Series: 'Medical desertification and care wandering' — Article 5, Part 4/5
How to scale industrially without turning predictive prevention into an error machine, a cognitive overload generator, and an iatrogenic factory? Industrialising in healthcare is not neutral: at small scale, human adjustment and local attention compensate for flaws; at large scale, anything not structurally designed becomes a systemic risk.
Every digital health catastrophe begins with the same illusion: what works in a pilot works at scale. Therac-25 shows that a rare bug multiplied by volume becomes a lethal statistical certainty. The NHS National Programme for IT (NPfIT, over £10 billion) shows that organisational complexity exceeding governance capacity collapses. HealthCare.gov shows that insufficient industrial capacity is not a technical detail but a major political and social risk.
The experience of large-scale EHR deployments (Epic Systems) reveals a specific failure: systems were designed to be deployed, not to be cognitively sustainable at tens of millions of patients. Technical industrialisation succeeded. Clinical industrialisation — maintaining usability and relevance at scale — failed.
The projection for 12 million PREDICARE patients is sobering: 3.6 million alerts per day for 60,000 physicians, or 60 alerts per physician daily — the critical threshold exceeded from year 1 without trajectory-based filtering via the digital twin. PREDICARE precisely transforms a threshold-based monitoring logic into a trajectory-based monitoring logic, where it is not the variation that triggers the alert but the probability of it leading to an avoidable adverse event.
The article establishes non-negotiable boundaries: industrialise collection, algorithmic processing, and traceability, but protect final clinical decisions, patient communication, and individual benefit/risk assessment. The clinician remains the decision author, never the validator of an automatic recommendation.
Industrial interoperability requires mandatory FHIR R4, documented APIs, third-party testing, and graduated sanctions. Change management is not a marginal accompaniment — it is an infrastructure to be funded at face value, on par with servers and algorithms. Article originally published on LinkedIn (in French).