DRAFT: This module has unpublished changes.

Health Economics and Quantitative Methods


Key Competencies:


1. Health Care Knowledge: Health care issues and trends, population health and status assessment, and economic concepts and their application to the health care field
2. Business Skills and Knowledge: Skills in conducting economic analysis, strategic planning, and quantitative skills
3. Communication and Relationship Management: Interpersonal communication, writing skills, and working in teams




Health Economics and Quantitative Methods_syllabus.pdf




This course was broken down into two sections focused on 1) health economic concepts and 2) quantitative methods.  Overall we were tasked with understanding economic concepts and developing skills to apply those concepts to high level decision making. Further we sought to understand reimbursement methods and cost benefit analysis. As economics tied into the quantitative methods section, we were able to make important connections between the two.  We also learned how to manipulate a data set in order to derive relevant conclusions about the data.


Healthcare Economics


Dr. Bartlett immersed us in health economics and described what makes health economics different.  In particular, Dr. Bartlett described where microeconomics and health economics intersect at five specific points:

  1. Availability of resources
  2. We assume that stakeholders and consumers are making rational decisions
  3. Using resources efficiently and effectively
  4. Marginal analysis
  5. Using models to describe or illustrate principles (e.g. regression analysis)

How people respond to incentives is particularly critical in healthcare.  We must find ways to align the goals of key stakeholders and properly incentivize the various groups to meet desired goals. As Bartlett said, “the real demand is for improved health.” Finally, we learned how to review treatment decisions from an economic perspective by understanding how to calculate QALY.


Quantitative Methods


Within the quantitative methods part of the course, through Merrill, I learned about epidemiology and biostatistics. The work with Merrill was fascinating.  However, the greatest gain was learning how to use Excel to manipulate data and, more importantly for my purposes, to have a solid understanding of what questions to ask when presented with data or a report. I have a better understanding of the types of studies I may encounter, scales of measurement and sampling – not just by definition but how to run sample sets in Excel. Analytic epidemiology involves hypothesis and assumption testing and in our coursework we examined associations between variables (e.g. health insurance and health status indicators). Further, we learned about categories of variables (nominal, ordinal, interval and ratio [where 0 means 0] and each of its respective presentation(s). Merrill emphasized that “data must be summarized” before we can make inferences about it (28). 


Understanding how to review and manipulate data will enable me to interpret and better understand the information I receive.  I also have a better sense of what questions to ask when I don't understand what I am seeing on the page.



DRAFT: This module has unpublished changes.