ONCODOC: a successful experiment of computer-supported guideline development and implementation in the treatment of breast cancer.
Originally published as textual documents, clinical practice guidelines have poorly penetrated medical practice because their editorial properties do not allow the reader to easily solve, at the point of care, a given medical problem. However, despite the proliferation of implemented clinical practice guidelines as decision support systems providing an easy access to patient-centered information, there is still little evidence of high physician compliance to guidelines recommendations. Apart from physicians' psychological reluctance, the incompleteness of guideline knowledge and the impreciseness of the terms used, another reason may be that, although suited to average patients, clinical practice guideline recommendations are not a substitute for the physician-controlled clinical judgement that should be applied to each actual individual patient. Therefore, computer-based approaches based on the automation of context-free operationalization of guideline knowledge, although providing uniform optimal strategies to problem-focused care delivery, may generate inappropriate inferences for a specific patient that the physician does not follow in practice. Rather than providing automated decision support, ONCODOC allows the clinician to control the operationalization of guideline knowledge through his hypertextual reading of a knowledge base encoded as a decision tree. In this way, he has the opportunity to interpret the information provided in the context of his patient, therefore, controlling his categorization to the closest matching formal patient. Experimented in life-size ONCODOC demonstrated good appropriation of the system by physicians with significantly high scores of compliance. We successfully tested the implemented strategy and the knowledge base in a second medical institution, giving then a noticeable example of reuse and sharing of encoded guideline knowledge across institutions.
Service d'Informatique Médicale, DSI/AP-HP & Département de Biomatiques, Université Paris 6, Paris, France. bs@biomath.jussieu.fr
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Séroussi
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Bouaud
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Antoine
D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000628:therapy*; D003201:Computers; D020000:Decision Support Systems, Clinical; D005260:Female; D006284:Health Plan Implementation; D006801:Humans; D010819:Physician's Role; D017410:Practice Guidelines as Topic