Programme(s) to which this project applies:
|☒ MPhil/PhD||☑ MRes[Med]||☒ URIS|
The findings of this research proposal can provide empirical evidence to inform all health care stakeholders on how to optimize the use of public health services so that we can get the best health outcomes from available public resources. The GOPC can serve as the proper gatekeeper of hospital services and provide quality continuous of care of patients with stable conditions; the A&E service can concentrate on urgent and emergency care, and only seriously ill patients need to be admitted to the hospital.
Research Plan and Methodology:
The cohort will include all patients who had ever used inpatient, specialist or primary outpatient clinics or A&E services inA&E services inA&E services inA&E services inA&E services in A&E services inA&E services in A&E services inA&E services in the Hospital Authority (HA) between 2006 and 2018 identified from the clinical management system (CMS) database in hospital authority.
For objective 1: Emulation randomized controlled trial will be used to evaluate the effectiveness of different follow-up period of doctor consultation (1-2, 3-5, 6-12 months) for patients with NCD. Based on the results, the simulation model will be developed to project the percentage of unoccupied capacity in ambulatory care from optimized follow up period for patients with NCD in order to determine the rate of semi-urgent and non-urgent cases of A&E attendance, and discharged patients with avoidable readmission that can be absorbed by ambulatory care. For example, if the simulation model projected that the strategy can free up 10% of capacity in ambulatory care from for patients with NCD, which may be equal to around 500,000 GOPC attendance and 200,000 SOPC attendance, then these capacities may absorb at least half of semi-urgent and non-urgent cases of A&E attendance, and discharged patients with avoidable readmission.
For objective 2: Propensity score matching will be conducted to obtain the balance comparison groups. Then, Poisson or negative binomial regression will be exploited to compare the frequency of A&E service attendance between referral to GOPC and SOPC during discharge from inpatient hospital or A&E service.
For objective 3: Both conventional method (e.g. Cox regression model) and artificial intelligence analysis (e.g. random survival forest analysis or deep neural network) will be used to predict the risk of CVD, mortality and A&E service attendance.
Dr EYF Wan, Department of Family Medicine and Primary Care
Dr Eric Wan is an experienced epidemiologist and medical statistician in health and health service research related to non-communicable disease using big data cohort study. He has published more than 150 articles in peer-reviewed international journal, in particular the evaluation of effectiveness and cost-effectiveness of various health interventions and services, and epidemiology and pharmacoepidemiology in hypertension, diabetes and multi-morbidity.
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