Training Program Title

Apply Prediction Models for Clinical Decision Support in a Real-World EMR in a Low-Resource Setting

Contributor

Advance-CTR

CTSA Hub Administered

yes

Contact Information

Advance-CTR, AdvanceRI@Brown.edu

Training Program Description

Applying Prediction Models for Clinical Decision Support within a Real-World EMR in a Low-Resource Setting: Lessons Learned on Using Haiti’s iSanté EMR to Support Differentiated HIV Care. Electronic medical records (EMRs) are promising tools for supporting differentiated HIV care, which matches intensity of services to patient needs, in low- and middle-income settings. Haiti’s national EMR, called iSanté, represents an excellent test case for studying the utility of EMR-based clinical decision support in low- and middle-income countries. We describe a trial of an EMR-based clinical decision support feature to identify patients at high risk of HIV treatment failure, as well as on-going work to optimize prediction models within EMRs such as iSanté.

Competency Keywords

Biostatistics, Biomedical informatics, Data management, Data analysis, Data capture, Research study management, Clinical research interactions, Responsible conduct of research, Documentation, Research participants, Clinical research, Translational research

Digital Commons Disciplines

Medical Education | Medicine and Health Sciences | Physical Sciences and Mathematics

Public

yes

Cost to Access

no

Learning Objectives

  • Applying Prediction Models for Clinical Decision Support
  • iSanté EMR to Support Differentiated HIV Care

Delivery Method

Online

Target Learners

Principal Investigators; Clinical Research Professionals (other than PI); MDs including residents, house officers, fellows, hospitalists; Post-doctoral scholars; Community Partners

Learning Level

Skilled

Onboarding

no

Frequency

Semi-Annually

Time Needed

56 minutes

Associated Assessment

no

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