A U.S. hospital network approached us following a series of medication-related incidents caused by manual cross-checks. Their existing EHR integration depended on static rule lists that could not capture complex interactions or new medications.
They needed a model capable of understanding context — dosage, timing, patient profile — rather than simple name matches.
We had prior experience implementing compliant AI systems under HIPAA and ISO 13485 standards. Our privacy-first design, auditable pipelines, and explainable-AI (XAI) approach matched their governance requirements.
01 Rule Explosion:
legacy systems stored > 15 000 static interaction
rules, outdated quarterly.
02 Context Loss:
No differentiation between duplicate prescriptions
for different treatment phases.
03 Alert Fatigue:
Clinicians ignored > 60 % of alerts as irrelevant.
04 Audit Gaps:
Interaction justifications were missing from audit logs.
