Improving Patient flow in Acute Care

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***In dealing with health care managerial problems, we are often forced to compartmentalize the health system in order to simplify the challenges into a tractable model. This compartmentalization fails to take into account the complexity involved in patient flow through acute care. The success of approximate dynamic programming (ADP) in solving large scale sequential decision problems means that some of that compartmentalization is no longer necessary. The first objective of this research is to merge some of the traditional compartments in OR models in health in order to include the highly dependent nature of these decisions. We begin with a model that incorporates the advanced scheduling and appointment scheduling problems. These problems have traditionally been dealt with in isolation but are in truth highly dependent with the input of the appointment scheduling problem being the output from an advanced scheduling model and the performance of the advanced scheduling model depending on the form of the appointment schedule. We will then increase the complexity of this problem in two ways: first by considering multiple resources consumed in sequence (with specific reference to surgical scheduling where patients consume operating room time followed by recovery time in a ward) and second by incorporating capacity decisions. However even this involves significant compartmentalization as the surgical schedule impacts not only surgical patients but also other patients admitted through the emergency department as both types of patients compete for the same beds. We will thus look to build predictive models that can predict the impact on hospital congestion of implementing the policies derived from the ADP models for surgical scheduling. Finally, the impact of these models is significantly hampered by any delays in discharging patients from the hospital due to a lack of capacity in the community. We will thus continue to explore the capacity planning models for the network of community services that ensure timely discharge from the hospital. Thus our proposal has a three pronged objective:***1) Build a model that combines the advanced scheduling and appointment scheduling problems into one model and that can handle multiple resources consumed in sequence as well as capacity planning decisions.***2) Build a model to predict congestion within a hospital and help assess the output of the model from objective 1.***3) Build a capacity planning model for community care services (queuing theory and optimization) in order to maintain proper flow out of acute care.***Thus, the proposed research will develop a series of interconnected models that together allow a health authority to determine the necessary capacity along the entire care pathway from entry into acute care through to discharge to community services while also providing intelligent scheduling policies that ensure the timeliness of patient service.**