Patient reported outcome measures: from the classics to AI

Area of Science:

• Orthopedics
• Psychometrics
• Health Outcomes Research

Background:

• Classical test theory (CTT) historically underpinned the development of patient-reported outcome measures (PROMs), particularly in hand surgery.
• CTT has been instrumental in quantifying subjective patient experiences and surgical outcomes.
• However, advancements in psychometric modeling necessitate exploring more sophisticated approaches.

Purpose of the Study:

• To review the evolution of measurement techniques in hand surgery outcome assessment.
• To compare classical test theory with modern psychometric methods such as item response theory (IRT) and computerized adaptive testing (CAT).
• To explore the potential impact of artificial intelligence (AI) on future outcome measurement.

Main Methods:

• This is a review article synthesizing existing literature on measurement theories in health outcomes.
• It traces the historical progression from CTT to IRT and CAT.
• It discusses emerging applications of AI in outcome assessment.

Main Results:

• CTT, while foundational, has limitations in accurately measuring patient-reported outcomes.
• IRT and CAT offer more precise and efficient measurement of patient traits.
• AI presents opportunities for personalized outcome prediction and assessment.

Conclusions:

• The field of patient-reported outcome measures in hand surgery is advancing beyond classical test theory.
• Modern psychometric approaches like IRT and CAT provide superior tools for quantifying patient outcomes.
• Artificial intelligence holds significant promise for revolutionizing outcome assessment in the future.