Mark A. Musen, M.D., Ph.D.! 1. Stanford Medical Informatics ... an application called ATHENA DSS [1], which has been deployed at the General Medical Clinic ...
A Client-Server Framework for Deploying a Decision-Support System in a Resource-Constrained Environment Martin J. O'Connor, M.Sc.1, Ravi D. Shankar, M.S.', Samson W. Tu, M.S.1, Aneel Advani, M.D. 1, Mary K. Goldstein, M.D.' 1, Robert W. Coleman, M.S.2, Mark A. Musen, M.D., Ph.D.! 1. Stanford Medical Informatics, Stanford University School of Medicine, Stanford, CA 94305; 2. VA Palo Alto Health Care System, Palo Alto, CA 94304
There is a conflict between the desire for increasingly complex and robust decision-support applications and the reality of deploying them in legacy systems in existing clinical settings. The hardware and software environments at a clinical site can often conspire to complicate this deployment process. These difficulties can be increased when deploying research-based decision-support systems because of the limited administrative and staffing resources typically available to such systems. We addressed these problems by designing a client-server framework for rapid deployment of a decision-support system in a resourceconstrained environment. This framework minimizes the resources required of client machines and also simplifies administrating the decision-support applications on those machines. We deployed a decision-support system developed using this framework at a hospital within the Department of Veterans Affairs.
System We have developed a framework for designing a decision-support system that addresses these issues. The primary goal of this framework is to allow rapid deployment of complex decision-support systems in a resource-constrained environment with limited adminiistrative support. We used this framework to build an application called ATHENA DSS [1], which has been deployed at the General Medical Clinic and Hypertension Clinic at the VA Palo Alto Health Care Systerm We used the EON guideline model [2], to create a computer interpretable representation of the hypertension guideline outlined in the Sixth Report of the Joint National Committee on Prevention, Detection, Evaluation and Treatment of High Blood Pressure [3]. Our system displays patient-specific treatment information at the point of care, and allows a clinician to modify patient data and fetch updated advisories, or to request an explanation for any recommendation.
Introduction Elaborate decision-support tools are useless if they cannot solve problems in real clinical settings. Unfortunately, the amount of work required to deploy such tools can often approach the amount of work that went into their initial development. Several factors generate difficulties in the deployment process: (1) Typical health care environments have a motley collection of clinic workstations, many with aging CPUs and severely limited system memory. This configuration diversity complicates software maintenance and updates. (2) Installing and administrating physically distributed client systems that have been deployed in a health care site can increase the system development effort. (3) Complex and resource-intensive systems are often necessary to ensure a consistently high quality of advice. Physician acceptance of a decision-support system is usually predicated on the quality and timeliness of this advice. (4) Deploying research-based decision-support systems can be even more complex because they are less stable than commercial systems and they evolve continuously. (5) Sufficient administrative and staffing support is seldom available to constantly update and maintain the guideline application on client machines.
1067-5027/01/$5.00 C) 2001 AMIA, Inc.
Acknowledgements This work has been supported, in part, by grant LM05708 from the National Library of Medicine, and by a grant from FastTrack Systems, Inc., and by VA grants HSR&D CPG-97-006 and RCD-96-301.
References 1. Goldstein MK, Hoffian BB, Coleman RW, Musen MA, Tu SW, Advani A, Shankar R, and O'Connor M. ATHENA DSS, an Easily Modifiable Decision Support System for Managing Hypertension in Primary Care. Proceedings of the AMIA Annual Symposium. Los Angeles, CA, November 2000; 300-304. 2. Musen MA, Tu SW, Das AK, and Shahar Y. EON: A Component-Based Approach to Automation of Protocol-Directed Therapy. Journal of the American Medical Informatics Association, 1996; 3(6): 367-388. 3. National High Blood Pressure Education ProgranL The Sixth Report of the Joint National Committee on Prevention, Detection, Evaluation and Treatment of High Blood Pressure. 1997, National Institutes of Health, Washington, D.C.
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