For many students, OM is an intimidating field of study–the first course that blends mathematical methods with qualitative decisions. For example, queuing theory is clearly mathematical, but choosing which queue structure is often a subjective decision. Today’s hot topics of “big data” and analytics require problem formulation skills, so I begin the OM course with an exercise to promote critical thinking. In particular, I seek to train my students to formulate decision questions in quantitative terms. I pose the question repeatedly, “What are the measurements?”
I motivate this discussion with a news story on health care, specifically, whether a surgically inserted stent or drug therapy is a better choice for patient care. First, I introduce the topic and instruct the students to think carefully about the news story. I ask them: Who are the stakeholders? Who are the decision makers? What are the objectives? What are the measurements? And finally: What defines success? I then play the news story audio (which is found here).
Almost invariably, initial student answers will be vague, such as “improve the quality of health care” or “provide high quality care.” But drill the students to focus on tangible measures. Ask: “How do I measure that?” After some dialogue, you should reach agreement on more precise measures such as life expectancy, death in surgery, cost of treatment, cost of drug, duration of treatment, etc.
The news story presents this question as an argument between a Dr. Teirstein and a Dr. Topol, but after working through these discussion questions, you will find the real conflict between these doctors is over the choice of objectives and measures. Dr. Topol’s primary objective is lower cost, but Dr. Teirstein’s is fast treatment. This discussion is in fact a prelude to future topics in the course. In so many areas of OM, cost and speed are fundamental tradeoffs. No where is this more evident than in the study of queuing theory (Module D). Thus the debate over healthcare is at its root a debate over the balance between fundamentally opposed performance measures.
A detailed teaching note for this lesson is available here.