I am developing a great planning method to help hospitals in Indonesia use their operating rooms more efficiently.
Oki Almas Amalia (from Indonesia) came to the Netherlands in 2017 to attend the University of Twente. She decided to do so after a thorough internet search for universities that offered programmes related to healthcare logistics.
The decision to study Healthcare Logistics within the Applied Mathematics master's programme at UT.
During a workshop, I got to know Professor Boucherie. He is a co-founder and co-chair of the well-established Center for Healthcare Operations Improvement and Research (CHOIR) (www.utwente.nl/en/choir/). CHOIR is a research group at the University of Twente whose mission is to help healthcare institutions improve the efficiency, quality of care and service, and quality of labour through redesigning and optimizing their processes. After visiting CHOIR's website, I knew for sure that I wanted to apply for the Applied Mathematics master's programme and choose the Stochastic Operations Research (SOR) group. My graduation project was on scheduling semi-urgent surgeries in the emergency operating theatre with daily supervisor Maartje van de Vrugt, who was a postdoctoral researcher in SOR and also a UT-AM alumna.
Logistic planning experts are in great shortage in Indonesia.
In hospitals in Yogyakarta, Indonesia, it is normal for doctors to take part in managerial work, for example in capacity planning. This sometimes causes problems as decisions are mostly made based on their intuitions. It would be much more convenient to have a logistics planning expert to do this work. It would benefit not only the hospitals but also the patients. I aspire to apply the research on healthcare logistics in Indonesia.
I grabbed the opportunity to pursue my PhD in Professor Boucherie's research group with both hands to dive deeper into this research field. During my PhD research, I am working partly at UT and partly back in Yogyakarta, Indonesia, under the joint degree programme between UT and Universitas Gadjah Mada.
This enables me to consult with Indonesian healthcare personnel on the spot and incorporate their concerns and feedback into my research. Professor Boucherie visits Indonesia once a year. This way, we can discuss my progress in my research in person and he can give workshops at the university to get more Indonesian students interested in logistics planning through Applied Mathematics.
Scheduling surgeries with different priorities in dedicated operating room capacities.
In hospitals, patients who need treatment or surgery are often categorised based on their urgency levels, each with its own due date. In our work, we consider surgeries of two urgency levels: urgent surgery, which must start within 6 hours, and semi-urgent surgery, which must begin within 24 hours.
Scheduling urgent and semi-urgent patients is non-trivial, as patients have multiple priority levels and thus different due date targets. The scheduler continuously balances between reserving capacity for urgent patients potentially left idle and meeting the due dates for all patients. We present two policies to schedule urgent and semi-urgent patients in the dedicated operating room (OR) capacity: last-minute scheduling and near-online scheduling. Under last-minute scheduling, the planner assigns capacity to the waiting patients just before the start of the time slot in which the patient will be treated. Under near-online scheduling, the scheduler assigns a future time slot to the patient shortly after the patient's arrival. We developed a Markov decision process (MDP) for both scheduling policies. We also propose two heuristics based on the policy easily applied by hospitals. In the first heuristic, we use all available capacity to schedule patients from the highest urgency level. In the second heuristic, we allow reserving capacity to anticipate urgent patient arrivals in the subsequent time slot. For the time-varying patient arrivals, we use nonstationary MDP, where we evaluate the performance of the optimal policies using discrete event simulation and compare it to the policies from the heuristics. We demonstrate the usefulness of our method by applying it to the case of our partnering hospital.
I am looking forward to developing useful models not only to schedule surgeries but also to provide insights into solving other challenges in healthcare logistics management.
