If you're building a digital health product, you've probably hit the same wall everyone hits: the gap between a wearable's raw signal and a clinical-grade artifact in the EHR that is actionable by a clinician.

There's a lot of plumbing in that gap: device integration, real-time ingest, signal processing, derived metrics, provenance, governance, FHIR projection, US Core conformance, and most of it is undifferentiated infrastructure that takes a year or more to build properly.

AnyBio handles the upstream half of that pipeline. Medplum handles the downstream half.

Together they give you a standards-based path from a BLE sensor on a patient's wrist to a US-Core-conformant Observation in a queryable FHIR record - and on the AnyBio side, wiring up that path is a configuration step, not an engineering project.

This post walks through what each side does and how the data is shaped, then gives you two ways in: a two-minute path that exports a synthetic demo run to your own Medplum instance with zero setup, and the full production wire-up. And - because "it works in the demo" is not the same as "I'd run this in production" - how the pipeline behaves when things go wrong.

There's a piece of conventional wisdom in health tech that won't die: FHIR is fine for interoperability, but too slow and bloated for real-time clinical apps.

The skepticism isn't unreasonable. FHIR resources can be verbose, real charts are messy, and clinical workflows don't forgive much. A physician who opens a chart and watches a spinner for five seconds has already been let down by the architecture.

More and more Medplum users are building with AI coding assistants — Cursor, Claude, Copilot, and others. We have been talking to users about what works and wrote up what we have learned in a new guide: Building on Medplum with AI Coding Assistants. This post is the short version of what is in there.

Demonstrating commitment to cybersecurity and information protection: HITRUST Certification validates the Medplum Platform is meeting rigorous cybersecurity and data protection standards through independent assessment and assurance.

San Francisco, Calif., June 1, 2026 – Medplum, a leading provider of open-source healthcare developer infrastructure, today announced its Medplum Platform residing at Amazon Web Services (AWS) has earned certified status from HITRUST for cybersecurity and information protection.

May was a heavy month for Medplum, with 100+ commits from 20+ contributors and four patch releases — v5.1.10 through v5.1.13. Scheduling was the headline: the full appointment operation suite — $find, $hold, $book, $confirm, and $cancel — landed in Alpha. The Provider App also gained medication ordering components, order set import, and operation-based claim submission. On the enterprise side, a new data warehouse export features were enabled, and the platform team shipped passwordless magic-link login, AWS GuardDuty malware protection, and a round of OAuth2 spec-compliance fixes. All of this continues to drive forward our 2026 roadmap priorities.

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Medplum stayed active this April, with 130+ commits from 20+ contributors amounting to three patch releases — v5.1.7, v5.1.8, and v5.1.9. The Provider app's billing and coverage workflows took a major step forward with integrated claim submission and live eligibility visibility. Scheduling added support for service line configurations on HealthcareService, aligning with R5/R6 FHIR conventions. Spaces, an AI-powered chat workspace inside the Provider App, now surfaces tool calls and responses directly in the chat so you can see exactly what the AI is doing. Clinical documentation expanded across intake, care plans, diagnostics, authentication, and integrations. All of this continues to drive forward our 2026 roadmap priorities.

March was yet another busy month for Medplum. Three patch releases — v5.1.2, v5.1.3, and v5.1.4 — arrived alongside over 100 commits from 20+ contributors, and the work underneath the surface was just as significant as the new features. Spaces gained richer AI tooling. The communications documentation received a full suite of guides covering everything from the data model to automations. Worker infrastructure got a substantial rethink, and WebSocket subscriptions were hardened across the board. All of it continues to advance our 2026 roadmap priorities.

One of the most common questions we hear from teams evaluating Medplum is: "Can it handle our scale?" We wanted to answer that question with real data. We recently ran a formal performance evaluation against a self-hosted Medplum deployment that mirrors one of our largest production clusters, and the results speak for themselves - peak throughput exceeding 46,000 requests per second for read-only traffic and nearly 7,000 requests per second for full read/write/search FHIR workflows, with zero HTTP failures across all scenarios.

February was a productive month for Medplum. We shipped four releases — v5.0.14, v5.0.15, v5.1.0, and v5.1.1, including a minor version bump reflecting the depth of new capabilities — with over 130 commits from 25+ contributors. The biggest themes this month were WebSocket subscription stability and performance improvements, AI-powered provider experiences, scheduling refinements, and revenue cycle tooling — all advancing our 2026 roadmap priorities.