A strategy for developing practice guidelines for the ICU using automated knowledge acquisition techniques

• Published in: Journal of clinical monitoring and computing Vol. 15; no. 2; pp. 109 - 117
• Main Authors: DE CLERCQ, P. A, BLOM, J. A, HASMAN, A, KORSTEN, H. H. M
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer 01-02-1999; Springer Nature B.V
• Subjects: Anesthesia. Intensive care medicine. Transfusions. Cell therapy and gene therapy; Artificial Intelligence; Biological and medical sciences; Data Display; Decision support systems; Decision Support Systems, Clinical; Emergency and intensive care: techniques, logistics; Expert Systems; Hospital data processing; Humans; Information management; Intensive care medicine; Intensive Care Units; Knowledge acquisition; Medical Records; Medical sciences; Miscellaneous; Practice Guidelines as Topic; Reminder Systems; Human; Clinical management; Knowledge acquisition; Professional practice; Emergency; Computer aid; Recommendation; Intensive care unit; Automated processing
• ISSN: 1387-1307; 1573-2614
• DOI: 10.1023/A:1009911121207

To implement practice guideline entry tools in a reminder system in order to provide decision support to health care workers in clinical care and emergency care environments. To design a knowledge acquisition environment that enables physicians to formulate, update, and verify guidelines without the assistance of a knowledge engineer.
We developed a knowledge acquisition environment for the Intensive Care Unit (ICU) consisting of 1) a graphical knowledge acquisition tool, 2) tools that perform logical and semantic tests on proposed guidelines, 3) a Patient Data Management System (PDMS) containing clinical patient data, and 4) an expert system that reminds ICU health care workers of inconsistencies between a treatment plan and implemented guidelines. Physicians enter the guidelines using the knowledge acquisition tool, after which consistency and correctness tests are performed on the guidelines. The guidelines are then transferred to the knowledge base of the reminder system and validated by applying the new guidelines to a large stored data set of previous patients. If the new guidelines are approved, they are exported to the reminder system that is used in daily practice.
ICU physicians used the knowledge acquisition tool to enter 58 guidelines into the reminder system's knowledge base. These guidelines were tested on a data set consisting of 803 previously admitted patients. As a result, 27 guidelines fired at least once, generating 406 reminders in total. Of the 406 generated reminders, 356 (88%) were issued correctly and 50 (12%) were false alarms. The reminders that were issued correctly involved 3 situations: 1) the database contained inconsistent or incomplete information, 2) the actions or decisions of the health care workers were not the most appropriate ones, and 3) there was a potential risk involved. All false alarms were caused by the fact that the corresponding guidelines were not specific enough to handle certain exceptions. As a result of this analysis, the guidelines could be improved in such a way as to eliminate all false alarms.
These first results demonstrate that this bottom-up knowledge acquisition strategy, implemented by the automated knowledge acquisition tools, enables medical specialists to improve the quality of computer support in an ICU without assistance of a knowledge engineer.