From prompt to patient.

PromptBefore AI reaches a patient:is it safe, does it fit the workflow,and what happens when it is wrong?

We help clinics and practices put AI to work, and help AI companies survive the bedside.

See AI where it has to work.

A small clinic in daylight: an agent's teal line at the front desk, routine calls stacked as done, refills marked to sign on the clinician's desk, symptoms at the nurse's station.

A clinic’s Monday morning

An agent sorts the calls. Anything clinical goes to a person.

For clinics and practices

A small, brightly lit box inside a large, dark emergency department: a triage desk, a waiting room, beds and an exit around it.

What the benchmark missed

A triage AI meets the rest of the emergency department.

For AI builders

AI fails where nobody measured.

We work in those places every shift.

One path, with an exit at every step.

Clinics, practices and AI companies follow the same path. Only the build or validate step differs.

30 minutes, free

Triage call

We hear the problem and decide together whether we are the right fit. If we are not, we tell you who might be.
1 to 2 weeks

Map

We map the workflow end to end and score every place it can fail.

You get: workflow map, risk register
One meeting

Decide

Go, fix first, or stop. Written down, with reasons.
- Go
- Fix first
- Stop
You get: a written decision. Stop is a real outcome.
2 to 6 weeks

Build or validate

Clinics: we set up, connect, and test the tools. AI builders: a clinical validation sprint against a real workflow.

You get: a working system, or red-team findings
Monthly

Run and measure

We maintain, tune, and report what changed.

You get: a monthly review

Two emergency physicians. Still on shift.

Portrait of Dr. DJ Apakama — **Donald (DJ) Apakama, MD, MS, FACEP**Board-certified emergency physician and NIH-funded researcher with a master’s in biomedical informatics. Studies how AI performs in emergency care: clinical decision support, fairness across patient groups, and AI agents built for clinicians. Practices in two high-volume safety-net emergency departments. Co-founder.
Email: dj@prompt2patient.ai

Portrait of Dr. Ethan Abbott — **Ethan Abbott, DO, MSCR, FACEP**Board-certified emergency physician and NIH-funded researcher. Contributing physician in OpenAI’s HealthBench evaluation, helping evaluate the accuracy and quality of model-generated health-related responses to improve the safety of AI models on health-related questions. Co-founder.
Email: ethan@prompt2patient.ai

Contributing PhysicianOpenAI HealthBench Evaluation
NIH-fundedBoth founders hold NIH research funding
STAT AIHosts of an emergency medicine podcast on AI

Red teaming

Synthetic red team · Clinics and AI builders

An AI scribe on a full shift

We ran a made-up AI scribe through one busy shift and found five things to test before you trust its notes.

Room 4
The scribe wrote the note before the CT came back. The CT changed the plan to admission, but the note sends her home.
Test: visits where the plan changes after the scribe has written the note.
Room 7
The patient spoke through a phone interpreter, and her son said she fell twice this week. The note says she denies falls.
Test: interpreter and family visits, scored separately against the recording.

Go
Fix first
Stop

Synthetic red team · Clinics

An AI reply drafter in a clinic inbox

Five drafts from a made-up AI reply drafter needed a fix before sending. Each has a test your clinic can run.

Go
Fix first
Stop

Synthetic red team · AI builders

An AI answer assistant on the night shift

A made-up AI answer assistant fielded one night shift’s questions and got five wrong in ways a builder can test.

Go
Fix first
Stop

New to clinical AI? Start with STAT AI, the emergency medicine podcast we host.

Book an AI Triage Call.

Thirty minutes, free. Bring the workflow, the tool, or the claim you need to defend. You leave with a straight answer on fit and a next step, even if the next step is not us.

Book a call

Or write to hello@prompt2patient.ai