The course is taught online as part of the MHPE Online program. This may be your first experience with an online course. Don't worry! Some advantages of taking a course over the web include:

• You can fit the coursework into your schedule
• You can get rapid feedback from students and faculty
• You don't need to take notes

• A web browser. I recommend Netscape Communicator, but Microsoft Internet Explorer works fine, too. Netscape comes with the UIC Network Services Kit, which you can download for free or purchase on CD for $15.
• Email software. Both of the major browser suites (Netscape and IE) include an email component, or you can use Eudora, which is bundled with the Network Services Kit, or any other program that you use to read and send email now.
• The free Adobe Acrobat reader, which is included with the Network Services kit, or available from Adobe. The readings are delivered in Adobe Acrobat (PDF) format.
• The free Microsoft Powerpoint 97 Viewer, which you can direct your web browser to use to display Powerpoint files when you click on them. Some class lectures are delivered as powerpoint files. Windows users can download the Powerpoint Viewer installation program here, and run it to install the viewer. You can test the viewer and learn to use it by clicking on this Powerpoint demonstration. If your browser asks you to pick an application to view the file with, select the powerpoint viewer, which is usually installed as c:\Program Files\PowerPoint Viewer\ppview32.exe

The class will consist of:

• Reading material (lectures and papers) about key concepts and applications.
• Discussion of readings using our class WebBoard, a web-based discussion system
• Online exercises
• A group project

• Presentation and discussion of readings: Each student will be asked to post a brief summary and critical review of some of the readings in the course on the WebBoard (readings marked with will have student presenters). A review should answer these questions:
  • What clinical situation or controversy is the subject of this article? A very brief summary of the facts presented in the article is in order, but everyone should have read the article, so please don’t go through the entire piece and re-present it.
  • What are the strengths and weaknesses of the article? Is there anything you’d like to know that you don’t? If you were re-designing the study, what would you change?
  • To what populations can the article be generalized, if this is an issue?
  • For decision analyses, were any plausible strategies left out of the analysis? Why? Where did the author get the utilities and probabilities?
  • Could the article’s conclusions be applied to clinical practice or public policy? How?
  All students should add their own comments and questions on the discussion board.
• Computer exercises: These provide an opportunity for hands-on experience with decision analysis software tools. Each exercise will be discussed on the WebBoard.
• Group project: The group project requires a team of 2-4 students to identify an important decision problem in one of their areas of expertise, review any relevant literature, and suggest how decision making could be improved, based on the material covered in the course. Projects will be presented on the WebBoard, and groups will respond to posted questions.

Weeks 1-2 - Introduction, decisions, uncertainty

1. Complete a decision making questionnaire
2. Read this session's lecture
3. Familiarize yourself with the WebBoard early this week by posting a short bio of yourself to the "Who's Who" conference, and ask any questions you have about the lecture in the "Introduction" conference.
4. Choose any disease or outcome and find two sources of numerical probabilities for that disease/outcome. Post the estimates and where you found them to the "Uncertainty" conference. How accurate do you think they are?

• Excerpts from chapters 11 and 12 of Baron, J. (1990). Thinking and Deciding. Cambridge: Cambridge University Press.

Week 3 - Diagnostic Tests, part 1: Signal Detection

Computer exercise: sensitivity and specificity

Computer demonstration: effect of changing cutoff points for continuous diagnostic tests

This week's readings:

• Dawson-Saunders, B. & Trapp, R. G. (1990). Basic and Clinical Biostatistics. Norwalk, CT: Appleton & Lange. Chapter 13: Evaluation of diagnostic procedures.
• Sackett, D. L. (1992). A primer on the precision and accuracy of the clinical examination. JAMA, 267(19): 2638-2644.

Week 4 - Diagnostic Tests, part 2: Bayesian Reasoning

Computer exercise: Use the online test calculator and nomogram to do the following for two diagnostic tests of your choice:

1. Determine what the post-test probability is if the pre-test probability was 0.1 and the test was positive.
2. Determine what the post-test probability is if the pre-test probability was 0.9 and the test was negative.
3. What is the treatment for the disease or condition that the test diagnoses? Above what post-test probability would you choose to treat the patient as if s/he had the disease? I.e., if a patient was 50% likely to have the disease, would you treat? What if s/he were 25% likely to have the disease?
4. Baesd on your answer above, determine below what pre-test probability we should neither test nor treat (i.e., at what pre-test probability would the post-test probability be below your threshold, even if the test were positive?) Determine above what pre-test probability we should treat without testing (at what pre-test probability would the post-test probability be above your threshold, even if the test were negative?) Are these pre-test probabilities clinically possible?

Feedback: Fill out this on-line feedback form to give Alan feedback on the course so far.

This week's readings:

• Gurney, J.W. (1993). Determining the Likelihood of Malignancy in Solitary Pulmonary Nodules with Bayesian Analysis. Part I: Theory. Radiology, 186(2): 405-413.
• Gurney, J.W., Lyddon, D. M., & McKay, J. A. (1993). Determining the Likelihood of Malignancy in Solitary Pulmonary Nodules with Bayesian Analysis. Part II: Application. Radiology, 186(2): 415-422.

Week 5 - Psychology of Diagnostic Reasoning

On the "Psychology of Diagnosis" conference, describe a diagnosis you or someone else made. What problem-solving procedure did the diagnostician go through? How way this like or unlike Bayes' Theorm? Were any errors made?

This week's readings:

• Dawson, N. V., & Arkes, H. R. (1987). Systematic errors in medical decision making: Judgment limitations. J. of Gen. Int. Med., 2:183-187.
• Hamm, R. M., & Zubialde, J. (1995). Physicians' expert cognition and the problem of cognitive biases. Primary Care, 22(2):181-212.

Week 6 - Preferences, Utilities, and Feelings

On the "Preferences" conference, describe a situation you've participated in where it was crucial to measure a patient's preferences. What outcomes did the patient need to evaluate and how was that done?

Computer exercise: Utility measurement. Assess your utility for having your nondominant hand amputated. Do this with all three utility assessment procedures. Post your results (the three utilities) and any questions on the "Preferences" conference.

This week's readings:

• Froberg, D. G., & Kane, R. L. (1989). Methodology for measuring health-state preferences -II: Scaling methods. J. Clin. Epi., 42(5): 459-471.
• Redelmeier, D. A., Rozin, P., & Kahneman, D. (1993). Understanding patients' decisions: Cognitive and emotional perspecctives. JAMA, 270(1): 72-76.
• Chapman, G. B., & Elstein, A. S. (1996) Utility assessment in prostate cancer. 
• Optional: Asch, D. A., Baron, J., Hershey, J. C., Kunreuther, H. Meszaros, J., Ritov, I., & Spranca, M. (1994). Omission Bias and Pertussis Vaccination. Med. Dec. Making, 14: 118-123.
• Optional: Guyatt, G. H., Naylor, C. D, Juniper, E., Heyland, D.K., Jaeschke, R., & Cook, D. J. (1997). Users' guides to the medical literature: XII. How to use articles about health-related quality of life measurements. JAMA, 277(15): 1232-1237. Also available on-line: http://hiru.hirunet.mcmaster.ca/ebm/userguid/11_qol.htm

Week 7 - Multiattribute Outcomes and Time

Computer exercise: Multi-attribute utility assessment of painkillers

This week's readings:

• Baron, J. (1990). Thinking and Deciding. Cambridge: Cambridge Univ. Press. pp. 310-317.
• Hodder, S. C., Edwards, M. J., Brickley, M. R., & Shepherd, J. P. (1997). Multiattribute utility assessment of outcomes of treatment for head and neck cancer. Br. J. Cancer, 75(6): 898-902.
• Chapman, G. B., & Elstein, A. S. (1995) Valuing the future: Temporal discounting of health and money. Med. Dec. Making, 15:373-386.
• Optional: Cropper, M. L., Aydede, S. K., & Portney, P. R. (1994). Preferences for life saving programs: How the public discounts time and age. J. Risk Unc., 8: 243-265.

Week 8 - Decision trees

This week's readings:

• Dawson-Saunders, B. & Trapp, R. G. (1990). Basic and Clinical Biostatistics. Norwalk, CT: Appleton & Lange. Chapter 14: Clinical decision making.
• Sox, H. C., Blatt, M. A., Higgins, M. C., & Marton, K. I. (1988). Medical Decision Making. Boston: Butterworths. Chapter 10: Bedside Decision Analysis
• O'Meara, J. J., McNutt, R. A., Evans, A. T., Moore, S. W., & Downs, S. M. (1994). A decision analysis of streptokinase plus heparin as compared with heparin alone for deep-vein thrombosis. NEJM, 330(26):1864-1869.
• Optional: Sox, H. C., Blatt, M. A., Higgins, M. C., & Marton, K. I. (1988). Medical Decision Making. Boston: Butterworths. Chapter 6: Expected Value Decision Making.

Week 9 - Markov Models and Influence Diagrams

Computer exercise: How to read an influence diagram

Computer exercise: Markov model simulation

This week's readings:

• Nease, R. F., Jr. & Owens, D. K. (1997). Use of Influence Diagrams to Structure Medical Decisions. Med. Dec. Making, 17(3):263-275.
• Sonnenberg, F. A. & Beck, J. R. (1993). Markov Models in Medical Decision Making: A Practical Guide. Med. Dec. Making, 13: 322-338.
• Kattan, M. W., Cowen, M. E., & Miles, B. J. (1997). A decision analysis for treatment of clinically localized prostate cancer. JGIM, 12: 299-305. 
• Optional: Owens, D. K., Shachter, R. D., & Nease, R. F., Jr. (1997). Representation and analysis of medical decision problems with influence diagrams. Med. Dec. Making, 17(3): 241-262.

Week 10 - Cost-Effectiveness Analysis

Computer exercise: cost-effectiveness analysis of breast cancer screening and/or treatment

Find an example of cost-effectiveness analysis from your field. Briefly summarize the decision, the costs and benefits, and the recommendation on the "Cost-Effectiveness" WebBoard conference. Do you agree with the recommendation?

This week's readings:

• Eddy, D. (1992). Cost-effectiveness analysis: A conversation with my father. JAMA, 267(12):1669-1675.
• Ganiats, T. G. (1995). Cost-effectiveness analysis. Primary Care, 22(2): 307-318.
• Ramsey, S. D. & Nettleman, M. D. (1992). Cost-effectiveness of prophylactic AZT following needlestick injury in health care workers. Med. Dec. Making, 12: 142-148. 

Week 11 - Quantitative Judgment and Mathematical Models

Find an example of a continuous quantitative judgment in your field (e.g. overall rating of medical school applications, predicting years of survival). What cues or pieces of information go into this judgment? On the "Quantitative Judgment" conference, list them, and for each one, give an informal rating of the direction and strength of the relationship between the cue and the judgment. For example, I might say that MCAT scores have a positive relation to the overall rating of medical school applicants, and that this relation is very strong relative to other cues.

This week's readings:

• Baron, J. (1990). Thinking and Deciding. Cambridge: Cambridge University Press. Chapter 18: Quantitative Judgment, pp. 352-373.
• Ebell, M. H. (1995). Using decision rules in primary care practice. Primary Care, 22(2): 319-340.
• Starmans, R., Muris, J. W., Fijten, G. H., Schouten, H. J. A., Pop, P., & Knottnerus, J. A. (1994). The diagnostic value of scoring models for organic and non-organic gastrointestinal disease, including the irritable-bowel syndrome. Med. Dec. Making, 14:208-216. 

Week 12 - How Else Might We Describe Judgments?

This week's readings:

• Gigerenzer, G. & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psy Rev., 103(4): 650-669.
• Cross, S. S., Harrison, R. F., & Kennedy, R. L. (1995). Introduction to neural networks. Lancet, 346: 1075-1079.
• Penny, W., & Frost, D. (1996). Neural networks in clinical medicine. Medical Decision Making, 16: 386-398.

Week 13 - Group Decision Making

On the "Group Decisions" conference, describe the last group decision you were involved in. What was your role in the group? How did the group reach its decision? Was there a decision rule involved (majority, consensus, leader decides)?

This week's readings:

• Plous, S. (1993). The Psychology of Judgment and Decision Making. New York: McGraw-Hill Inc. Chapter 18: Group Judgments and Decisions.
• Fiorelli, J. S. (1988). Power in work groups: Team member's perspectives. Hum. Rel., 41(1): 1-12. 

Week 14 - Ethical Decisions and Decision Making Ethics

1. Is decision analysis an ethical approach to making medical decisions?
2. Can decision analytic concepts be used to resolve ethical dilemmas in health care in particular?

This week's readings:

• Dowie, J. (1994). Decision analysis: the ethical approach to medical decision making. In: R. Gillon (Ed.) Principles of Health Care Ethics. Chichester: John Wiley, 412-434.
• Ubel P. A., Dekay, M. L., Baron, J., Asch, D. A. (1996). Cost-effectiveness analysis in a setting of budget constraints: Is it equitable? NEJM, 334: 1174-1177.
• Singer, P., McKie, J., Kuhse, H., & Richardson, J. (1995). Double jeopardy and the use of QALYs in health care allocation. J. Med. Ethics, 21(3): 144-150.
• Harris, J. (1995). Double jeopardy and the veil of ignorance -- a reply. J. Med. Ethics, 21(3): 151-157.

Week 15 - Improving Health Care Decisions

This week's readings:

• Eddy, D. M. (1996). Clinical Decision Making. Sudbury, MA: Jones and Bartlett. Chapters 4-6 (pp. 18-47) on practice guidelines.
• Elson, R. B. & Connelly, D. P. (1995). Computerized decision support systems in primary care. Primary Care, 22(2): 365-384.
• Elstein, A. S., Friedman, C. P., Wolf, F. M., Murphy, G., Miller, J., Fine, P., Heckerling, P., Miler, T., Sisson, J., Barlas, S., Biolsi, K., Ng, M., Mei, X., Franz, T., & Capitano, A. (1996). Effects of a decision support system on the diagnostic accuracy of users: A preliminary report. J Am. Med. Info. Ass., 3(6): 422-428. 

Medicine

John Clarke's Workshop on Surgical Decision Making is an excellent interactive tutorial on the web at http://www.auhs.edu/cgi-bin/tutorial/tutorial.cgi.

The Evidence-Based Medicine Toolbox at the Center for Evidence-Based Medicine at Oxford has a bunch of web-based explanations of diagnostic testing concepts, along with examples from the literature at http://cebm.jr2.ox.ac.uk/docs/toolbox.html.

• Bergus, G. R. & Cantor, S. B. (Eds.) (1995). Primary Care: Medical Decision Making. Philadelphia: Saunders.
• Bogner, M. S. (Ed.) (1994). Human Error in Medicine. Hillsdale, NJ: Lawrence Erlbaum.
• Dowie, J. & Elstein, A. (1991). Professional Judgment: A reader in clinical decision making. Cambridge: Cambridge University Press.
• Eddy, D. M. (1996). Clinical Decision Making: From theory to practice. Sudbury, MA: Jones and Bartlett.
• Kassirer, J. P, & Kopelman, R. I. (1991). Learning Clinical Reasoning. Philadelphia: Williams & Wilkins.
• Llewelyn, H. & Hopkins, A. (1993). Analysing How We Reach Clinical Decisions. London: Royal College of Physicians of London.
• Panzer, R. J., Black, E. R., & Griner, P. F. (Eds.) Diagnostic Strategies for Common Medical Problems. Philadelphia: American College of Physicians.
• Rigelman, R. K. (1995). The Measure of Medicine: Benefits, Harms, and Costs. Cambridge, MA: Blackwell Science.
• Sox, H. C., Blatt, M.A., Higgins, M. C., & Marton, K. I. (1988). Medical Decision Making. Boston: Butterworths.
• Sackett, D. L., Haynes, R. B., & Tugwell, P. (1991). Clinical Epidemiology: A basic science for clinical medicine, 2nd Ed. Boston: Little, Brown.
• Weinstein, M. C. & Fineberg, H. V. (1980). Clinical Decision Analysis. Philadelphia: Saunders.

Psychology

• Arkes, H. R. & Hammond, K. R. (1986). Judgment and Decision Making: An interdisciplinary reader. New York: Cambridge University Press.
• Baron, J. (1994). Thinking and Deciding, 2nd Ed. New York: Cambridge University Press.
• Edwards, W. (1992). Utility Theories: Measurements and applications. Boston: Kluwer.
• Hogarth, R. (1987). Judgment and Choice: The psychology of decision making, 2nd Ed. New York: John Wiley and Sons.
• Johnson-Laird, P.N. & Shafir, E. (1993). Reasoning and Decision Making. Cambridge, MA: Blackwell.
• Kahneman, D., Slovic, P., & Tversky, A. (1988). Judgment Under Uncertainty: Heuristics and biases. New York: Cambridge University Press.
• Payne, J.W., Bettman, J.R., & Johnson, E. J. (1993). The Adaptive Decision Maker. New York: Cambridge University Press.
• Plous, S. (1993). The Psychology of Judgment and Decision Making. New York: McGraw-Hill Inc.
• Poulton, E. C. (1994). Behavioral Decision Theory: A New Approach. New York: Cambridge University Press.
• Yates, J. F. (1990). Judgment and Decision Making. New Jersey: Prentice Hall.

Business and Public Policy

• Bazerman, M. H. (1990). Judgment in Managerial Decision Making, 2nd Ed. New York: John Wiley and Sons.
• Keeney, R. L. and Raiffa, H. (1993). Decisions with Multiple Objectives: Preferences and Value Tradeoffs. New York: Cambridge University Press.
• Kleindorfer, P. R., Kunreuther, H. C., and Schoemaker, P. J. H. (1993). Decision Sciences: An Integrative Perspective. New York: Cambridge University Press.
• Morgan, M. G. and Henrion, M. (1992). Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis. New York: Cambridge University Press.
• Russo, J. E. and Schoemaker, P. J. H. (1989). Decision Traps: The Ten Barriers to Brilliant Decision-Making and How to Overcome Them. New York: Simon & Schuster.