EON

Project Description

EON is an extensible architecture for developing decision-support tools for various aspects of protocol-based care. Initially developed to create systems for executing clinical trial protocols for the treatment of cancer and HIV infection¹, it has been extended for the management of chronic diseases and of other types of guidelines.

It is now in use in the Athena (Assessment and Treatment of Hypertension: Evidence-Based Automation) Decision Support System.

The architecture of a software system consists of the system’s constituent components and the application programming interfaces that relate them. An EON application may contain several classes of components:

1) a declarative guideline knowledge base that defines all clinical protocols and guidelines in terms of a general computer-interpretable guideline model and

2) problem-solving modules such as the EON Guideline Interpreter that generates patient-specific advisories based on encoded guidelines and protocols

3) the ChronusII temporal mediator that processes complex temporal queries over relational databases

4) client programs that access the services provided by problem-solving modules and by the ChronusII temporal mediator.

Related People

Mark A. Musen, M.D., Ph.D
Professor of Medicine (Biomedical Informatics); Division Head (BMIR); Co-Director, Biomedical Informatics Training Program

Martin J. O'Connor, M.Sc.
System Software Developer

Samson W. Tu, M.S.
Sr. Research Engineer

Related Publications Only the 5 most recent displayed

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BMIR-2010-1473
Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain: J. Trafton, S. B. Martins, D. Wang, S. W. Tu, C. J. David, E. Jan, V. Brigit, B. Steve, C. E. Michael, S. D. Charles, R. Jack, D. Denise, M. K. Goldstein; Implement Sci, 5, 26. Published in 2010

BMIR-2010-1401
Formal Representations of Eligibility Criteria: A Literature Review: C. Weng, S. W. Tu, I. Sim, R. Richesson; Journal of Biomedical Informatics, JBI, 43, 3, 451-467. Published in 2010

BMIR-2010-1400
Evaluation of the acceptability and usability of a decision support system to encourage safe and effective use of opioid therapy for chronic, non-cancer pain by primary care providers: J. Trafton, S. B. Martins, M. Michel, E. Lewis, D. Wang, A. Combs, N. Scates, S. W. Tu, M. K. Goldstein; Pain Medicine, 11, 4, 575-585. Published in 2010

BMIR-2008-1472
Improving Patient Safety Using ATHENA-Decision Support System Technology: The Opioid Therapy for Chronic Pain Experience: M. Michel, J. Trafton, S. B. Martins, D. Wang, S. W. Tu, M. K. Goldstein; Advances in Patient Safety, 4. Published in 2008

BMIR-2007-1258
Mining Hospital Data to Learn SDA* Clinical Algorithms: D. Riaño, J. A. López-Vallverdú, S. W. Tu; 11th Conference on Artificial Intelligence in Medicine, From Knowledge to Global Care Workshop, Amsterdam. Published in 2007

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