Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain

Abstract

BackgroundOpioid 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.MethodsHere 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.ResultsThe 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.ConclusionsUse 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.

Measurement of adherence to clinical practice guidelines for opioid therapy for chronic pain

“…The work of the project team was informed by the process and results of an existing knowledge base constructed from the 2003 VA/DoD CPG for Management of Chronic Opioid Therapy [11] for use in the ATHENA-Opioid Therapy (ATHENA-OT) clinical decision support system [15,16]. The knowledge base in a decision support system represents the defining and encoding of a CPG into actionable clinical practices.…”

“…The knowledge base in a decision support system represents the defining and encoding of a CPG into actionable clinical practices. The accuracy and adherence of the ATHENA-OT knowledge base to the intent of the 2003 VA/DoD CPG has been previously validated through iterative testing by local clinical and content experts and three members of the committee who participated in development of the guideline [11,16]. Existing definitions were reviewed by the project team to further ensure accuracy and to update domains where necessary based on recent improvements in the clinical evidence base and the 2010 VA/DoD CPG [12].…”

Clinical Decision Support Tool for Parental Tobacco Treatment in Primary Care

“…[21][22][23][24] The development team included researchers, clinical informaticians, clinical content experts, and end-users. To further inform tool development, we performed focus groups and 1-on-1 interviews with clinicians and the practice's medical director in April and May 2015.…”

Clinical Decision Support Tool for Parental Tobacco Treatment in Hospitalized Children

“…This framework involves developing a tool, incorporating input from future users of that tool, followed by inspection of its use [19][20][21]. We included input from a diverse team comprised of research experts, clinical informatics experts, clinical content experts (all members of the study team), and end-users (first year pediatric residents, hereafter referred to as "clinicians") [22]. Additionally, we received feedback about the tool and workflow from various hospital committees, including the CDS Committee, Therapeutics Standards Committee, and Clinical Care Committee.…”