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Cognitive Science and Medical InformaticsSpring
Semester 2004 (G5043)
Instructors: Vimla Patel (patel@dbmi.columbia.edu) David Kaufman (davek@dbmi.columbia.edu) Date & Time: Monday & Wednesday 10:35-11:50 rationale Cognitive
science is a multidisciplinary field incorporating theories and methods from
psychology, linguistics, philosophy, anthropology, and computer science in the
study of cognition. Cognitive science provides a framework for the analysis and
modeling of complex human performance and has considerable applicability to
address a range of issues in informatics. Developments in medical informatics
research have afforded possibilities for great advances in healthcare delivery.
These exciting opportunities also present formidable challenges in terms of
implementation and integration of technologies in the workplace. As in most
domains, there is a gulf between technological artifacts and end-users. Since
medical practice is a human endeavor and there is a need for bridging
disciplines to enable clinicians to benefit from rapid technological advances.
This necessitates a broadening of disciplinary boundaries to consider cognitive
and social factors pertaining to the design and use of technology. The
focus in this course is on conceptual and methodological issues in cognitive
science and medical informatics. Prerequisites: None. Introduction
to Computer Applications in Health Care and Biomedicine (Medical Informatics
G4001/Computer Science W4001) would be beneficial. This is an introductory
survey course and presupposes no background in cognitive science, although some
familiarity with cognitive issues (e.g., decision making) as they pertain to
medicine will be helpful. This is a course intended for graduate students.
However, advanced undergraduates may take the course with the instructors’
permission. Course Format: The
course will include lectures, seminar discussions, and methodology
laboratories. Readings: Selected Articles Grading: ·
Class
Participation: 20% ·
Assignments:
40% ·
Final
Project: 40% The final project will ask participants to employ theories
and methods of cognitive science to address research issues of their own
interest. The assignment may include an empirical component in the form of 1) a
small study (e.g., a single subject), 2) evaluation of an information
technology or 3) a computer program that embodies or illustrates a facet of the
course. Objectives 1)
To
familiarize participants with concepts, methods, and theories in cognitive science. 2)
To
introduce students to issues at the interface between cognitive science and
medical informatics. 3)
To
elucidate through readings and discussions the different ways in which cognitive
theory can be relevant to the practice of medical informatics and in turn, how
medical informatics can meaningfully inform cognitive theory. 4)
To
provide a forum for students to explore the ways in which cognitive science can
illuminate aspects of their own research. Tentative Reading List: Students will be expected to read 1 or 2
readings for each class 1)
Cognitive
Science and Medical informatics: Issues at the Interface (Vimla
& Dave) Patel, V.L. & Kaufman, D.R. (1998) Medical Informatics and the Science of Cognition. Journal of the American Medical Informatics Association; 5: 493-502. Patel, V.L., Arocha, J.F. & Kaufman, D.R. (2001) A Primer on Aspects of Cognition for Medical Informatics. Journal of the American Medical Informatics Association; 8: 2)
Historical Introduction to Cognitive
Science Bechtel W, Abrahamsen A, Graham
G. The life of cognitive science. In: Bechtel W, Graham G, al. e, eds. A companion to cognitive science. 3)
Cognitive Evaluation of Medical Information Technologies Kushniruk, A. W., Kaufman, D.R., Patel, V.L., Lévesque, Y., & Lottin, P. (1996) Assessment of a Computerized Patient Record System: A Cognitive Approach to Evaluating an Emerging Medical Technology. M.D. Computing, 13, 406-415. Kushniruk A.W., Patel V.L. (1998) Cognitive evaluation of decision making processes and assessment of information technology in medicine. International Journal of Medical Informatics; 51- 83-90. Patel, V.L., Kushniruk, A.W.,
Yang, S., & Yale, J.F. (2000) Impact of a computerized patient record
system on medical data collection, organization and reasoning. JAMIA; 7(6): 569-585. Polson P, Lewis C, Rieman J, Wharton C. (1992). Cognitive walkthroughs: A method for theory-based evaluation of user interfaces. International Journal of Man-Machine Studies 36, 741-773. Berg, M. (1999) Patient care information systems and health care work: A sociotechnical approach. International Journal of Medical Informatics; 55, 87-101. 4) Research in Medical Cognition Patel, V.L., Arocha, J.F. & Kaufman, D.R. (1994) Diagnostic Reasoning and Expertise. Psychology of Learning and Motivation, 31, 137-252. 5)
Nature
of Medical Expertise Patel, V.L., & Kaufman, D.R.,
(2000). Cognitive Psychology of
Medical Expertise. The International Encyclopedia of the Social
and Behavioral Sciences. Patel, V. L., & Groen, G. J.
(1991a). The General and Specific Nature of Medical Expertise: A Critical Look.
In A. Ericsson and J. Smith (Eds.), Towards a general theory of expertise:
Prospects and limits (pp. 93-125). 6) Medical Artificial Intelligence and Cognitive Modeling Shortliffe E. H. (1993). The adolescence of AI in Medicine: Will the field come of age in the '90s? Artificial Intelligence in Medicine.5 93-106. Clancey W.J. (1988) Acquiring,
representing and evaluating a competence model of diagnostic strategy. Chi MTH,
Glaser R, Farr MJ, eds. The nature of expertise. Pople H. Heuristic methods for
imposing structure on ill-structured problems: The structuring of medical
diagnosis. In: Szolovits P, ed. Artificial Intelligence in Medicine. 7)
Medical Problem Solving and Knowledge
Organization Feltovich, P. J., Johnson, P.
E., Moller, J. H., & Swanson, D. B. (1984). LCS: The role and development
of medical knowledge in diagnostic expertise. In W. J. Clancey & E. H.
Shortliffe (Eds.), Kaufman, D.R., Kushniruk, A.W., Yale, J.F & Patel, V.L (1999). Conceptual Knowledge and Decision Strategies in Relation to Hypercholesterolemia and Coronary Heart Disease. International Journal of Medical Informatics, 55, 159-177. 8)
Comprehension and Medical Discourse Kintsch, W.: Comprehension: A
Paradigm for Cognition. Arocha, J.F. & Patel, V.L.
(1994) Construction-Integration Theory and Clinical Reasoning. In C.A. Weaver,
III, S. Mannes, & C.R. Fletcher (Eds.), Discourse
Comprehension: Essays in honor of
Walter Kintsch (pp. 359-381). Patel, V.L., Arocha, J.F., Diemeier, M., How, J. & Mottur-Pilson, C. (2001) Cognitive psychological studies of representation and use of clinical practice guidelines. International Journal of Medical Informatics, 63 147-168. 9)
Learning and Clinical Reasoning Kaufman, D.R. Patel, V.L., & Magder, S (1996). The Explanatory Role of Spontaneously Generated Analogies in a Reasoning about Physiological Concepts. International Journal of Science Education, 18, 369-386. Spiro, R.J., Feltovich, P.J.,
Coulson, R.L., and Chinn, C. A. & Brewer, W. F. (1993). The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction. Review of Educational Research, 63(1), pp. 1-49. Arocha, J.F., Patel, V.L., & Patel, Y.C. (1993) Hypothesis generation and the coordination of theory and evidence in medical diagnostic reasoning. Medical Decision Making, 13, 198-211. 10)
Psychological Studies of Decision-Making Tversky, A. & Kahneman, D. (1974). Judgment under uncertainty: heuristics and biases. Science, 185, 1124-1131. Kahneman, D., & Tversky, A. Choices, values and frames. American Psychologist, 1984, 39, 341-350. 11) Medical Decision Making Chapman, G.B., & Elstein,
A.S. (2000). Cognitive processes and biases in medical decision-making. In G.
B. Chapman & F. S. Sonnenberg, (Eds.) Decision-making
in health care: Theory, psychology, and
applications (pp. 183-210). Elstein, A.S., Holzman, G.B, Belzer, L.J & Ellis, R.D. (1992). Hormonal replacement therapy: Analysis of clinical strategies used by residents, Medical Decision Making, 12, 265-273. 12) Naturalistic
Decision Making Gaba, D. (1992). Dynamic
decision-making in anesthesiology: Cognitive models and training approaches. In
D. A. Evans & V. L. Patel (Eds.), Advanced
Models of Cognition for Medical Training and Practice (pp. 123-148). Leprohon, J., & Patel, V. L. (1995). Decision making strategies for telephone triage in emergency medical services. Medical Decision Making. 15, 240-253. Orasanu, J., & Connoly, T.
(1993). The reinvention of decision making. In G. A. Klein, J. Orasanu, R.
Calderwood, & C. E. Zsambok (Eds.), Decision making in action: Models and
methods (pp. 3-20). Patel, V.L., Kaufman, D.R. & Arocha, J.F. (in press). Emerging paradigms on cognition and Medical decision making. To appear in Journal of Biomedical Informatics. 13) Distributed
Cognition and the Medical Workplace: Issues of Safety and the Study of Errors Patel, V. L., Kaufman, D. R.,
& Magder, S. A. (1996). The acquisition of medical expertise in complex
dynamic environments. In K. A. Ericsson (Ed.), The road to excellence: The acquisition of expert performance in the
arts and sciences, sports and games (pp. 127-165). Xiao, Y., Hunter, W. A., Mackenzie, C. F., Jefferies, N. J., Horst, R. L. and others. (1996). Task complexity in emergency medical care and its implications for team coordination. Human Factors. 38 636-645. Vicente, K. J. (1999). Cognitive Work Analysis: Toward Safe, Productive and Healthy Computer-Based Work. Introductory Chapter. Kohn, L. T., Corrigan, J.M.
& Donaldson, M. S. (eds.) (1999). To Err Is Human: Building a Safer Health
System. |
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