Medical errors from communication failures are enormous during the perioperative period of cardiac surgical patients. As caregivers change shifts or surgical patients change location within the hospital, key information is lost or misconstrued. After a baseline cognitive study of information need and caregiver work?ow, we implemented an advanced clinical decision support tool of intelligent agents, medical logic modules, and text generators called the "Inference Engine'' to summarize indi vidual patient's raw medical data elements into procedural milestones, illness severity, and care therapies. The system generates two displays: 1) the continuum of care, multimedia abstract generation of intensive care data (MAGIC) - an expert system that would automatically generate a physician brie?ng of a cardiac patient's operative course in a multimodal format; and 2) the isolated point in time, "Inference Engine'' - a system that provides a real-time, high-level, summarized depiction of a patient's clinical status. In our studies, system accuracy and ef?cacy was judged against clinician performance in the workplace. To test the automated physician brie?ng, "MAGIC,'' the patient's intraoperative course, was reviewed in the intensive care unit before patient arrival. It was then judged against the actual physician brie?ng and that given in a cohort of patients where the system was not used. To test the real-time representation of the patient's clinical status, system inferences were judged against clinician decisions. Changes in work?ow and situational awareness were assessed by questionnaires and process evaluation. MAGIC provides 200% more information, twice the accuracy, and enhances situational awareness. This study demonstrates that the automation of clinical processes through AI methodologies yields positive results.
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