AI-Powered Clinical Documentation and Data Infrastructure for Point-of-Care Transformation

veradigm · 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: Clinical documentation inside EHR workflows

Veradigm wanted to reduce clinician documentation burden in Practice Fusion without disrupting point-of-care EHR workflows or losing safety controls around generated clinical notes.

Constraints:

• Practice Fusion handles about 60 million patient visits per year
• clinicians spend substantial time on charting and administrative tasks
• generated notes must be grounded in patient-clinician conversations
• clinicians need easy adoption with minimal training
• gen AI cost and market fit had to be learned through real usage

Architecture

Veradigm embedded AWS HealthScribe into Practice Fusion so clinicians can record inside the EHR workflow, review transcript-backed notes, submit them, and eventually use FHIR/HealthLake plus edit analytics as the healthcare data foundation.

Diagram confidence: reconstructed (reconstructed from prose).

flowchart LR
  practice-fusion-ehr["Practice Fusion EHR"]
  healthscribe-integration["HealthScribe integration"]
  transcript-and-note-review["Transcript and note review"]
  fhir-data-foundation["FHIR data foundation"]
  edit-analytics-pipeline["Edit analytics pipeline"]
  product-feedback-loop["Product feedback loop"]
  analytics-services["Analytics services"]
  practice-fusion-ehr --> healthscribe-integration
  healthscribe-integration --> transcript-and-note-review
  transcript-and-note-review --> practice-fusion-ehr
  transcript-and-note-review --> edit-analytics-pipeline
  practice-fusion-ehr --> fhir-data-foundation
  fhir-data-foundation --> analytics-services
  edit-analytics-pipeline --> product-feedback-loop
  analytics-services --> product-feedback-loop
  product-feedback-loop --> healthscribe-integration

Components

Data flow

Clinicians start recording in the EHR workflow, HealthScribe produces real-time transcripts and generated notes, and clinicians review and submit those notes back into Practice Fusion.

Why this architecture

The system is embedded at point of care because separate ambient-scribe tooling would disrupt clinician workflow and generated notes must stay grounded in source conversations.

Models used: AWS HealthScribe generative AI service

Scale

• Practice Fusion handles about 5 million patient visits per month and 60 million per year, making workflow fit and cost learning material.
  “There are 5 million patient visits on that platform a month, 60 million a year.”transcript, stated
• Practice Fusion handles 5 million patient visits per month and 60 million visits per year.
  “There are 5 million patient visits on that platform a month, 60 million a year.”transcript, stated
• Sixty-five percent of users needed no training beyond product materials.
  “65% of our users needed no training. And I don't mean human training, I mean watching the videos or whatever.”transcript, stated
• One clinician reported completing 95% of notes immediately after seeing the patient.
  “Scribe has allowed me to complete 95% of my notes immediately after seeing the patient. Even more so than other AI ambient scribes I've tried.”transcript, stated
• Edit analytics are intended to measure changed drug names, formatting changes, edit time, and safety signals on every note.
  “we will be able to measure how many edits did they make? How long did those edits take? Did they change a drug name because we got it wrong? Did they convert a paragraph to bullets? And we can do that on every note.”transcript, stated

Alternatives considered (roads not taken)

Build a custom ambient-scribing stack before market feedback

Rejected because: Veradigm used HealthScribe as one API call to ship a safe scribe with a smaller team and learn from real usage.

Wait for perfect embedded UX before release

Rejected because: The first release used a separate EHR window even though it was suboptimal because feedback was positive and informative.

Spreadsheet-only economics before launch

Rejected because: The team needed real usage because gen AI costs real money and market fit could not be known purely from estimates.

Clinical note generation without edit analytics

Rejected because: Veradigm plans to measure every note edit as both product feedback and safety risk mitigation.

Patterns

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

Clinicians can verify transcripts and generated notes before submitting them into the EHR.

safety-policy-enforcement (safety)

Generated notes are grounded in the original conversation and include references back to transcript evidence for clinician verification.

data-quality-loop (data)

The team is building analytics over edits to understand note quality, safety risk, and provider expectations on every note.

structured-output-contract (model-ops)

FHIR provides a common structured data model for patient information and API access across healthcare systems.

Tradeoffs

How to get an ambient scribe to market quickly.

Chose HealthScribe API integration with a small team over building a full proprietary scribe stack first.

Rationale: The team could ship a safe product quickly and collect economic and quality feedback from real usage.

How deeply to embed the first release in the EHR.

Chose separate-window workflow at first over fully deep EHR integration before learning from users.

Rationale: Veradigm considered the separate window suboptimal but got good feedback because phones could serve as microphones in offices.

Gotchas (what bit them)

Ambient scribing must fit the EHR workflow rather than become a separate tool clinicians manage.

Root cause: Clinicians already lose attention to the exam-room computer and administrative work.

Resolution: Expose HealthScribe through APIs/SDKs inside the point-of-care application flow.

Gen AI economics could not be understood from a spreadsheet alone.

Root cause: Usage patterns such as encounter length, number of encounters, and satisfaction affect the cost model.

Resolution: Ship a small safe version using HealthScribe's single API call to gather real usage feedback.

Zero-edit clinical notes remain a target rather than a guaranteed current state.

Root cause: Generated notes still need clinician review and may require edits for quality and safety.

Resolution: Track edits per note and use that data as feedback toward zero-edit quality.

Evaluation

Veradigm measured adoption, user training needs, patient-focus feedback, charting-time feedback, and planned per-note edit analytics.

Metrics: 5 million patient visits per month · 60 million patient visits per year · 65% of users needed no training · 4.6 out of 5 feedback that Scribe reduced charting time while with patients

Outcomes

• The scribe was easy for many users to adopt.
• Veradigm reported improved patient focus and reduced charting time feedback.
• The team is moving toward deeper EHR integration and edit analytics.

AWS/Veradigm presentation with strong platform scale and adoption signals; clinical note quality is still being iterated toward zero edits.