Mini Case: Community Clinic — Automating Intake Triage

A small community clinic wanted to reduce patient intake delays. The team suspected that a triage-assistant could route routine requests to self-service and flag urgent cases for staff.

Approach

They used the Pilot Intake Canvas to capture the hypothesis: "If we use an AI triage assistant, we will reduce average intake processing time by 30% within 6 weeks without increasing missed urgent cases." The squad included a nurse manager (owner), two clinicians (SMEs), an IT lead, and a compliance reviewer.

Pilot design

The pilot was limited to appointment request messages (non-clinical initial triage). Staff reviewed every automated recommendation for the first three weeks (human-in-the-loop). Data was de-identified for early testing and a privacy review was completed before any live messages.

Results & lessons

• The primary metric improved: average handling time fell by 35% for routed messages.
• A guardrail metric revealed a higher false-negative rate for a particular patient language group; the team paused expansion and added targeted training data and a fallback to human review for those cases.
• Documentation reduced operator confusion: a short runbook and a clear rollback trigger avoided hurried, ad-hoc fixes.

Outcome

The clinic adopted the assistant for low-risk channels with ongoing monitoring and a plan to re-evaluate after three months. The pilot showed value but also highlighted the need for continuous bias monitoring and language coverage before wider rollout.