Data and knowledge standards for learning health: A population management example using chronic kidney disease.

Journal Article (Journal Article)

The widespread creation of learning health care systems (LHSs) will depend upon the use of standards for data and knowledge representation. Standards can facilitate the reuse of approaches for the identification of patient cohorts and the implementation of interventions. Standards also support rapid evaluation and dissemination across organizations. Building upon widely-used models for process improvement, we identify specific LHS activities that will require data and knowledge standards. Using chronic kidney disease (CKD) as an example, we highlight the specific data and knowledge requirements for a disease-specific LHS cycle, and subsequently identify areas where standards specifications, clarification, and tools are needed. The current data standards for CKD population management recommendations were found to be partially ambiguous, leading to barriers in phenotyping, risk identification, patient-centered clinical decision support, patient education needs, and care planning. Robust tools are needed to effectively identify patient health care needs and preferences and to measure outcomes that accurately depict the multiple facets of CKD. This example presents an approach for defining the specific data and knowledge representation standards required to implement condition-specific population health management programs. These standards specifications can be promoted by disease advocacy and professional societies to enable the widespread design, implementation, and evaluation of evidence-based health interventions, and the subsequent dissemination of experience in different settings and populations.

Full Text

Duke Authors

Cited Authors

  • Cameron, B; Douthit, B; Richesson, R

Published Date

  • October 2018

Published In

Volume / Issue

  • 2 / 4

Start / End Page

  • e10064 -

PubMed ID

  • 31245588

Pubmed Central ID

  • PMC6508834

Electronic International Standard Serial Number (EISSN)

  • 2379-6146

Digital Object Identifier (DOI)

  • 10.1002/lrh2.10064


  • eng

Conference Location

  • United States