Ongoing Critiquing During Trauma Management

Artificial Intelligence in Medicine: Interpreting Clinical Data

Volume

Papers from the 1994 AAAI Spring Symposium

Contents

A major challenge facing the developers of intelligent decision-support systems is how to present pertinent information to physicians in such a way that it will be effective in influencing their behavior. The successful integration of medical decision-support systems into clinical environments has been a widely reported problem ever since such systems began to appear. While recognizing these systems’ potential for improving the quality of patient care and for controlling costs, physicians have tended to reject new technologies which they see as intrusive, time-consuming, or a challenge to their judgment or autonomy as clinical decision-makers [1]. At the same time, the information processing demands on physicians have been increasing dramatically. Utilizing available clinical data to make appropriate decisions about patient care can be challenging to physicians, who are susceptible to information overload which can lead to biases in data acquisition and processing [8]. To counteract the limitations of human information processing, computer-based decision-support systems have been developed to monitor clinical data as they become available and make physicians aware of pertinent events by means of reminders [4] or alerts [2]. Other systems follow the critiquing approach, providing a more extensive off-line discussion of the risks and benefits of alternative management plans [5, 9].