MMI 406 Decision Support Systems and Healthcare Instructor: Pallav Sharda
Description:
This course provides an introduction to decision analysis with an emphasis on medical decision-making and elements of human cognition under uncertainty. Topics covered in the course include structuring decision problems and developing creative decision options, quantifying uncertainty and preferences, and combining them to arrive at optimal decisions. The course also provides the foundation needed to apply the methods of decision analysis in decision support systems and intelligent systems. Students will become familiar with the graphical display of medical information, decision analysis and modeling, evidence-based medicine, Bayes' theorem, knowledge-based systems and data mining. Class meetings may be augmented by presentations from selected subject matter experts.
Student Learning Goals:
Describe current uses of decision-making and decision support systems in health care.
Understand the benefits and limitations of medical decision-making techniques.
Understand the overall use various decision-making and analytic models to solve both
Structured and unstructured problems.
Understand the basic features, benefits, and limitations of machine learning and intelligent
Ddecision support methods in the healthcare environment.
Develop a model for a prototype DSS to address a healthcare problem.
Identify trends and issues related to the use of DSS.
Desired Course Outcomes
Understand the basic features, benefits, and limitations of machine learning and intelligent decision support methods in the healthcare environment.
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Artifacts:
ORDS Scheduler.doc
MED INF 406 - OR Scheduling Project Final.ppt
MED INF 406 - OR Scheduling Project Final3.pdf
Wiki decision support link.doc
Use of references.doc
Informatics Resources.xls
406_Resource_list_Winter 09.doc
The OR Scheduling Project is a white paper that discusses the design, benefits, and use of an automated software scheduler for surgical departments, enabling the creation of more efficient staff and resource schedules. The paper outlines a set of algorithms for generating and improving schedules. The Wiki document was a group assignment to edit Wikipedia content for Clinical Decision Support Systems (CDSS). The document contains a link to the web page of the finished product. The use of references.doc demonstrates medical problem solving using online support resources. . The resource spreadsheet and word document where supplied by Pallav Sharda and I found them to be very useful decision support resources.
Main Take-A-Way
The goal of any decision support system is to present the clinician with pertinent data when it is needed and how it is needed to facilitate diagnosis and treatment. Decision support systems help to provide the evidence for evidence based medicine.
Areas of Strength
Understand the key issues in implementing clinical decision support systems
Have an understanding of the policies needed to implement clinical decision support systems to improve healthcare outcomes
Clear understanding of how modeling uncertainty becomes basis for decision support systems
Can Develop and interpret Bayesian probabilities for healthcare applications
Areas for Development
The need to develop methods for decision support systems to fit seamlessly in the clinician’s workflow. If information is not presented when needed it is not used.
Need clearer understanding of the role of regulation and accountability in clinical decision support systems