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

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Abstract

Patient-reported assessments are transforming many facets of health care, 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 on both patients and health care professionals; improve test accuracy; and provide individualized, actionable feedback. The Concerto platform is a highly adaptable, secure, and easy-to-use console that can harness the power of CAT and machine learning for developing and administering advanced patient-reported assessments. This paper introduces readers to contemporary assessment techniques and the Concerto platform. It reviews advances in the field of patient-reported assessment that have been driven by the Concerto platform and explains how to create an advanced, adaptive assessment, for free, with minimal prior experience with CAT or programming.

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Author and article information

Contributors

Conrad Harrison:

ORCID: https://orcid.org/0000-0002-1428-5751

Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal SciencesUniversity of OxfordThe Botnar Research CentreWindmill RoadOxford, OX3 7LDUnited Kingdom44 1865227374conrad.harrison@medsci.ox.ac.uk

Journal

Journal ID (nlm-ta): J Med Internet Res

Journal ID (iso-abbrev): J Med Internet Res

Journal ID (publisher-id): JMIR

Title: Journal of Medical Internet Research

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Print): 1439-4456

ISSN (Electronic): 1438-8871

Publication date Collection: October 2020

Publication date (Electronic): 29 October 2020

Volume: 22

Issue: 10

Electronic Location Identifier: e20950

Affiliations

[1 ] Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences University of Oxford Oxford United Kingdom

[2 ] The Psychometrics Centre University of Cambridge Cambridge United Kingdom

[3 ] MD Anderson Center for INSPiRED Cancer Care University of Texas Houston, TX United States

Author notes

Corresponding Author: Conrad Harrison conrad.harrison@ 123456medsci.ox.ac.uk

Author information

Conrad Harrison https://orcid.org/0000-0002-1428-5751

Bao Sheng Loe https://orcid.org/0000-0001-6310-1608

Przemysław Lis https://orcid.org/0000-0003-1969-7146

Chris Sidey-Gibbons https://orcid.org/0000-0002-4732-7305

Article

Publisher ID: v22i10e20950

DOI: 10.2196/20950

PMC ID: 7661245

PubMed ID: 33118937

SO-VID: fcef4747-f9b6-414e-94a6-9ed4c199b762

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 2 June 2020

Date revision requested : 21 July 2020

Date revision received : 28 July 2020

Date accepted : 18 August 2020

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