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Medical Informatics and Clinical Decision Making: The Science and the Pragmatics*

Medical Informatics and Clinical Decision Making: The Science and the Pragmatics* There are important scientific and pragmatic synergies between the medical decisionmaking field and the emerging discipline of medical informatics. In the 1970s, the fieldof medicine forced clinically oriented artificial intelligence (AI) researchers to developways to manage explicit statements of uncertainty in expert systems. Classic probabilitytheory was considered and discussed, but it tended to be abandoned because of complexitiesthat limited its use. In medical AI systems, uncertainty was handled by a variety of adhoc models that simulated probabilistic considerations. To illustrate the scientificinteractions between the fields, the author describes recent work in his laboratory thathas attempted to show that formal normative models based on probability and decisiontheory can be practically melded with AI methods to deliver effective advisory tools. Inaddition, the practical needs of decision makers and health policy planners areincreasingly necessitating collaborative efforts to develop a computing and communicationsinfrastructure for the decision making and informatics communities. This point isillustrated with an example drawn from outcomes management research. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Medical Decision Making SAGE

Medical Informatics and Clinical Decision Making: The Science and the Pragmatics*

Shortliffe, Edward H.
Medical Decision Making , Volume 11 (4_suppl): 13 – Dec 1, 1991
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References (29)

Publisher
SAGE
Copyright
© 1991 Society for Medical Decision Making
ISSN
0272-989X
eISSN
1552-681X
DOI
10.1177/0272989X9101104s02
Publisher site
See Article on Publisher Site

Abstract

There are important scientific and pragmatic synergies between the medical decisionmaking field and the emerging discipline of medical informatics. In the 1970s, the fieldof medicine forced clinically oriented artificial intelligence (AI) researchers to developways to manage explicit statements of uncertainty in expert systems. Classic probabilitytheory was considered and discussed, but it tended to be abandoned because of complexitiesthat limited its use. In medical AI systems, uncertainty was handled by a variety of adhoc models that simulated probabilistic considerations. To illustrate the scientificinteractions between the fields, the author describes recent work in his laboratory thathas attempted to show that formal normative models based on probability and decisiontheory can be practically melded with AI methods to deliver effective advisory tools. Inaddition, the practical needs of decision makers and health policy planners areincreasingly necessitating collaborative efforts to develop a computing and communicationsinfrastructure for the decision making and informatics communities. This point isillustrated with an example drawn from outcomes management research.

Journal

Medical Decision Making – SAGE

Published: Dec 1, 1991

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