Real-time hospital bed occupancy and requirements forecasting
Collaborative partners: Nanyang Technological University and Tan Tock Seng Hospital, Singapore.
Approach: Markov modelling, reinforcement learning
Data: Tan Tock Seng Hospital, Singapore.
Abstract: Healthcare resource planners need to develop policies that ensure optimal allocation of scarce healthcare resources. This goal can be achieved by forecasting daily resource requirements for a given admission policy. If resources are limited, admission should be scheduled according to the resource availability. Such resource availability or demand can change with time. We here model patient flow through the care system as a discrete time Markov chain. In order to have a more realistic representation, a non-homogeneous model is developed which incorporates time-dependent covariates, namely a patient’s present age and the present calendar year. However, these models uses historical data to estimate parameter valuse to be used in the model. These parameter values may not always represent the present scenario. As the healthcare system is a very dynamic and complex system it is necessary to continuously assess and update the parameter values to correctly represent the present scenario. We would enhance models developed earlier to facilitate real-time assessment and corrections of parameter values to well reflect and accommodate changes in the scenario.
References:
Approach: Markov modelling, reinforcement learning
Data: Tan Tock Seng Hospital, Singapore.
Abstract: Healthcare resource planners need to develop policies that ensure optimal allocation of scarce healthcare resources. This goal can be achieved by forecasting daily resource requirements for a given admission policy. If resources are limited, admission should be scheduled according to the resource availability. Such resource availability or demand can change with time. We here model patient flow through the care system as a discrete time Markov chain. In order to have a more realistic representation, a non-homogeneous model is developed which incorporates time-dependent covariates, namely a patient’s present age and the present calendar year. However, these models uses historical data to estimate parameter valuse to be used in the model. These parameter values may not always represent the present scenario. As the healthcare system is a very dynamic and complex system it is necessary to continuously assess and update the parameter values to correctly represent the present scenario. We would enhance models developed earlier to facilitate real-time assessment and corrections of parameter values to well reflect and accommodate changes in the scenario.
References:
- Garg L, McClean SI, Meenan BJ, Millard PH (2010). A non-homogeneous discrete time Markov model for admission scheduling and resource planning in a care system. Health Care Management Science. 13(2):155–169.
- Garg L, McClean SI, Meenan BJ, Millard PH (2009). Non-homogeneous Markov Models for Sequential Pattern Mining of Healthcare Data. IMA journal Management Mathematics. 20(4): 327-344.
- Garg L, McClean SI, Meenan BJ, Barton M, Fullerton K (2012). Intelligent patient management and resource planning for complex, heterogeneous and stochastic healthcare systems. In press. IEEE Transactions on Systems, Man, and Cybernetics--Part A: Systems and Humans.
Skills required
Soft skills: Strong analytical and problem solving skills and fast learning abilities, reliable, responsible, hard working, enthusiasm and determination to learn and acquire new skills.
Software Skills: Good programming skills in a language of your choice is highly desirable but not required.
Soft skills: Strong analytical and problem solving skills and fast learning abilities, reliable, responsible, hard working, enthusiasm and determination to learn and acquire new skills.
Software Skills: Good programming skills in a language of your choice is highly desirable but not required.