Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Generation and Evaluation of Intraoperative Inferences for Automated Health Care Briefings on Patient Status After Bypass Surgery

Generation and Evaluation of Intraoperative Inferences for Automated Health Care Briefings on... AbstractObjective: The authors present a system that scans electronic records from cardiac surgery and uses inference rules to identify and classify abnormal events (e.g., hypertension) that may occur during critical surgical points (e.g., start of bypass). This vital information is used as the content of automatically generated briefings designed by MAGIC, a multimedia system that they are developing to brief intensive care unit clinicians on patient status after cardiac surgery. By recognizing patterns in the patient record, inferences concisely summarize detailed patient data.Design: The authors present the development of inference rules that identify important information about patient status and describe their implementation and an experiment they carried out to validate their correctness. The data for a set of 24 patients were analyzed independently by the system and by 46 physicians.Measurements: The authors measured accuracy, specificity, and sensitivity by comparing system inferences against physician judgments, in cases where all three physicians agreed and against the majority opinion in all cases.Results: For laboratory inferences, evaluation shows that the system has an average accuracy of 98 percent (full agreement) and 96 percent (majority model). An analysis of interrater agreement, however, showed that physicians do not agree on abnormal hemodynamic events and could not serve as a gold standard for evaluating hemodynamic events. Analysis of discrepancies reveals possibilities for system improvement and causes of physician disagreement.Conclusions: This evaluation shows that the laboratory inferences of the system have high accuracy. The lack of agreement among physicians highlights the need for an objective quality-assurance tool for hemodynamic inferences. The system provides such a tool by implementing inferencing procedures established in the literature. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the American Medical Informatics Association Oxford University Press

Generation and Evaluation of Intraoperative Inferences for Automated Health Care Briefings on Patient Status After Bypass Surgery

Download PDF
14 pages

Loading next page...
 
/lp/oxford-university-press/generation-and-evaluation-of-intraoperative-inferences-for-automated-YLd2vin5rE

References (22)

Publisher
Oxford University Press
Copyright
American Medical Informatics Association
ISSN
1067-5027
eISSN
1527-974X
DOI
10.1136/jamia.2001.0080267
Publisher site
See Article on Publisher Site

Abstract

AbstractObjective: The authors present a system that scans electronic records from cardiac surgery and uses inference rules to identify and classify abnormal events (e.g., hypertension) that may occur during critical surgical points (e.g., start of bypass). This vital information is used as the content of automatically generated briefings designed by MAGIC, a multimedia system that they are developing to brief intensive care unit clinicians on patient status after cardiac surgery. By recognizing patterns in the patient record, inferences concisely summarize detailed patient data.Design: The authors present the development of inference rules that identify important information about patient status and describe their implementation and an experiment they carried out to validate their correctness. The data for a set of 24 patients were analyzed independently by the system and by 46 physicians.Measurements: The authors measured accuracy, specificity, and sensitivity by comparing system inferences against physician judgments, in cases where all three physicians agreed and against the majority opinion in all cases.Results: For laboratory inferences, evaluation shows that the system has an average accuracy of 98 percent (full agreement) and 96 percent (majority model). An analysis of interrater agreement, however, showed that physicians do not agree on abnormal hemodynamic events and could not serve as a gold standard for evaluating hemodynamic events. Analysis of discrepancies reveals possibilities for system improvement and causes of physician disagreement.Conclusions: This evaluation shows that the laboratory inferences of the system have high accuracy. The lack of agreement among physicians highlights the need for an objective quality-assurance tool for hemodynamic inferences. The system provides such a tool by implementing inferencing procedures established in the literature.

Journal

Journal of the American Medical Informatics Association – Oxford University Press

Published: May 1, 2001

There are no references for this article.