Decision Making in Health Care
Introduces decision sciences in health care, covering medical decision making tools like decision trees, sensitivity analysis, Markov processes, and Monte Carlo simulations.
Summary
Past student reviews · Oguzhan Alagoz
Students found the course manageable, noting that Alagoz did not make the material more difficult than necessary.
What you’ll be able to do
Explain how decision sciences are used in the health-care industry
Describe concepts related to medical decision making and cost-effectiveness analysis
Apply technical skills in decision analysis including the creation and evaluation of decision trees, the use of sensitivity analysis, and the use of Markov processes and Monte Carlo simulation
Incorporate specific patient preferences into medical decision models through the use of utility analysis
Identify and apply an advanced decision analytical modeling framework to solve medical decision making problems
Source: UW Guide
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course map(STAT/MATH 309, 409, 431,STAT 311,MATH 331) and (I SY E 323 or E C E/COMP SCI/I SY E 524), graduate/professional standing, or member of Engineering Guest Students
- take one
- take one
- graduate/professional standing
- member of Engineering Guest Students
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Any of
- All of
- graduate/professional standing
- member of Engineering Guest Students
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
OGUZHAN ALAGOZ is recorded teaching in Spring 2011, Fall 2013, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Fall 2020, Fall 2021, Fall 2022, Fall 2023, Fall 2024, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
Student experience
the class
Past student reviews · Oguzhan Alagoz
Oguzhan Alagoz taught with visible passion, making a personal effort to learn every student's name and fostering a supportive classroom environment.
Recent recorded grades — Fall 2023: 3.59 GPA, 81.8% A/AB (n=33 letter grades); Fall 2024: 3.64 GPA, 75.0% A/AB (n=32 letter grades); Fall 2025: 3.57 GPA, 69.0% A/AB (n=29 letter grades).
difficulty & workload
Past student reviews · Oguzhan Alagoz
Students found the course manageable, noting that Alagoz did not make the material more difficult than necessary.
Past student reviews · Oguzhan Alagoz
Reviews highlighted interesting topics and great examples connected to the instructor's professional experience, which students found engaging.
Topics
- Medical decision making, decision trees, sensitivity analysis, Markov processes, and Monte Carlo simulations.
- Cost-effectiveness analysis in medical contexts.
- Utility analysis for incorporating patient preferences.
Skills
- Creation and evaluation of decision trees, sensitivity analysis, Markov processes, and Monte Carlo simulation.
- Incorporating patient preferences into medical decision models using utility analysis.
- Applying advanced decision analytical modeling frameworks to solve medical decision-making problems.
Grades
Latest available · Fall 2025— not enough history to project Spring 2027.
Grade distribution · % of letter grades
Grades over time
Through Fall 2025
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
Sources & history
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
Observations at scan time; dates do not imply when a catalog change took effect.
Selected offering source records
No offering records for the selected term.
Raw records
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Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
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"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "2de14c767c3ae13ed6e8bfe833777e84be1e491269049cb874337ea7a907da1e",
"requirements_status": "needs_review",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}