Quantitative modelling for health care policy planning and resource allocation

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My research focus is on demand and capacity modeling and resource allocation issues in the health care industry. I am particularly concerned about the tendency in health care to work in independent silos of activity. For example, decision makers construct schedules for the operating rooms with little or no consideration of the impact this has on recovery rooms and wards. Hospitals make operational decisions independent from nursing homes, home care and the community including primary care physicians. It is little wonder that the system is inefficient and cumbersome. How should limited financial resources be distributed? How should scarce human resources be allocated? My research focuses primarily on the issues of care coordination across the healthcare system. I have been working to provide decision makers at all levels (hospital, institution, health region, government, etc.) with the quantitative tools that they require to support data-driven decision making and capacity planning. The primary projects that will be supported by this grant will revolve around strategic healthcare decision making at the policy and system level. In particular, we have developed a model for providing capacity for seniors to stay in the homes that reflects the impact on the whole system from hospitals, rehab and long term care. The model allows (Ontario) policy makers to evaluate the potential impact of proposed policy initiatives. We plan to extend this to primary care design and to mental health sectors. Our current research includes models to help planners determine the number of clinical workers that Canada will require in the next 10-20 years. We have done this for cardiac surgeons, and have done some work on orthopaedic surgeons and family physicians. The research will involve evaluating different "models of care" where patients are treated by clinical teams instead of always by a physician. These efforts are essential to build a sustainable system into the future. Finally, we will develop models to understand the real capacity that hospitals require to function effectively.