Impact of Inpatient Care in Emergency Department on Outcomes: A Quasi-Experimental Cohort Study

BACKGROUND: Hospitals around the world are faced with the issue of boarders in emergency department (ED), patients marked for admission but with no available inpatient bed. Boarder status is known to be associated with delayed inpatient care and suboptimal outcomes. A new care delivery system was developed in our institution where boarders received full inpatient care from a designated medical team, acute medical team (AMT), while still residing at ED. The current study examines the impact of this AMT intervention on patient outcomes. METHODS: We conducted a retrospective quasi-experimental cohort study to analyze outcomes between the AMT intervention and conventional care in a 1250-bed acute care tertiary academic hospital in Singapore. Study participants included patients who received care from the AMT, a matched cohort of patients admitted directly to inpatient wards (non-AMT) and a sample of patients prior to the intervention (pre-AMT group). Primary outcomes were length of hospital stay (LOS), early discharges (within 24 h) and bed placement. Secondary outcomes included unplanned readmissions within 3 months, and patient’s bill size. χ2- and Mann-Whitney U tests were used to test for differences between the cohorts on dichotomous and continuous variables respectively. RESULTS: The sample comprised of 2279 patients (1092 in AMT, 1027 in non-AMT, and 160 in pre-AMT groups). Higher rates of early discharge (without significant differences in the readmission rates) and shorter LOS were noted for the AMT patients. They were also more likely to be admitted into a ward allocated to their discipline and had lower bill size compared to non AMT patients. CONCLUSIONS: The AMT intervention improved patient outcomes and resource utilization. This model was noted to be sustainable and provides a potential solution for hospitals’ ED boarders who face a gap in inpatient care during their crucial first few hours of admissions while waiting for an inpatient bed

The Singapore national precision medicine strategy

Precision medicine promises to transform healthcare for groups and individuals through early disease detection, refining diagnoses and tailoring treatments. Analysis of large-scale genomic-phenotypic databases is a critical enabler of precision medicine. Although Asia is home to 60% of the world's population, many Asian ancestries are under-represented in existing databases, leading to missed opportunities for new discoveries, particularly for diseases most relevant for these populations. The Singapore National Precision Medicine initiative is a whole-of-government 10-year initiative aiming to generate precision medicine data of up to one million individuals, integrating genomic, lifestyle, health, social and environmental data. Beyond technologies, routine adoption of precision medicine in clinical practice requires social, ethical, legal and regulatory barriers to be addressed. Identifying driver use cases in which precision medicine results in standardized changes to clinical workflows or improvements in population health, coupled with health economic analysis to demonstrate value-based healthcare, is a vital prerequisite for responsible health system adoption.Agency for Science, Technology and Research (A*STAR)Ministry of Health (MOH)National Medical Research Council (NMRC)National Research Foundation (NRF)We thank all investigators, staf members and study participants of the contributing cohorts and studies: (1) the HELIOS study at the Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; (2) the GUSTO study jointly hosted by the National University Hospital, KK Women’s and Children’s Hospital, the National University of Singapore and the Singapore Institute for Clinical Sciences, the Agency for Science Technology and Research (A*STAR); (3) the SEED cohort at the Singapore Eye Research Institute; (4) the MEC, National University of Singapore; (5) the PRISM cohort; and (6) the TTSH Personalised Medicine Normal Controls cohort. We also thank the National Supercomputing Centre, Singapore (https://www.ncss.sg) for computation resources. The SG10K_Health project is funded by the Industry Alignment Fund (Pre-Positioning) (IAF-PP, H17/01/a0/007); the project made use of participating study cohorts supported by the following funding sources: (1) the HELIOS study by grants from a Strategic Initiative at Lee Kong Chian School of Medicine, the Singapore MOH under its Singapore Translational Research Investigator Award (NMRC/STaR/0028/2017) and the IAF-PP (H18/01/a0/016); (2) the GUSTO study by the Singapore National Research Foundation under its Translational and Clinical Research Flagship Program and administered by the Singapore MOH’s National Medical Research Council Singapore (NMRC/TCR/004-NUS/2008, NMRC/ TCR/012-NUHS/2014) with additional funding support available through the A*STAR and the IAF-PP (H17/01/a0/005); (3) the SEED study by NMRC/CIRG/1417/2015, NMRC/CIRG/1488/2018 and NMRC/OFLCG/004/2018; (4) the MEC by individual research and clinical scientist award schemes from the Singapore National Medical Research Council (including MOH-000271-00) and the Singapore Biomedical Research Council, the Singapore MOH, the National University of Singapore and the Singapore National University Health System; (5) the PRISM cohort study by NMRC/CG/ M006/2017_NHCS, NMRC/STaR/0011/2012, NMRC/STaR/0026/2015, the Lee Foundation and the Tanoto Foundation; and (6) the TTSH cohort study by NMRC/CG12AUG2017 and CGAug16M012. This research is also supported by the National Research Foundation Singapore under its NPM program Phase II funding (MOH-000588) and administered by the Singapore MOH’s National Medical Research Council