Improving stroke outcomes through operational policies.
Dr. Beste Kucukyazici from MIT-Zaragoza International Logistics Program, will be giving a talk based on her research in health care.
Summary of discussion topic
Hospital provision for accommodating increasing numbers of admissions is a matter of considerable public and political concern and has been the subject of widespread debate. Many different services are needed to treat different types of patients during the care given in the hospital, and the coordination of these multi-level services among different patient types is often a formidable challenge. Failure in matching the hospital’s service capacity (e.g., the number of properly staffed beds) and the patients’ demand for certain levels of care can be problematic. Moreover, day to day fluctuations in demand affect the efficient allocation of the capacity and hospital efficiency, accordingly. As a result; the patients may not receive care from the appropriate professional at the appropriate time and place. The key issue for matching the demand and service capacity and improving the performance of the hospitals is intelligently designed operational policies, i.e. capacity allocation and patient admission policies.
This talk focuses on the impact of inadequate capacity on patient outcomes, and improving these outcomes through operational policies, i.e. identifying appropriate capacity levels as well as patient admission and bed allocation policies. To this end, we first present an empirical study to establish the potential impact of delays in accessing to care due to in-hospital capacity on inpatient operations and health outcomes. Second, we present a combination of analytical and simulation models of the in-hospital care process. The models are used not only to validate our empirical findings in a more comprehensive environment but also to evaluate the current admissions and bed allocation policies as well as to explore alternative policies that might decrease delays and hence improve clinical outcomes. Since the results of our simulation model show significant improvements to switch from a static bed allocation to a dynamic policy, finally, we discuss on our optimization model (i.e. stochastic dynamic programming model) for dynamic patient admission policies.