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Background: Unplanned re-attendance at the Emergency Department (ED) is often monitored as a quality indicator of the care accorded to patients during their index ED visit. High bed occupancy rate (BOR) has been considered as a matter of reduced patient comfort and privacy. Most hospitals in Singapore operate under BORs above 85 %. This study aims to explore factors associated with the unplanned 3-day ED re-attendance rate and, in particular, if higher BOR is associated with higher 3-day unplanned ED re-attendance rate. Methods: This was a multicenter retrospective study using time series data. Three acute tertiary hospitals were selected from all six adult public hospitals in Singapore based on data availability. Daily data from year 2008 to 2013 were collected from the study hospitals' information systems. These included: ED visit date, day of week, month, year, public holiday, daily hospital BOR, daily bed waiting time (BWT) at ED (both median and 95th percentile), daily ED admission rate, and 3-day ED re-attendance rate. The primary outcome of the study was unplanned 3-day ED re-attendance rate from all reasons. Both univariate analysis and generalized linear regression were respectively applied to study the crude and adjusted association between the unplanned 3-day ED reattendance rate and its potential associated factors. All analyses were conducted using SPSS 18 (PASW 18, IBM). Results: The average age of patients who visited ED was 35 years old (SD = 2), 37 years old (SD = 2), and 40 years old (SD = 2) in hospitals A, B, and C respectively. The average 3-day unplanned ED re-attendance rate was 4.9 % (SE = 0.47 %) in hospital A, 3.9 % (SE = 0.35 %) in hospital B, and 4.4 % (SE = 0.30 %) in hospital C. After controlling for other covariates, the unplanned 3-day ED re-attendance rates were significantly associated with hospital, time trend, day of week, daily average BOR, and ED admission rate. Strong day-of-week effect on early ED re-attendance rate was first explored in this study. Thursday had the lowest re-attendance rate, while Sunday has the highest re-attendance rate. The patients who visited at ED on the dates with higher BOR were more likely to re-attend the ED within 3 days for hospitals A and B. There was no significant association between BOR and ED re-attendance rate in hospital C. Conclusions: A study using time series data has been conducted to explore the factors associated with the unplanned 3-day ED re-attendance rate. Strong day-of-week effect was first reported. The association between BOR and the ED re-attendance rate varied with hospital. [ABSTRACT FROM AUTHOR]

Background: Stroke is the 4th leading cause of death and 1st leading cause of disability in Singapore. However the information on long-term post stroke outcomes for Singaporean patients was limited. This study aimed to investigate the post stroke outcomes of 5-year survival and rehospitalization due to stroke recurrence for hemorrhagic and ischemic stroke patients in Singapore. The outcomes were stratified by age, ethnic group, gender and stroke types. The causes of death and stroke recurrence were also explored in the study. Methods: A multi-site retrospective cohort study. Patients admitted for stroke at any of the three hospitals in the National Healthcare Group of Singapore were included in the study. All study patients were followed up to 5 years. Kaplan-Meier was applied to study the time to first event, death or rehospitalization due to stroke recurrence. Cox proportional hazard model was applied to study the time to death with adjustment for stroke type, age, sex, ethnic group, and admission year. Cumulative incidence model with competing risk was applied for comparing the risks of rehospitalization due to stroke recurrence with death as the competing risk. Results: Totally 12,559 stroke patients were included in the study. Among them, 59.3% survived for 5 years; 18.4% were rehospitalized due to stroke recurrence in 5 years. The risk of stroke recurrence and mortality increased with age in all stroke types. Gender, ethnic group and admitting year were not significantly associated with the risk of mortality or stroke recurrence in hemorrhagic stroke. Male or Malay patient had higher risk of stroke recurrence and mortality in ischemic stroke. Hemorrhagic stroke had higher early mortality while ischemic stroke had higher recurrence and late mortality. The top cause of death among died stroke patients was cerebrovascular diseases, followed by pneumonia and ischemic heart diseases. The recurrent stroke was most likely to be the same type as the initial stroke among rehospitalized stroke patients. Conclusions: Five year post-stroke survival and rehospitalization due to stroke recurrence as well as their associations with patient demographics were studied for different stroke types in Singapore. Specific preventive strategies are needed to target the high risk groups to improve their long-term outcomes after acute stroke. [ABSTRACT FROM AUTHOR]

Objective: This study aims to assess the impact of diabetes mellitus (DM) on the health-care utilization and clinical outcomes of patients with acute stroke. Methods: This is a retrospective cohort study. All patients who were admitted for the first time to one of the three public hospitals in the National Healthcare Group in Singapore from January 2005 to June 2007 with a primary diagnosis of acute stroke were included and were followed up for 1 year after the index hospitalization. The study population was divided into two groups: with DM and without DM. Both univariate and multivariate analyses were applied to compare the hospital length of stay (LOS), hospitalization costs, mortality, as well as the 1-year hospital readmissions between the DM and non-DM groups. Results: There were 9766 study patients, and 38.5% of them had DM. DM patients with ischemic stroke (IS) and transient ischemic attack (TIA) stayed 1-day and 0.6-day longer, and incurred 10% and 26% higher hospital cost during index admission, respectively, compared with their counterparts in the non-DM group. They also had more hospital readmission within 1 year. The mortality rate in IS patients with diabetes was 24% higher. After risk adjustment, subarachnoid hemorrhage patients with diabetes had more hospitalizations. Intracerebral hemorrhage (ICH) and IS patients in the DM group had all worse outcomes but the 1-year stroke recurrence; TIA patients with DM incurred longer LOS and hospital costs. Conclusion: DM predicts worse clinical outcomes and higher health-care expenditures in the 1-year poststroke especially for the IS, ICH, and TIA stroke subtypes. [ABSTRACT FROM AUTHOR]

Introduction: Singapore is a developed country with a population of 5.6 million and an acutecentric healthcare system. While the acute hospitals deliver extensive care, patients with multiple care needs face challenges in managing their conditions post-discharge, due to various reasons, such as weak caregiver support. This phenomenon is especially evident in older and frail patients who are frequently readmitted as their condition(s) deteriorate post-discharge. Methods: In April 2017, Singapore's Ministry of Health (MOH) launched a nationwide Hospital to Home (H2H) care model to provide holistic patient-centric care to support patients discharging from public hospitals, so that they can transit back home, and stay well in the community. H2H targets high healthcare utilizers who are frail with complex care needs. The service includes medication reconciliation, case management, care coordination, telephonic support and caregiver training. To facilitate the development and deployment of the predictive model to risk stratify and segment the patients for enrolment assessment into H2H, a workgroup consisting of clinicians and data scientists across the various public hospitals was formed. The dependent variable for the model was three or more non-elective inpatient admissions within a 1-year period. Independent variables, generated based on literature review and input from clinicians and care managers, were segmented into 3 primary categories: sociodemographic, past hospital utilization and past medical conditions. Results: The top important variable was "total LOS in the past 12 months" followed by "number of days from previous non-elective admission" which lifted the model performance significantly. The best performing machine learning algorithm has an AUC of 0.79, recall of 39%, with precision set at 70% after consultation with the H2H clinical committee to capture very high risk patients. The prediction list, generated on daily basis, is now an integrated part of patient assessment workflow. The list helps the care team to support and follow up with the high healthcare utilizers and their caregivers after discharge. As of Oct 2018, more than 23,000 patients had benefited. Conclusions: The predictive model exemplifies the benefit of augmenting prediction model with clinical assessment. By having the prediction tool act as a bigger "sieve" to do the first level of filtering and the subsequent removal of potential false positives by clinicians when they assessed the patient in-ward provides a targeted intervention with minimal clinical resources. Lessons learnt: To manage the multidisciplinary development team in this endeavour efficiently, we adopted an iterative development process that allow us to fail fast and often. To convince clinicians to shift their decision making process to be augmented with a predictive model, we engaged reputable clinicians in the H2H clinical committee and analytics teams from different public hospitals to co-develop the model. Limitations: In this discussion, we had not been able to cover in depth on areas such as comparing the predictive model performance with different set of features. Future research: Future enhancements include the use of Natural Language Processing on unstructured clinical notes to extract care-giver and non-clinical information as additional features for the prediction model. [ABSTRACT FROM AUTHOR]