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• Information Fusion and Complex Event Detection
• Efficient Computation of Social Network Metrics
• Optimal Resource Allocation for Spacial Analysis
• Causal Inference with Observational Data
• Social Network Analysis of Online Smoking Cessation Communities
• Stochastic Modeling of Hospital Readmission Process
• IE 374: Systems Modeling and Optimization: Operations Research II
• IE 575: Stochastic Methods
• IE 411/511: Social Network Behavior Analysis
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Stochastic Modeling of Hospital Readmission Process
•Description
•Personnel
| Description |
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High readmission rates of patients after their hospital discharge is a serious concern
for the U.S. healthcare system, negatively impacting the wellbeing of people,
especially the increasing population of elderly citizens.
In the existing literature on quantitative (mostly Finite State Machine and Markov Chain)
modeling of transitions of disease stages, a recognized shortcoming is the inability to accurately
model conditional transition probabilities assuming the interdependence of patient health states,
attributes, decisions and timing, under uncertainty.
This project addresses the challenge of reducing hospital readmission
rates through the structured, educated Care Transition program design and implementation by using
stochastic models capable of capturing the dynamics of patient health status.
The project identifies quantitative models best-suited for the assessment and analysis
of Care Transition program procedures, incorporating the effects of multiple intervention
steps and the uncertainties in patient decision-making and health progression, and creates
decision-support tools that can conduct the continuous assessment of the program outcome,
depending on the level of patient commitment, providing feedback to the patients,
care providers and insurance payers, to better inform all the stakeholders.
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| Personnel |
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Collaborators: Dr. Li Lin (Industrial Engineering, UB),
Dr. Manish Shah (School of Medicine, University of Rochester),
Dr. Suzanne Gillespie (Monroe Community Hospital)
Students: Sabrina Casucci (Ph.D.)
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