# Co-production of Diagnostic Excellence – Patients, Clinicians, and Artificial Intelligence: Comment on "Achieving Diagnostic Excellence: Roadmaps to Develop and Use Patient-Reported Measures With an Equity Lens"

## TL;DR

This paper discusses how patient-reported measures and AI can improve the diagnostic process by capturing the patient's perspective and enhancing diagnostic accuracy.

## Contribution

The paper introduces the concept of combining patient-reported measures with AI to co-produce diagnostic excellence.

## Key findings

- Existing diagnostic quality measures fail to capture the patient's perspective.
- Combining patient-reported metrics with AI techniques like diagnostic wayfinding can improve diagnostic performance.

## Abstract

Patients often experience long journeys within the healthcare system before obtaining a diagnosis. Though progress has been made in measuring the quality of diagnosis, existing measures largely fail to capture the diagnostic process from the patient’s perspective. McDonald and colleagues’ paper presents 7 overarching goals for the use of patient-reported measures (PRMs) in diagnostic excellence and presents visual roadmaps to guide the development, implementation, and evaluation of these measures. To accelerate the real-world use of PRMs, organizations should initially prioritize the use of patient-reported metrics that are already in development, such as patient-reported experience measures. Pairing PRMs with artificial intelligence (AI) techniques, such as "diagnostic wayfinding" (a dynamic diagnostic refinement process that also includes analysis of electronic health record data and metadata to characterize the diagnostic journey), should also improve diagnostic performance. Ultimately, combining PRMs with technological advancements holds the potential to achieve true co-production of diagnostic excellence.