Augmenting Clinicians with AI at Kry

Kry is using AI to turn each doctor visit into a higher output, better coded, lower cost workflow, which matters more than full automation in a regulated payer driven market. The model is simple. AI listens, drafts notes, suggests diagnosis codes and treatment paths, and sends that work back to a clinician for approval. That improves doctor throughput, reduces admin time, and can lift payment when coding is more complete, especially in systems where secondary diagnoses increase reimbursement.

• Kry already framed AI as part of the core care workflow, not a side tool. Patients answer intake questions first, AI organizes symptoms and likely codes, then a doctor reviews and decides. That lets Kry keep the clinician in the loop while moving more of the prep and paperwork into software.
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• This is also a revenue cycle product, not just a productivity product. In Krys model, better coding can mean better payment from payers. That is the same basic wedge used by AI medical scribes like Abridge, which convert visit transcripts into billing ready notes and surface missed codes that raise provider revenue.
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• The advantage for Kry versus standalone AI tools is distribution. It already has patients, clinicians, payer contracts, and local integrations across Europe. That makes AI immediately usable inside real visits, while smaller startups may have the model but not the care delivery system needed to turn it into reimbursed healthcare.
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The next phase is AI moving earlier in the visit and deeper into payment and triage. As these tools get more reliable, telehealth leaders with their own clinician networks and payer relationships should capture more visits per doctor, document them more completely, and push more markets toward subscription and value based reimbursement.