Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain
- Equal contributors
1 Center for Health Care Evaluation (CHCE), VA Palo Alto Health Care System and Stanford University Medical School, 795 Willow Road (152-MPD), Menlo Park, CA 94025, USA
2 Geriatrics Research Education and Clinical Center (GRECC), VA Palo Alto Health Care System, 3801 Miranda Ave, Palo Alto, CA 94304-1290, USA
3 Center for Biomedical Informatics Research, Stanford University Medical School, Medical School Office Building, Room X-215, 251 Campus Drive, Stanford, CA 94305-5479, USA
4 VA Palo Alto Pain Management Service VA Palo Alto Health Care System and Stanford University Medical School, 3801 Miranda Ave, Palo Alto, CA 94304-1290, USA
5 Chronic Pain Rehabilitation Program, James A Haley Veterans Hospital, 13000 Bruce B. Downs Blvd., Tampa, FL 33612, USA
6 VA Eastern Colorado Health Care System, 1055 Clermont Street, Denver, CO 80220, USA
7 School of Pharmacy, University of Colorado Denver Health Sciences Center, Aurora, CO 80045, USA
8 Center for Human Development, 4211 Rickey's Way, Suite B, Palo Alto, CA 94306, USA
9 Center for Primary Care and Outcomes Research (PCOR), Stanford University, 117 Encina Commons, Stanford, CA 94305-6019, USA
Implementation Science 2010, 5:26 doi:10.1186/1748-5908-5-26Published: 12 April 2010
Opioid prescribing for chronic pain is common and controversial, but recommended clinical practices are followed inconsistently in many clinical settings. Strategies for increasing adherence to clinical practice guideline recommendations are needed to increase effectiveness and reduce negative consequences of opioid prescribing in chronic pain patients.
Here we describe the process and outcomes of a project to operationalize the 2003 VA/DOD Clinical Practice Guideline for Opioid Therapy for Chronic Non-Cancer Pain into a computerized decision support system (DSS) to encourage good opioid prescribing practices during primary care visits. We based the DSS on the existing ATHENA-DSS. We used an iterative process of design, testing, and revision of the DSS by a diverse team including guideline authors, medical informatics experts, clinical content experts, and end-users to convert the written clinical practice guideline into a computable algorithm to generate patient-specific recommendations for care based upon existing information in the electronic medical record (EMR), and a set of clinical tools.
The iterative revision process identified numerous and varied problems with the initially designed system despite diverse expert participation in the design process. The process of operationalizing the guideline identified areas in which the guideline was vague, left decisions to clinical judgment, or required clarification of detail to insure safe clinical implementation. The revisions led to workable solutions to problems, defined the limits of the DSS and its utility in clinical practice, improved integration into clinical workflow, and improved the clarity and accuracy of system recommendations and tools.
Use of this iterative process led to development of a multifunctional DSS that met the approval of the clinical practice guideline authors, content experts, and clinicians involved in testing. The process and experiences described provide a model for development of other DSSs that translate written guidelines into actionable, real-time clinical recommendations.