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Case study 04, all workClinical callback workflow

Anatomia

2025Shipped

A nurse callback and case review product where transcripts, AI triage and follow-up calls all have to stay attached to the right patient.

RoleLed the product and backend design

Team
Entropy Labs team, for a healthcare client
Timeline
2025
Stack
FastAPI, React, PostgreSQL, AWS Cognito, AWS S3, AWS KMS, Redis, OpenAI, Vapi
Source
Private, client work

What came of it

  1. 01Transcripts and recordings encrypted and access-controlled by default from the start
  2. 02AI limited to bounded jobs: summarizing, urgency scoring and drafting, with a nurse deciding
  3. 03Callbacks, recordings and follow-ups tied to one case across every role handoff

Anatomia is a care workflow product built around callbacks. A patient calls, the call is transcribed and analyzed, a nurse reviews the case, escalates to a doctor when needed, and a follow-up goes out, sometimes by an automated voice call. The product is the loop between a call coming in and a patient being looked after; the health records live elsewhere.

It was a team project for a healthcare client. I led the product and the backend design: how a case moves between roles, how transcripts and triage are handled, and how follow-ups land on the right patient. The team built it with me.

Sensitive by default

Transcripts, recordings and anything linked to a patient are treated as sensitive from the start. Encryption with managed keys, role-based access, audit logging and retention rules are part of the core design, because adding them after the fact means finding every place the data already leaked to.

AI that assists without deciding

The AI does specific, checkable jobs: summarizing a transcript, scoring urgency, labeling priority and drafting the callback. A nurse reviews every case. That split lets the AI save reading time in a clinical setting without anyone pretending the model is making clinical decisions.

Handoffs between roles

A case moves through nurse review, doctor review, waiting states and completion. Most of the ways a care workflow fails are handoff failures: the doctor sees the case without the context the nurse had, or a follow-up goes out against the wrong record. So the case carries its context with it (medications, allergies, history, the call itself) through every stage, and every state change is recorded.

Follow-up calls that know where they belong

Outbound voice follow-up only helps if the call, its recording, its transcript and the resulting state all end up on the right case. Most of that work was plumbing.

What people judge it on

People judge products like this on trust long before polish. If access rules are fuzzy, audit trails thin, or context gets lost between roles, they stop relying on the system, however good the AI is.

End of case study.

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