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Using Personal Health Records for Automated Clinical Trials Recruitment: the ePaIRing Model.

Adam Wilcox1, Karthik Natarajan, Chunhua Weng

  • 1Department of Biomedical Informatics, Columbia University, New York, NY.

Summit on Translational Bioinformatics
|February 25, 2011
PubMed
Summary
This summary is machine-generated.

A new model, electronic Participant Identification and Recruitment (ePaIR) model, uses patient data to enhance clinical trial recruitment. It details information flow and the role of personal health records in improving participant identification and enrollment.

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Area of Science:

  • Health Informatics
  • Clinical Trial Management
  • Data Science in Healthcare

Background:

  • Clinical trial recruitment faces challenges in identifying and enrolling eligible participants.
  • Efficient patient recruitment is crucial for the timely completion of clinical research.

Purpose of the Study:

  • To develop a model for optimizing patient recruitment in clinical trials using patient information.
  • To describe variations in information flow among stakeholders in the recruitment process.
  • To highlight the role of personal health records in facilitating patient identification and recruitment.

Main Methods:

  • Development of the electronic Participant Identification and Recruitment (ePaIRing) model.
  • Analysis of information flow between various stakeholders involved in clinical trials.
  • Assessment of how personal health records can be leveraged for recruitment.

Main Results:

  • The ePaIRing model provides a framework for understanding and improving patient recruitment processes.
  • Identified key variations in data exchange among stakeholders.
  • Demonstrated the potential of personal health records to streamline participant identification.

Conclusions:

  • The ePaIRing model offers a structured approach to enhance clinical trial participant recruitment.
  • Utilizing patient information and personal health records can significantly improve recruitment efficiency.
  • Further development and implementation of such models are recommended for advancing clinical research.