MED 269: Clinical Decision Support

Course Directors: Jim Killeen, MD and Amy Sitapati, MD

When and Where: 
 Lecture:  Tuesdays and Thursdays from 4-6 pm in BRF building

Course Overview

This course is designed to give students an introduction to clinical decision support as well as clinical terminologies, quality programs, and population health. Decision support can be applied to use alerts, reminders, and other electronic health record related tools to inform users including clinicians and patients to make health related decisions. In a broader sense, clinical decision support (CDS) can include electronic tools which serve to reduce the cognitive burden involved in patient care delivery including innovative use of population health, health information exchange, and devices. The end result of CDS is to improve efficiency, screening, diagnosis, following treatment algorithms/protocols, adverse outcome avoidance, follow up, and cost.

The class is comprised of lectures including expert guest speakers as well as group projects that will be briefly presented at the course completion. 

Grading Policies
Criteria for Pass:

            80% attendance to class

            Completion of required reading

            Participation in class discussion and exercises

            Satisfactory performance/demonstration of learning objectives

            Completion of the class group project

            Meet passing criteria on Final Exam (70% correct)

 

Bibliography

1.         Gerstman BB. Basic Biostatistics, Jones and Bartlett Publishers, 2008.

2.         Hunink M, Glasziou P. Decision making in health and medicine. 1st ed. Cambridge: Cambridge University Press, 2001. p. 388.

3.         Blois MS. Information and Medicine: The Nature of Medical Descriptions. P. 160 University of California Press: Berkeley, 1984.

4.         Faga TJ Nomogram for Baye’s Theorem N Engl J Med Jul 31, 1975;293(5)257.

5.         Berner ES. Clinical Decision Support System: State of the Art. AHRQ Publication No. 09-0069_EF. Rockville, Maryland: Agency for Healthcare Research and Quality. June 2009.

6.         Osheroff JA, Teich JM Improving Outcomes with Clinical Decision Support: An Implementer’s Guide (2nd Ed). Chicago, Il, Healthcare Information Management Systems Society 2012.

7.         Sittig DF, Wright A, et.al. Grand challenges in clinical decision support. Journal of Biomedical Informatics. 2008 41:387-392.

8.         Kilbridge PM, Welebob Em, Classen DC. Development of the Leapfrog methodology for evaluating hospital implemented inpatient computerized physician order entry systems. Qual Saf Health Care 2006;15:81-4.

9.         Kannry J. Effect of e-prescribing systems on patient safety. Mt Sinai J Med. 2011;78(6):827-33.

10.       Clinical Practice Guidelines We Can Trust. Institute of Medicine 2011

11.       Straus S, Glaszious P, Richardson W and Haynes R Evidence-Based Medicine: How to PRacitce and Teach it (4th Ed) Churchill Livingstone (2010)

12.       Codish S, Shiffman RN. A model of ambiguity and vagueness in clinic practice guideline recommendations. AMIA Annu Symp Poc. 2005:146-50.

13.       De Clercq P, Kaiser K, Hasman A. Computer-interpretable Guidelines Formalism. In: ten Teije A, Miksch S, Lucas P, editors. Computer-based Medical Guidelines and Protocols: A primer and Current Trends. Amsterdam: IOS Press; 2008. P22-43.

14.       Elkin PL, Peleg M, Lacson R, Bernstam E, Tu S, Boxwala A, Greenes R, Shortliffe EH. Toward the standardization of electronic guidelines. MD Coput. 2000 Nov-Dec;17(6):39-44.