PubMed Health⌕ Search

PubMed · 15360798

Modeling guidelines for integration into clinical workflow.

Abstract

The success of clinical decision-support systems requires that they are seamlessly integrated into clinical workflow. In the SAGE project, which aims to create the technological infra-structure for implementing computable clinical practice guide-lines in enterprise settings, we created a deployment-driven methodology for developing guideline knowledge bases. It involves (1) identification of usage scenarios of guideline-based care in clinical workflow, (2) distillation and disambiguation of guideline knowledge relevant to these usage scenarios, (3) formalization of data elements and vocabulary used in the guideline, and (4) encoding of usage scenarios and guideline knowledge using an executable guideline model. This methodology makes explicit the points in the care process where guideline-based decision aids are appropriate and the roles of clinicians for whom the guideline-based assistance is intended. We have evaluated the methodology by simulating the deployment of an immunization guideline in a real clinical information system and by reconstructing the workflow context of a deployed decision-support system for guideline-based care. We discuss the implication of deployment-driven guideline encoding for sharability of executable guidelines.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Samson W Tu, Mark A Musen, Ravi Shankar, James Campbell, Karen Hrabak, James McClay, Stanley M Huff, Robert McClure, Craig Parker, Roberto Rocha, Robert Abarbanel, Nick Beard, Julie Glasgow, Guy Mansfield, Prabhu Ram, Qin Ye, Eric Mays, Tony Weida, Christopher G Chute, Kevin McDonald, David Molu, Mark A Nyman, Sidna Scheitel, Harold Solbrig, David A Zill, Mary K Goldstein. 2004. Modeling guidelines for integration into clinical workflow.. https://pubmed.ncbi.nlm.nih.gov/15360798/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Viewpoint: controversies surrounding use of order sets for clinical decision support in computerized provider order entry.

Order sets provide straightforward clinical decision support within computerized provider order entry systems. They make "the right thing" easier to do because they are much faster than writing single orders; they deliver real-time, evidence-based prompts; they are easy to update; and they support coverage of multiple patient problems through linkages among order sets. This viewpoint paper discusses controversies surrounding use of order sets--advantages and pitfalls, decision-making criteria, and organizational considerations, including suggestions for vendors. Order sets have the potential to improve clinician efficiency, provide real-time guidance, facilitate compliance with Joint Commission on Accreditation of Healthcare Organizations and Centers for Medicare and Medicaid Services performance measure sets, and encourage overall acceptance of computerized provider order entry, but may not do so unless these controversies are addressed.

Decision Making, Computer-Assisted↗

VisualDx: decision-support software for the diagnosis and management of dermatologic disorders.

The VisualDx system (http://www.logicalimages.com/prodVDx.htm) is a JAVA-based decision-support program developed by Logical Images to be used in clinical care to develop differential diagnoses based upon morphologic finding- and patient finding-driven searching. It consists of several modules, many of which are very relevant to infectious diseases specialists, such as Fever & Rash; International Travel; Drug Eruptions; Smallpox Vaccination; Terrorism Recognition; Immunocompromised, HIV or AIDS; Female Genital Rashes & Growths; and Male Genital Rashes & Growths. Unlike books and atlases indexed by disease, with VisualDx, clinicians can enter patient descriptors and lesion morphologies, resulting in rapid assistance with differential diagnosis. VisualDx also increases clinician awareness of, knowledge about, and skills in the recognition of chemical warfare, bioterrorism, and radiation injuries.

Decision Making, Computer-Assisted↗