What question did this study set out to answer?

This note addresses the challenges of e-iatrogenesis in big medical data and proposes solutions for maintaining clinical accuracy.

February 9, 2026Open Access

Technical Note: The Thin Red Line of Epidemiological Stability. Detecting e- Iatrogenesis in Big Medical Data Big Medical Data

View Full Paper

Ask AI

Bookmark

Authors

This technical note examines e-iatrogenesis in big medical data, suggesting a need for rigorous benchmarking to ensure clinical accuracy.

This note addresses the challenges of e-iatrogenesis in big medical data and proposes solutions for maintaining clinical accuracy.
Analysis of three cases in oncology, dermatology, and psychiatry.
Introduction of the Sensitivity Hazard Ratio to assess signal instability.
Benchmarking data-driven outcomes against national health registries.
Clinical signals appeared misleading due to flawed denominators and biases.
A 77% reduction in schizophrenia cases and a fourfold increase in vitiligo incidence were erroneously reported.
Recalibrating against benchmarks showed that these perceived risks were not valid.

Marco Roccetti (2026) studied this question.

View Full Paper

Ask AI

Bookmark