AI-Powered Clinical Documentation with Multi-Region Healthcare Compliance

heidi_health · Healthcare · 2025 · source Agentic: no · promotional bias: med · source-backed · extracted with prompt 0.5.4 (schema v2)

Generated from extraction.json — do not edit by hand. Every claim cites verbatim source evidence; stated means the quote directly asserts the claim, implied means it supports it indirectly.

Problem

Domain: Global clinical documentation and healthcare AI validation

Heidi Health needed to scale an ambient AI scribe globally while maintaining clinician trust, local medical accuracy, and compliance across regions with different data-sovereignty and model-availability constraints.

Constraints:

Architecture

Heidi Health scales ambient clinical documentation through clinician-led evaluation, synthetic consultation data, internal review tooling, regional Bedrock/EKS model choices, strict data locality, and local clinician ambassadors.

Diagram confidence: reconstructed (reconstructed from prose).

flowchart LR
  heidi-ambient-scribe["Heidi ambient scribe"]
  synthetic-consultation-generator["Synthetic consultation generator"]
  clinical-evaluation-loop["Clinical evaluation loop"]
  internal-review-tooling["Internal review tooling"]
  regional-model-layer["Regional model layer"]
  data-locality-architecture["Data locality architecture"]
  clinician-ambassadors["Clinician ambassadors"]
  heidi-ambient-scribe --> clinical-evaluation-loop
  synthetic-consultation-generator --> clinical-evaluation-loop
  clinical-evaluation-loop --> internal-review-tooling
  clinical-evaluation-loop --> clinician-ambassadors
  heidi-ambient-scribe --> regional-model-layer
  regional-model-layer --> data-locality-architecture
  data-locality-architecture --> regional-model-layer
  clinician-ambassadors --> clinical-evaluation-loop
Drag to pan · scroll or use +/− to zoom · reconstructed from the source's described component topology

Components

Component Role Interacts with
Heidi ambient scribe Generates clinical notes and templates for clinicians across regions. Clinical evaluation loop, Regional model layer
Synthetic consultation generator Creates audio and text consultations for testing without using user data. Clinical evaluation loop
Clinical evaluation loop Clinicians review word error rate, template adherence, safety, and hallucination behavior. Internal review tooling, Clinician ambassadors
Internal review tooling Flags sessions and supports testing-environment review. Clinical evaluation loop
Regional model layer Uses Bedrock for compliant immediate availability and EKS/self-hosting for control where needed. Heidi ambient scribe
Data locality architecture Maintains strict regional data locality and network boundaries. Regional model layer
Clinician ambassadors Validate local medical practice and specialty-specific behavior in each region. Clinical evaluation loop

Data flow

The scribe is evaluated with clinicians, synthetic consultation data, LLM-as-judge tooling, and regional model infrastructure that combines Bedrock availability with EKS control.

3 supporting quotes
“With enough data, clinicians can start to do more evaluation on word error rate, template adherence check, basically making sure that the templates that clinicians made is safe, medically safe, hallucination rate check”transcript, stated
“the most recent techniques that we use is using LLM to try to create synthetic data to generate consultation both in audio form and text form.”transcript, stated
“This hybrid approach, Bedrock for speed and EKS for control, solved the model availability globally.”transcript, stated

Why this architecture

The architecture treats clinical quality and regional compliance as first-order because ordinary unit tests cannot validate empathy, diagnostic nuance, or local model availability.

“You can't just write unit tests for clinical empathy or diagnostic nuance, we needed doctors”transcript, stated
“the moment you try to expand to new regions, that luxury disappears. We suddenly had to think about other options because the models we wanted simply just not available or not compliant in those local zones.”transcript, stated

Models used: LLMs for clinical note generation · LLMs for synthetic consultation generation · LLM-as-judge evaluators · self-hosted inference models

Scale

Alternatives considered (roads not taken)

Use real user data freely for test coverage

Rejected because: Heidi uses mock consultations and synthetic data because testing cannot freely reuse user data.

“in testing environment because we can't use any user's data. So one of the ways that the doctors do is we can do some mock consultation and have case studies with Heidi users.”transcript, stated

One global model deployment strategy

Rejected because: The team uses Bedrock for immediate compliant availability and EKS/self-hosting where control or regional availability requires it.

“This hybrid approach, Bedrock for speed and EKS for control, solved the model availability globally.”transcript, stated
“When when we're talking about immediate availability, we're entering new regions. We use LM providers that are already available and compliant in that designated”transcript, stated

Ordinary unit tests as the clinical-quality gate

Rejected because: Heidi needed doctors because unit tests cannot validate clinical empathy or diagnostic nuance.

“You can't just write unit tests for clinical empathy or diagnostic nuance, we needed doctors”transcript, stated

Translation-only localization

Rejected because: Clinician ambassadors validate local practice patterns so notes feel natural to local clinicians.

“They validate that Heidi doesn't just translate words, but also understand local practice patterns ensuring the output feels natural to GP in New York or a specialist in Sydney.”transcript, stated

Patterns

human-in-the-loop-gate (safety)

Heidi uses clinicians to evaluate word error rate, template adherence, medical safety, and hallucination behavior.

“With enough data, clinicians can start to do more evaluation on word error rate, template adherence check, basically making sure that the templates that clinicians made is safe, medically safe, hallucination rate check”transcript, stated

synthetic-data-generation (data)

The team generates synthetic consultation audio and text because real user data cannot be used freely in testing.

“the most recent techniques that we use is using LLM to try to create synthetic data to generate consultation both in audio form and text form.”transcript, stated

llm-as-judge (evaluation)

Heidi introduced LLM-as-judge tooling to scale evaluation beyond what clinicians alone could review manually.

“engineers started to introduce more tooling that can help clinicians such as internal tooling for evaluating flag sessions to be reviewed than in testing environment”transcript, implied
“and also LLM as a judge tool to do it at scale.”transcript, stated

model-routing (model-ops)

Heidi uses Bedrock when entering regions quickly and EKS/self-hosted models when local control or availability requires it.

“This hybrid approach, Bedrock for speed and EKS for control, solved the model availability globally.”transcript, stated

privacy-preserving-processing (safety)

The regional architecture constrains where healthcare data is stored and how workloads communicate inside local boundaries.

“This isn't just about storage or like about strict, it's about strict data locality and network architecture.”transcript, stated

Tradeoffs

How to scale clinical evaluation without using real patient data freely.

Chose synthetic consultations plus clinician review over testing only on user data.

Rationale: The source says they cannot use user data in testing, so synthetic audio/text consultations expand coverage while clinicians still review safety.

“in testing environment because we can't use any user's data. So one of the ways that the doctors do is we can do some mock consultation and have case studies with Heidi users.”transcript, stated

How to handle regional model availability.

Chose Bedrock for speed and EKS for control over depending on one provider or one deployment model everywhere.

Rationale: The hybrid architecture lets Heidi enter regions quickly while retaining a path to self-hosted models.

“When when we're talking about immediate availability, we're entering new regions. We use LM providers that are already available and compliant in that designated”transcript, implied

Gotchas (what bit them)

Clinical output quality cannot be validated with ordinary unit tests.

Root cause: Medical note quality involves empathy, diagnostic nuance, template adherence, and hallucination risk.

Resolution: Use clinician-in-the-loop evaluation and scalable review tooling.

“You can't just write unit tests for clinical empathy or diagnostic nuance, we needed doctors”transcript, stated

Global healthcare expansion is not a single deployment problem.

Root cause: Regions differ in data locality, model availability, medical terminology, consultation flow, and regulation.

Resolution: Template infrastructure per region and use local clinical ambassadors to validate practice patterns.

“healthcare isn't a single standard. Like we encountered like four distinct layers that I can think of, of complexity that we have to solve simultaneously.”transcript, stated

Model availability breaks down outside the easiest regions.

Root cause: Preferred models may be unavailable or non-compliant in local zones.

Resolution: Use Bedrock for compliant immediate availability and EKS for self-hosted inference where control is needed.

“the moment you try to expand to new regions, that luxury disappears. We suddenly had to think about other options because the models we wanted simply just not available or not compliant in those local zones.”transcript, stated

Evaluation

Heidi evaluates clinical notes with clinicians in the loop, synthetic consultations, word-error and template-adherence checks, hallucination checks, LLM-as-judge tooling, and regional clinical ambassadors.

Metrics: 370,000 plus clinicians · 10 million consults per month · number one AI scribe by adoption in Canada

“370,000 plus clinicians on Heidi, 10 million consults per month, number one AI scribe by adoption in Canada and backed by well-known investors.”transcript, stated
“They validate that Heidi doesn't just translate words, but also understand local practice patterns ensuring the output feels natural to GP in New York or a specialist in Sydney.”transcript, stated

Outcomes

Conference talk from Heidi; adoption numbers are self-reported but the deployment and validation lessons are concrete.

“We hire clinician ambassadors in every region we operate in. These are doctors who believe in Heidi Mission and provide specific on the ground support.”transcript, stated