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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Jun 2, 2020
Date Accepted: Aug 18, 2020

The final, peer-reviewed published version of this preprint can be found here:

Maximizing the Potential of Patient-Reported Assessments by Using the Open-Source Concerto Platform With Computerized Adaptive Testing and Machine Learning

Harrison C, Loe BS, Lis P, Sidey-Gibbons C

Maximizing the Potential of Patient-Reported Assessments by Using the Open-Source Concerto Platform With Computerized Adaptive Testing and Machine Learning

J Med Internet Res 2020;22(10):e20950

DOI: 10.2196/20950

PMID: 33118937

PMCID: 7661245

Delivering the full potential of patient-reported assessments: computerized adaptive testing and machine learning using the open source Concerto platform

  • Conrad Harrison; 
  • Bao Sheng Loe; 
  • Przemysław Lis; 
  • Chris Sidey-Gibbons

ABSTRACT

Patient-reported assessments are transforming many facets of healthcare, but there is scope to modernize their delivery. Contemporary assessment techniques like computerized adaptive testing (CAT) and machine learning can be applied to patient-reported assessments to reduce burden, improve accuracy and provide individualize, actionable feedback. The Concerto platform is a highly adaptable, secure and easy-to-use console for developing and administering advanced patient-reported assessments that can harness the power of CAT and machine learning. In this paper, we introduce readers to contemporary assessment techniques and the Concerto platform. We review advances in the field of patient-reported assessment that have been driven by the Concerto platform and explain how to create an advanced, adaptive assessment, for free, with no prior experience of CAT or programming.


 Citation

Please cite as:

Harrison C, Loe BS, Lis P, Sidey-Gibbons C

Maximizing the Potential of Patient-Reported Assessments by Using the Open-Source Concerto Platform With Computerized Adaptive Testing and Machine Learning

J Med Internet Res 2020;22(10):e20950

DOI: 10.2196/20950

PMID: 33118937

PMCID: 7661245

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