Structural Patterns in Chronic Disease Clinical Practice Guidelines Formalized for Clinical Decision Support

Samson W Tu; Tanya Podchiyska; Connie Oshiro; Susana Martins; Michael Ashcraft; Justin Chambers; Amy Robinson; Paul Heidenreich; Mary K Goldstein

Structural Patterns in Chronic Disease Clinical Practice Guidelines Formalized for Clinical Decision Support

AMIA Annu Symp Proc. 2023 Apr 29:2022:1081-1090. eCollection 2022.

Authors

Samson W Tu^{1

2}, Tanya Podchiyska¹, Connie Oshiro¹, Susana Martins^{1

3}, Michael Ashcraft¹, Justin Chambers¹, Amy Robinson^{1

3}, Paul Heidenreich^{1

2}, Mary K Goldstein^{1

2}

Affiliations

¹ VA Palo Alto Health Care System, Palo Alto, CA.
² Stanford University, Stanford, CA.
³ VA Program Evaluation and Resource Center, Office of Mental Health and Suicide Prevention, Menlo Park, CA.

PMID: 37128390
PMCID: PMC10148302

Abstract

Making recommendations from clinical practice guidelines (CPGs) computable for clinical decision support (CDS) has typically been a laborious and costly process. Identifying domain-specific regularities helps clinicians and knowledge engineers conceptualize, extract, and encode evidence-based recommendations. Based on our work to provide complex CDS in the management of multiple chronic diseases, we propose nine chronic disease CPG structural patterns, discuss considerations in representing the necessary knowledge, and illustrate them with the solutions that our CDS system provides.

Publication types

Practice Guideline
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Chronic Disease
Decision Support Systems, Clinical*
Humans
Multiple Chronic Conditions*