 0.003%). However, correlation coefficients in hospitals' paired monthly changes in 30-day RARRs and 30-day RAMRs after discharge were weakly positive: HF, 0.066 (95% CI, 0.036 to 0.096); AMI, 0.067 (95% CI, 0.027 to 0.106); and pneumonia, 0.108 (95% CI, 0.079 to 0.137). Findings were similar in secondary analyses, including with alternate definitions of hospital mortality.Conclusions and relevanceAmong Medicare fee-for-service beneficiaries hospitalized for heart failure, acute myocardial infarction, or pneumonia, reductions in hospital 30-day readmission rates were weakly but significantly correlated with reductions in hospital 30-day mortality rates after discharge. These findings do not support increasing postdischarge mortality related to reducing hospital readmissions.

Project description:ImportanceHospital readmissions contribute to higher expenditures and may sometimes reflect suboptimal patient care. Individuals discharged against medical advice (AMA) are a vulnerable patient population and may have higher risk for readmission.ObjectivesTo determine odds of readmission and mortality for patients discharged AMA vs all others, to characterize patient and hospital-level factors associated with readmissions, and to quantify their overall cost burden.Design, setting, and participantsNationally representative, all-payer cohort study using the 2014 National Readmissions Database. Eligible index admissions were nonobstetrical/newborn hospitalizations for patients 18 years and older discharged between January 2014 and November 2014. Admissions were excluded if there was a missing primary diagnosis, discharge disposition, length of stay, or if the patient died during that hospitalization. Data were analyzed between January 2018 and June 2018.ExposuresDischarge AMA and non-AMA discharge.Main outcomes and measuresThirty-day all-cause readmission and in-hospital mortality rate.ResultsThere were 19.9 million weighted index admissions, of which 1.5% resulted in an AMA discharge. Within the AMA cohort, 85% were younger than 65 years, 63% were male, 55% had Medicaid or other (including uninsured) coverage, and 39% were in the lowest income quartile. Thirty-day all-cause readmission was 21.0% vs 11.9% for AMA vs non-AMA discharge (P < .001), and 30-day in-hospital mortality was 2.5% vs 5.6% (P < .001), respectively. Individuals discharged AMA were more likely to be readmitted to a different hospital compared with non-AMA patients (43.0% vs 23.9%; P < .001). Of all 30-day readmissions, 19.0% occurred within the first day after AMA discharge vs 6.1% for non-AMA patients (P < .001). On multivariable regression, AMA discharge was associated with a 2.01 (95% CI, 1.97-2.05) increased adjusted odds of readmission and a 0.80 (95% CI, 0.74-0.87) decreased adjusted odds of in-hospital mortality compared with non-AMA discharge. Nationwide readmissions after AMA discharge accounted for more than 400 000 inpatient hospitalization days at a total cost of $822 million in 2014.Conclusions and relevanceIndividuals discharged AMA have higher odds of 30-day readmission at significant cost to the health care system and lower in-hospital mortality rates compared with non-AMA patients. Patients discharged AMA are also more likely to be readmitted to different hospitals and to have earlier bounce-back readmissions, which may reflect dissatisfaction with their initial episode of care.

Dataset Information

The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns.

Publications

The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets