Maximizing the Potential of Patient-Reported Assessments by Using the Open-Source Concerto Platform With Computerized...

Area of Science:

• Health Informatics
• Digital Health
• Patient-Reported Outcomes

Background:

• Patient-reported assessments are crucial in healthcare but require modernization.
• Current methods can be burdensome for patients and professionals.
• Opportunities exist to enhance accuracy and feedback through technology.

Purpose of the Study:

• Introduce contemporary assessment techniques like CAT and machine learning.
• Present the Concerto platform for developing and administering advanced assessments.
• Demonstrate how to create adaptive assessments with minimal experience.

Main Methods:

• Leveraging computerized adaptive testing (CAT) for dynamic question selection.
• Applying machine learning for individualized and actionable feedback.
• Utilizing the Concerto platform for assessment development and deployment.

Main Results:

• Concerto platform facilitates the creation of advanced, adaptive patient-reported assessments.
• CAT and machine learning integration reduce assessment burden and improve accuracy.
• Individualized feedback is generated for patients and healthcare professionals.

Conclusions:

• The Concerto platform offers a powerful, accessible solution for modernizing patient-reported assessments.
• Advanced techniques like CAT and machine learning can significantly enhance healthcare delivery.
• Free access to Concerto empowers users to create sophisticated assessments with ease.