Semi-automated entry of clinical temporal-abstraction knowledge.
OBJECTIVE
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.


METHODS
Based on their previous work on the RESUME 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 RESUME. 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.


METHODS
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 RESUME experts.


RESULTS
Understanding RESUME 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 RESUME, using knowledge entered by means of the KA tool, generated abstractions that were almost identical to those generated using the same knowledge entered manually.


CONCLUSIONS
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.
Shahar
Y
Y
Stanford Medical Informatics, Stanford University, California 94305-5479, USA. shahar@smi.stanford.edu
Chen
H
H
Stites
D P
DP
Basso
L V
LV
Kaizer
H
H
Wilson
D M
DM
Musen
M A
MA
D000163:Acquired Immunodeficiency Syndrome / Q000628:therapy; D001185:Artificial Intelligence; D003199:Computer Systems; D001330:Electronic Data Processing; D005069:Evaluation Studies as Topic; D006801:Humans; D016347:Medical Records Systems, Computerized; D012984:Software; D013995:Time; D014584:User-Computer Interface