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Currently accepted at: JMIR Formative Research

Date Submitted: Dec 17, 2019
Open Peer Review Period: Dec 17, 2019 - Feb 1, 2020
Date Accepted: Aug 16, 2020
(closed for review but you can still tweet)

This paper has been accepted and is currently in production.

It will appear shortly on 10.2196/17512

The final accepted version (not copyedited yet) is in this tab.

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

XML Data and knowledge encoding structure for a web and mobile antenatal clinical decision support system

  • Ever Augusto Torres Silva; 
  • Sebastian Uribe Ocampo; 
  • Jack Smith; 
  • Ivan Felipe Luna Gomez; 
  • Jose Fernando Florez Arango; 


Displeasure with clinical decision support systems (CDSS) functionality is considered the primary challenge in CDSS development. A major difficulty in CDSS design is matching the functionality to correct and actual clinical workflow. Computer-Interpretable guidelines (CIG) are used to formalize medical knowledge in clinical practice guidelines (CPG) in a computable language, however, existing CIG frameworks require a specific interpreter for each language, hindering the ease of implementation and interoperability. This has led the authors to propose a different approach in terms of how clinical knowledge and data is represented and change the clinician’s perception of a CDSS. This approach was originally intended to create a readable and minimal syntax for a web CDSS for antenatal care guidelines, with improved human-computer interaction and enhanced usability by aligning the system behavior with clinical workflow.


Please cite as:

Torres Silva EA, Ocampo SU, Smith J, Luna Gomez IF, Florez Arango JF

XML Data and knowledge encoding structure for a web and mobile antenatal clinical decision support system

JMIR Preprints. 17/12/2019:17512

DOI: 10.2196/preprints.17512


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