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Every hospital in Europe loses millions to a manual, error‑prone coding process that hasn't changed in decades. When a patient is discharged, a trained specialist reads 20–50 pages of clinical documentation and manually translates every diagnosis and procedure into standardised codes. These codes determine how much the hospital gets paid.
In Germany, 17.5 million inpatient cases per year require manual coding. 51.5% of audited cases contain errors. €2.5–3 billion is clawed back annually by insurers through MD audits. 75% of German hospitals ended 2024 in deficit. The coder workforce — several thousand Kodierfachkräfte, each requiring 6–12 months of training at ~€42K salary — cannot scale fast enough.
We are building an autonomous AI coding agent that reads everything the coder reads, reasons from the official coding guidelines (Deutsche Kodierrichtlinien), and enters codes into existing hospital software — in seconds, not 35 minutes. Unlike traditional ML approaches that learn from historical patterns (and inherit their errors), our architecture reasons from the rules at inference time: when InEK publishes annual updates, we swap the guideline document. No retraining. No training data needed. Every code comes with an evidence chain and DKR citation — audit‑ready by construction.
The deployment model is equally differentiated: computer‑use agents navigate any hospital information system (KIS) via VPN, exactly as a human coder would. No API integration. No IT burden on the hospital. Deployment in days, not months. Deep EHR integration follows as relationships deepen, creating switching costs.
The AI clinical coding market is ~$3B today, projected to reach $10–11B by 2035. In Germany, the addressable market is €150–400M for AI coding software alone, with an additional ~€1.6B/year in audit clawbacks that better coding recovers. US players (CodaMetrix, Fathom, Nym) have raised $600M+ but are built for American codes and EHRs. Parallel has proven the model in France ($23.5M raised, computer‑use approach validated) but hasn't entered Germany. The German G‑DRG system is wide open.
As Founder & CEO, you will be responsible for turning a strong thesis into a scaled, defensible business.
We are not looking for a traditional healthcare executive or a pure AI researcher. The ideal candidate sits at the intersection.
Most importantly, you are motivated by building the system that automates the most cognitively demanding administrative process in healthcare — and turns every hospital into a customer for an expanding suite of AI agents.
#J-18808-LjbffrVeröffentlichungsdatum:
30 Jul 2026Standort:
BerlinTyp:
VollzeitArbeitsmodell:
Vor OrtKategorie:
Erfahrung:
2+ yearsArbeitsverhältnis:
Angestellt
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