Metriport Raises $26M for Open Source Patient Records
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Metriport has raised $26 million in a Series A led by TJ Parker, General Partner at Matrix, with ARTIS and Y Combinator participating. Total funding now stands at $28.4 million.
The company works from San Francisco. Its base sits in California, and it builds open source data infrastructure for healthcare, and its purpose is to take fragmented medical records and turn them into structured, searchable information a clinician can actually use while a patient is sitting in front of them.
Anyone who has worked in a clinic knows the current process. Providers assemble a patient history from a scattering of hospitals, pharmacies, specialists and other facilities, often receiving files in incompatible formats or waiting weeks on manual requests. The consequences are familiar too: duplicated tests, repeat specialist visits, delayed treatment and decisions made on partial information.
Metriport maintains dozens of connections into major healthcare data sources, then uses its open source code to match, extract, standardize, deduplicate and enrich what comes back. Those records land in a unified data model that can be reached through a single API, a data warehouse, or applications embedded directly inside electronic health record systems. Providers also get real time alerts as patients move through the system, along with AI generated summaries that pull relevant details forward.
Sollis Health described a case that shows the point. A patient arrived with upper abdominal pain but could not recall the details of a previous endoscopy. Physicians used Metriport to find the earlier operative report, identified an ulcer, and began treatment without ordering another CT scan or waiting on outside records.
The platform is already in use at Amazon One Medical, Sollis Health and Color Health, and the company recently earned HITRUST r2 Certification. The new capital will go toward scaling the platform and building additional AI and agentic functionality that runs on top of longitudinal patient data.
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