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Modeling a Clinical Pathway for Contraception.

Letha J Sooter1, Steve Hasley2, Robert Lario3

  • 1Department of Informatics and Networked Systems, University of Pittsburgh, Pittsburgh, Pennsylvania, United States.

Applied Clinical Informatics
|December 21, 2019
PubMed
Summary
This summary is machine-generated.

Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN) were used to model the Centers for Disease Control and Prevention (CDC) contraceptive recommendations. This standardization facilitates integration into digital health platforms.

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Area of Science:

  • Health Informatics
  • Clinical Decision Support

Background:

  • The Centers for Disease Control and Prevention (CDC) published the "U.S. Selective Practice Recommendations for Contraceptive Use" in 2016.
  • This document outlines medical eligibility criteria (MEC) for initiating or continuing contraception based on patient health status.

Purpose of the Study:

  • To model and standardize the processes and decisions for birth control initiation using the CDC's MEC.
  • To explore the utility of Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN) for this purpose.
  • To enable integration of these standardized models into electronic health records (EHR) systems or other digital platforms.

Main Methods:

  • Utilized BPMN and DMN to model medical terminology, processes, and decisions related to contraceptive use.
  • Collaborated with the CDC to ensure the accuracy of the modeled information.
  • Identified and categorized challenges encountered during terminology binding.

Main Results:

  • Successfully developed a standardized model for contraceptive initiation based on CDC MEC.
  • Encountered challenges primarily related to the integration of clearly defined data elements.

Conclusions:

  • Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN) can be effectively employed to create models for clinical pathways, despite inherent strengths and weaknesses.
  • The developed model demonstrates the potential for standardizing clinical decision-making processes in digital health applications.