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利用具有计算机自适应测试和机器学习功能的开源 Concerto 平台,最大限度地发挥患者报告评估的潜力。

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

机构信息

Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom.

The Psychometrics Centre, University of Cambridge, Cambridge, United Kingdom.

出版信息

J Med Internet Res. 2020 Oct 29;22(10):e20950. doi: 10.2196/20950.

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.

摘要

患者报告评估正在改变医疗保健的许多方面,但在其交付方式方面仍有现代化的空间。像计算机化自适应测试(CAT)和机器学习这样的当代评估技术可以应用于患者报告评估,以减轻患者和医疗保健专业人员的负担;提高测试准确性;并提供个性化、可操作的反馈。Concerto 平台是一个高度可适应、安全且易于使用的控制台,它可以利用 CAT 和机器学习的力量来开发和管理高级患者报告评估。本文向读者介绍当代评估技术和 Concerto 平台。它回顾了 Concerto 平台推动的患者报告评估领域的进展,并解释了如何在几乎没有 CAT 或编程经验的情况下免费创建一个高级的、自适应的评估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf23/7661245/50ee2aa2ada3/jmir_v22i10e20950_fig1.jpg

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