Research Projects
Optimisation In Hong Kong Public Health Service Using Big Data Research


Programme(s) to which this project applies:

☒ MPhil/PhD ☑ MRes[Med] ☒ URIS
About the Project

Objectives:

  1. Evaluate the effectiveness of different follow-up frequencies of doctor consultation for patients with NCD.
  2. Evaluate the outcomes between patients with stable health conditions referred to GOPC and SOPC from hospital inpatient or A&E service.
  3. Develop and validate risk prediction model for risk of unnecessary A&E attendance including semi-urgent and non-urgent cases of A&E attendance and discharged patients with avoidable readmission.

Significance:

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.

About the Supervisor

Dr EYF Wan, Department of Family Medicine and Primary Care

Biography
yfwan@hku.hk

Next Step?

For more information or to express interest for this project, please email the supervisor or the specified contact point in the project description.  Interested candidates are advised to enclose with your email:

  1. your CV,
  2. a brief description of your research interest and experience, and
  3. two reference letters (not required for HKUMed UG students seeking MRes[Med]/URIS projects).

Information on the research programme, funding support and admission documentations could be referenced online at the Research Postgraduate Admissions website. General admission enquiries should be directed to rpgmed@hku.hk.

HKUMed MBBS students interested in the Master of Research in Medicine (MRes[Med]) programme may visit the programme website for more information.  

HKUMed UG students interested in the Undergraduate Research Internship Scheme (URIS) may visit the scheme’s website for more information.