Seltzer et al. (2026)
- Authors: Jaime Seltzer, Stephanie L. Grach, Scott D. Eggers, Melissa M. Redetzke, Katie J. Mau, Tony Y. Chon, Ravindra Ganesh
- Institutes: The Myalgic Encephalomyelitis Action Network, Santa Clara, CA, USA, Division of General Internal Medicine, Mayo Clinic, Rochester, MN, USA, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Publisher: International Journal of Environmental Research and Public Health (IJERPH)
- Link: DOI
Summary
This research demonstrates a practical solution to the long-standing problem of delayed or missed diagnosis in ME/CFS by using digital clinical support tools. By providing primary care doctors with an expert-vetted algorithm at the point of care, the study achieved a significant increase in the accuracy of referrals. This indicates that the “diagnostic crisis” in ME/CFS can be partially addressed through targeted knowledge delivery rather than relying solely on specialized medical training. For patients, such tools can shorten the path to a correct diagnosis and ensure that management recommendations are evidence-based from the first clinical encounter.
What was researched?
The study investigated whether a point-of-care clinical decision support tool (the AskMayoExpert algorithm) could improve the diagnostic accuracy and referral quality for ME/CFS within a large healthcare system.
Why was it researched?
The research addressed the widespread lack of medical education regarding ME/CFS, which often leads to a majority of patients remaining undiagnosed or receiving incorrect clinical guidance.
How was it researched?
Researchers performed a retrospective analysis at the Mayo Clinic to compare diagnostic concordance—the agreement between a primary referral and a specialist’s final diagnosis—before and after the introduction of the digital algorithm. They also tracked how frequently the tool was accessed by healthcare providers to assess its reach and utility.
What has been found?
Following the implementation of the algorithm, diagnostic concordance increased from 55.3% to 76.9%, meaning primary referrals were much more likely to be accurate. Referrals were 1.39 times more likely to result in a confirmed specialist diagnosis of ME/CFS, and over 580 providers accessed the tool during the study period.
Discussion
The findings suggest that providing “just-in-time” clinical guidance can effectively bridge educational gaps for complex, poorly understood diseases. A key strength noted was the involvement of individuals with lived experience in the tool’s development, though the study’s scope was limited to the Mayo Clinic’s institutional environment.
Conclusion & Future Work
Implementing accessible clinical care algorithms can significantly improve the recognition and management of ME/CFS in general medical settings. The authors suggest that healthcare systems should prioritize similar digital resources to address public health-scale diagnostic delays.