「Google Cloud API（Cloud Healthcare API）で HL7 FHIR R4 を叩く — FHIR ストア・OAuth・ローカル検証、Google Health API との違い」にコード片と手順まとめた。

Detailed setup, OAuth, and local tests in "Calling HL7 FHIR R4 via Google Cloud Healthcare API".

Public commenters said ADT exchange is already being replaced with FHIR in some deployments and urged the group to include jail and diversion-court health data in DXF planning; vendors asked whether the work group will produce implementation guides.

The new standards work group met for an initial session to begin advising a roadmap for adopting FHIR within California's Data Exchange Framework (DXF). Participants outlined use cases, pilots and implementation barriers; DHCS and federal CMS staff were present to provide policy context.

A detailed checklist to audit and secure open-source FHIR servers, addressing vulnerabilities like unauthorized data access and memory exhaustion in HAPI FHIR.

Panelists described query-and-response pilots (Helios/HL7 accelerator and eHealth Exchange intermediaries) that let public-health departments call back for updated case information rather than receiving static snapshots. Cedars-Sinai and Stanford were named as early California participants.

EMS participants said FHIR's ability to perform layered, targeted queries could supply time-critical, curated information (allergies, current meds, last-seen) to ambulance crews and avoid overwhelming providers with full historical records. Panelists noted misalignment between existing EMS standards and FHIR.

Speakers named prior authorization and referrals as strong FHIR use cases and noted a related federal prior-auth interoperability requirement that takes effect on 2027-01-01. Panelists cautioned that mandates alone may produce checkbox compliance unless paired with business incentives and support.

Many digital projects shine at launch… then fade. Libello has been in daily use for 18 months. Still in the field, still used, still growing — new features and new modules, to cover ever more needs. ...

Introduction: The deployment of agentic AI within clinical electronic health record (EHR) environments represents a critical paradigm shift in health IT, moving from static query systems to dynamic, code-executing autonomous workflows. In high-stakes medical contexts such as inpatient vancomycin dosing—where precision is essential to prevent nephrotoxicity or clinical failure—system autonomy must be balanced against rigorous safety constraints and robust data integration.