Whitepapers and technical writing from the CareScribe Research & Clinical Informatics team on how AI documentation can be made accurate, auditable, and safe for regulated home and community care.
Our flagship publication on how CareScribe operationalizes AI documentation quality as a governance layer.
A composite, explainable 0–100 metric with eight weighted dimensions across two signal families — generation quality and workflow assurance — that updates dynamically as human-in-the-loop approval actions are completed. Every deduction is traceable to a named, auditable check rather than an opaque model confidence number.
The themes our research and engineering teams publish on as we build the governance infrastructure for clinical AI.
How PHIPA-aligned documentation, audit readiness, and immutable evidence chains turn AI output into defensible clinical records.
Sentence-level transcript grounding, calibrated confidence, and why every deduction should trace to a named, reproducible check.
PSW review, clinical event triage, worker attestation, and supervisor approval — and how quality metrics should rescore around them.