← Selected work

Let’s Talk Doc

Standards-aware patient communication, built as a team

A healthcare interoperability project recognized by the Global HL7 AI Challenge for Transformative Impact in Healthcare.

Team award projectAward project; official award and recipient sources verified
In this case study
Project typeTeam award project
RecognitionGlobal HL7 AI Challenge, 2025
AttributionTeam recipient for Let’s Talk Doc
Evidence statusOfficial award and recipient lists; contribution résumé-supported
One-sentence outcome

Team recipient of the 2025 Global HL7 AI Challenge recognition for Let’s Talk Doc.

01

Context and stakes

Clinical information is often technically available but difficult for patients to act on. The team explored a standards-aware communication workflow that could translate structured care context into a clearer post-visit experience.

The portfolio keeps the claim narrow: Let’s Talk Doc is the recognized project. The separate HEDIS/HL7 repository is not presented as the award winner.

02

Team and my role

HL7’s official recipient list names Shailesh Dudala, William Laolagi, and Diane Nguyen among the 2025 AI Challenge award recipients, while public team posts connect them to Let’s Talk Doc. Shailesh’s latest résumé states that he built an AI video-avatar application for multilingual post-visit follow-ups and smart intake using EHR-integrated FHIR/HL7 workflows.

Because the official project narrative does not isolate component ownership, that contribution remains labeled résumé-supported and is not presented as sole authorship.

03

System at a glance

The public-safe architecture centers on a FHIR-aware context boundary, a patient-friendly transformation layer, language and safety checks, and an explicit human touchpoint. The synthetic contract demonstrates the standard, not a production patient record.

04

Key decisions

Standards alignment matters because a useful experience has to connect to real clinical context without inventing a parallel data model. Patient-friendly output also needs uncertainty and escalation paths; fluent text is not the same as safe communication.

05

Validation and safety boundary

A credible evaluation would separate factual consistency, reading level, language quality, clinical completeness, and escalation behavior. The current public evidence does not support a claim of clinical deployment or patient outcome impact, so none is made.

06

Outcome and evidence

The verified portfolio claim is team recognition: Global HL7 AI Challenge — Transformative Impact in Healthcare Award (2025), for Let’s Talk Doc. HL7’s official winners page ties the award to the project, and HL7’s official recipient list names Shailesh Dudala. His specific implementation contribution remains résumé-supported.

07

What I would improve next

I would publish an approved contribution map, demonstration media, and a compact evaluation card once those materials are confirmed for public use.

E

Evidence and limitations

public-source

Official award source

HL7 confirms Let’s Talk Doc and the Transformative Impact in Healthcare award.

Open source ↗
public-source

Official recipient list

HL7 News names Shailesh Dudala among the 2025 AI Challenge award recipients.

Open source ↗
public-source

Team project context

Public team posts connect Shailesh, William Laolagi, and Diane Nguyen to Let’s Talk Doc.

Open source ↗
resume-supported

Individual contribution

Video-avatar and multilingual post-visit/smart-intake contribution is stated in the latest résumé; official sources do not isolate component ownership.

Limitations

  • Individual contribution is résumé-supported, not isolated by the official project narrative.
  • No clinical deployment or patient-outcome claim is made.
  • The HEDIS public repository is classified separately.

Only team-level, public-safe project framing is shown. No patient information or unverified individual contribution is claimed.