School of Public Health
Faculty at the Center for Health Decision Science teach all core decision science courses at the Harvard T.H. Chan School of Public Health.
Decision Analysis for Health and Medical Practices (RDS 280)
Description: This course introduces students to the methods and applications of decision analysis and cost-effectiveness analysis in health technology assessment, medical and public health decision making, and health resource allocation. The objectives are: (1) to provide a basic technical understanding of the methods used in decision analysis, (2) to give the student an appreciation of the practical problems in applying these methods to the evaluation of clinical interventions and public health policies, and (3) to give the student an appreciation of the uses and limitations of these methods in decision making at the individual, organizational, and policy level both in developed and developing countries. Prerequisite/Level: Introductory economics is recommended but not required. BST 200 or BST 201 or BST 202 and BST 203 or BST 206 and BST 207 or BST 206 and BST 208 or BST 206 and BST 209 (concurrent enrollment allowed).
Economic Evaluation of Health Policy & Program Management (RDS 282)
Description: This course uses a case-study approach to introduce students to the application of health decision science to policymaking and program management at various levels of the health system. Both developed and developing country contexts will be covered. Topics include: (1) theoretical foundations of cost-effectiveness analysis (CEA); (2) controversies and limitations of CEA in practice; (3) the design and implementation of tools and protocols for measurement and valuation of cost and benefit of health programs; (4) the integration of evidence of economic value into strategic planning and resource allocation decisions, performance monitoring and program evaluation; (5) the role of evidence of economic value in the context of other stakeholder criteria and political motivations. Prerequisite/Level: RDS 280 or RDS 286 (concurrent enrollment allowed). Prior coursework in microeconomics is recommended.
Decision Theory (RDS 284)
Course Link: RDS 284 Decision Theory
Description: This course introduces the standard model of decision-making under uncertainty; its conceptual foundations, challenges, and alternatives; and methodological issues arising from the application of these techniques to health issues. Topics include von Neumann-Morgenstern and multi-attribute utility theory, Bayesian statistical decision theory, stochastic dominance, the value of information, judgment under uncertainty and alternative models of probability and decision making (regret theory, prospect theory, generalized expected utility). Applications are to preferences for health and aggregation of preferences over time and across individuals.
Decision Analysis Methods in Public Health and Medicine (RDS 285)
Description: This is an intermediate-level course on the methods and health applications of decision analysis modeling techniques. Topics include Markov models, microsimulation models, life expectancy estimation, cost estimation, deterministic and probabilistic sensitivity analysis, value of information analysis, and cost-effectiveness analysis. Lab or section times to be announced at first meeting. Prerequisite/Level: (BST 201 or ID 201) and (RDS 280 or RDS 286) (concurrent enrollment allowed). Familiarity with matrix algebra and elementary calculus may be helpful but not required.
Experiential Learning and Applied Research in Decision Analysis (RDS 290)
Description: This course is geared towards Masters students from any department. Students will undertake semester-long research projects on a clinical or public health decision problem using decision analysis, simulation modeling, and/or cost-effectiveness analysis. Each session will be dedicated to a particular topic of decision analytic methods or student presentations of prospectus, works-in-progress, and final projects. Students are expected to work in pairs, or occasionally groups of three, ideally including at least one student who is familiar with the clinical content area of the project. Prerequisite: (RDS 280 or 286) and (RDS 285 or 288).
Decision Analysis in Clinical Research (RDS 286)
Course Link: RDS 286 Decision Analysis in Clinical Research
Description: This course introduces students to decision analysis and cost effectiveness in a clinical context. Topics include: decision analysis methods relevant to clinical decision making, clinical research and comparative effectiveness research; the use of probability to express uncertainty; Bayes theorem and evaluation of diagnostic test strategies; sensitivity analysis; utility theory and its use to express patient preferences for health outcomes; cost-effectiveness analysis in clinical research and health policy; and the use, limits, and ethical issues of decision analysis and cost-effectiveness in clinical decision making and research design. Prerequisite/Level: Requires knowledge of clinical medicine and strong quantitative ability/aptitude. Priority given to students in the Program for Clinical Effectiveness (PCE). HSPH degree candidates who are not in PCE must demonstrate knowledge of clinical medicine (others should consider taking RDS 280 as an alternative). Non-degree students must provide evidence of both clinical training/research experience and mathematical ability (e.g., grades from quantitative courses, test scores).
Methods for Decision Making in Medicine (RDS 288)
Course Link: RDS 288 Methods for Decision Making in Medicine
Description: This course covers intermediate-level topics in medical decision making. Topics include decision models, evaluation of diagnostic tests, utility assessment, multi-attribute utility theory, Markov cohort models, microsimulation state-transition models, calibration and validation of models, probabilistic sensitivity analysis, value of information analysis, and behavioral decision making. The course will focus on the practical application of techniques and will include published examples and computer practicums. Prerequisite/Level: An introductory course in Decision Analysis (RDS 280 and RDS 286 or faculty approval of an equivalent course) and knowledge of probability and statistics. Limited enrollment.
Decision Science for Public Health (RDS 202)
Course Link: RDS 202 Decision Science for Public Health
Description: This course introduces students to the methods and applications of decision analysis and cost-effectiveness analysis in clinical and public health decision making. The course objectives are: (1) to provide a basic introduction to the methods and tools of decision science, and to recognize when, how, and in what context they can provide value in clinical and public health decision making; (2) to equip students with the ability to structure and bound a decision problem logically, identify key elements and influential factors; (3) to provide students with basic skills in revising probabilities given new information, building and analyzing decision trees, conducting cost effectiveness analysis, performing sensitivity analyses, and communicating results; (4) to enable students to thoughtfully and critically evaluate published analyses conducted to evaluate or inform clinical strategies, health technologies, and public health policies in developed and developing countries. Prerequisite/Level: Course restricted to students in the MPH-EPI or summer-only programs.
Risk Assessment (RDS 500)
Course Link: RDS 500 Risk Assessment
Description: This course introduces the framework of risk assessment and considers its relationship with cost-benefit analysis, decision analysis, and other tools for improving environmental decisions. The scientific foundations for risk assessment (epidemiology, toxicology, and exposure assessment) are discussed. The mathematical sciences involved in developing models of dose-response, fate and transport, and the statistical aspects of parameter estimation and uncertainty analysis are introduced. Case studies are used for illustration. Prerequisite/Level: Required for all Exposure, Epidemiology and Risk Program students.
Operations Management in Service Delivery Organizations (HCM 232)
Description: This course introduces concepts of operations management in service delivery organizations: operations management is concerned with evaluating the performance of operating units, understanding why they perform as they do, designing new or improved operating procedures and systems for competitive advantage, making short-run and long-run decisions that affect operations, and managing the work force. To understand the role of operations in any organization, a manager must understand process analysis, capacity analysis, types of processes, productivity analysis, development and use of quality standards, and the role of operating strategy in corporate strategy. The course will introduce students to these concepts and will also present the focused management approach which can help an organization achieve more with existing resources. Prerequisite/Level: Open only to students in Master in Health Care Management Program.