Support for guideline development through error classification and constraint checking

Mor Peleg, Vimla L. Patel, Vincenza Snow, Samson Tu, Christel Mottur-Pilson, Edward H. Shortliffe, Robert A. Greenes

Research output: Contribution to journalConference articlepeer-review


Clinical guidelines aim to eliminate clinician errors, reduce practice variation, and promote best medical practices. Computer-interpretable guidelines (CIGs) can deliver patient-specific advice during clinical encounters, which makes them more likely to affect clinician behavior than narrative guidelines. To reduce the number of errors that are introduced while developing narrative guidelines and CIGs, we studied the process used by the ACP-ASIM to develop clinical algorithms from narrative guidelines. We analyzed how changes progressed between subsequent versions of an algorithm and between a narrative guideline and its derived clinical algorithm. We recommend procedures that could limit the number of errors produced when generating clinical algorithms. In addition, we developed a tool for authoring CIGs in GLIF3 format and validating their syntax, data type matches, cardinality constraints, and structural integrity constraints. We used this tool to author guidelines and to check them for errors.

Original languageEnglish
Pages (from-to)607-611
Number of pages5
JournalProceedings / AMIA ... Annual Symposium. AMIA Symposium
StatePublished - 2002
Externally publishedYes

ASJC Scopus subject areas

  • General Medicine


Dive into the research topics of 'Support for guideline development through error classification and constraint checking'. Together they form a unique fingerprint.

Cite this