Remote patient monitoring looks simple on a slide: a patient takes a reading at home, a clinician sees it, and someone intervenes before a problem becomes a hospitalization. The value is obvious and t
Ask a general-purpose LLM a clinical question and it will answer with confidence: sometimes correct, sometimes a plausible fabrication, and rarely with a way to tell the difference. In most domains a
Most software fails gracefully. A dropped request retries, a broken page shows a fallback, a bad recommendation wastes a few minutes. AI patient triage does not get that luxury. When a symptom checker
AI healthtech products rarely fail because the model was wrong. They fail because the architecture around the model treated Protected Health Information (PHI) as an afterthought: logged where it shoul
The pitch for an AI clinical documentation product is easy to make and hard to build. A clinician talks to a patient, the software listens, and a finished, coded, EHR-ready note appears. The demo land
The best AI agency for a healthtech product is the one that talks about controls before features: PHI data flows, BAAs, audit trails, and what happens when the model is wrong about a patient. This gui
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