Currently submitted to: JMIR Medical Informatics
Date Submitted: Nov 25, 2025
Open Peer Review Period: Dec 10, 2025 - Feb 4, 2026
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Negative Telemetry: A Novel Framework for Real-Time Diagnostic Error Prevention Through Behavioral Analysis of Clinical Omissions
ABSTRACT
Diagnostic errors kill 371,000 Americans and permanently disable 424,000 more each year, yet no technology exists to detect these errors before they cause harm. Cognitive factors—premature closure, anchoring, confirmation bias—drive 74-96% of diagnostic failures, but current clinical decision support systems intervene only after clinicians have already committed to erroneous diagnostic paths. This Viewpoint introduces negative telemetry, a paradigm shift proposing that diagnostic accuracy is more powerfully predicted by what clinicians fail to examine than by what they examine. We synthesize convergent evidence from behavioral biometrics (keystroke dynamics achieving 97.9% sensitivity and 94.7% specificity for cognitive impairment detection; r = -0.497, P<.001), electronic health record workflow analysis, and just-in-time adaptive intervention research (effect sizes of Hedges' g = 0.79-1.65). These behavioral signatures are sufficiently distinctive to enable individual-level cognitive modeling, yet sensitive enough to detect clinically meaningful state changes. The technical infrastructure for negative telemetry already exists within electronic health record systems; what has been missing is a conceptual framework for interpreting which omissions matter. We provide that framework and propose a research agenda for validation. The 371,000 Americans who die annually from diagnostic errors deserve more than retrospective analysis—they deserve real-time prevention.
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