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Semi-automated Entry of Clinical Temporal-abstraction Knowledge

¹ Stanford University, Stanford, California.
² University of California, San Francisco, California.

Correspondence and reprints: Yuval Shahar, MD, PhD, Assistant Professor, Medicine and Computer Science, Stanford Medical Informatics, 251 Campus Drive, Medical School Office Building X215, Stanford University, Stanford, CA 94305-5479. e-mail: <shahar{at}smi.stanford.edu>.

Objectives: The authors discuss the usability of an automated tool that supports entry, by clinical experts, of the knowledge necessary for forming high-level concepts and patterns from raw time-oriented clinical data.

Design: Based on their previous work on the RESUMÉ system for forming high-level concepts from raw time-oriented clinical data, the authors designed a graphical knowledge acquisition (KA) tool that acquires the knowledge required by RÉSUMÉ. This tool was designed using Protégé, a general framework and set of tools for the construction of knowledge-based systems. The usability of the KA tool was evaluated by three expert physicians and three knowledge engineers in three domains—the monitoring of children's growth, the care of patients with diabetes, and protocol-based care in oncology and in experimental therapy for AIDS. The study evaluated the usability of the KA tool for the entry of previously elicited knowledge.

Measurements: The authors recorded the time required to understand the methodology and the KA tool and to enter the knowledge; they examined the subjects' qualitative comments; and they compared the output abstractions with benchmark abstractions computed from the same data and a version of the same knowledge entered manually by RÉSUMÉ experts.

Results: Understanding RÉSUMÉ required 6 to 20 hours (median, 15 to 20 hours); learning to use the KA tool required 2 to 6 hours (median, 3 to 4 hours). Entry times for physicians varied by domain—2 to 20 hours for growth monitoring (median, 3 hours), 6 and 12 hours for diabetes care, and 5 to 60 hours for protocol-based care (median, 10 hours). An increase in speed of up to 25 times (median, 3 times) was demonstrated for all participants when the KA process was repeated. On their first attempt at using the tool to enter the knowledge, the knowledge engineers recorded entry times similar to those of the expert physicians' second attempt at entering the same knowledge. In all cases RÉSUMÉ, using knowledge entered by means of the KA tool, generated abstractions that were almost identical to those generated using the same knowledge entered manually.

Conclusion: The authors demonstrate that the KA tool is usable and effective for expert physicians and knowledge engineers to enter clinical temporal-abstraction knowledge and that the resulting knowledge bases are as valid as those produced by manual entry.

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