Jin Qi Assistant Professor, Depart of Industrial Engineering & Decision Analytics, HKUST
Freshly announced in the budget three weeks ago, a new HK$10 billion stabilization fund has been earmarked to soothe Hong Kong’s manpower-starved public medical sector.
While we welcome the initiative, we can’t help but wonder – if public hospitals are currently short-staffed and new blood requires training time – how we can cope with swamped outpatient clinics during future peak flu seasons?
Media reports last month indicated that every public ward exceeded capacity, with some patients queuing for over eight hours to see a doctor.
The inpatient bed occupancy rates of every hospital, aside from North Lantau and Tin Shui Wai hospitals, exceeded 100 percent almost on a daily basis.
Scarce resources require carefully planned policies to ensure optimal bed allocation and quality services.
This is where industrial engineering and decision analytics comes in.
Once linked to optimizing automated manufacturing systems, computer simulations can be applied to service-based sectors like public health services to determine bottlenecks, identify and test potential solutions.
Hospital inpatients can be divided into two groups – planned (eg, Cesarean delivery) versus unplanned (eg, emergency wards).
Each ward experiences different patient load changes, such as seasonal fluctuations and day-of-week patterns.
Reserving too many emergency arrival beds leads to underutilization, while reserving too few results in overcrowding.
With accurate models/forecasts, we can reserve enough extra beds for emergency wards during peak season without requiring hospitals to reduce the number of scheduled elective surgeries.
I conducted an optimization study in Singapore examining the relationship between elective admissions and unplanned emergency admissions based on this concept when there is a high degree of uncertainty in emergency arrivals and length of stay.
A similar study can be done here using seasonal public hospital admission data in Hong Kong.
Additionally, we can work directly with hospital operation management staff to develop customized simulations to test their suggestions.
The simulation can exhaustively test different scenarios using real patient inflow data to narrow down the best solutions without impacting day-to-day hospital operations.
Once identified, the best solution can then be set up accordingly.
Greater public-private health care collaboration could also be a solution for seasonal crunches like this. The General Outpatient Clinic Public Private Partnership program, launched in mid-2014, aims to encourage such collaboration.
Initially operated in three pilot districts – Kwun Tong, Wong Tai Sin and Tuen Mun – the program has then been extended to all 18 districts for selected patients with chronic and episodic illnesses.
The collaboration could be extended to selected services provided by private hospitals for treating influenza during flu season to reduce the workload in public hospitals.
There is no quick answer to the overcapacity problem and providing more funding alone without identifying and tackling the root of the problem will not help.
However, we can make use of technology for better utilization of resources, communication with the public and cost saving.
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