Clinician-Reviewed Care Workflow
AfriHealth AI connects voice capture, structured documentation, follow-up support, and model evaluation in one workflow for community health workers and clinicians. It is a decision-support prototype, not an autonomous diagnostic or prescribing system.
1. Architectural Breakdown
Module 1: Triage
Captures code-switched speech, presents a transcript, and prepares a clinician-review triage summary.
Module 2: EHR Intake
Structures reviewed transcripts into draft SOAP, ICD-10, symptom, and medication artifacts.
Module 3: Post-Care Follow-up
Simulates follow-up conversations and highlights possible escalation signals for human review.
2. Benchmarking Compliance
Provides separate fixture, AfriSwitch, and simulated clinical benchmark evidence. Metrics include Word Error Rate (WER), Character Error Rate (CER), target-term recall, and safety-critical term misses. Results are intended for model comparison and error analysis, not population-level clinical claims.
3. Technical Governance & Privacy
API credentials remain server-side, generated artifacts are drafts for clinician review, and simulated benchmark recordings are kept separate from the public application repository. Production deployment still requires formal security, privacy, monitoring, and governance controls.
4. Project Team
Owner: Ermias Amare
Clinical experts: Rahel Tamiru and Hiwot Shewangizaw
AI & ML research: Melaku Bayu
Team roles support product development and evaluation; they do not represent regulatory approval or autonomous-care authorization.