Function Health has acquired Getlabs, a mobile healthcare platform that dispatches licensed professionals to collect blood samples at a patient’s home, office, or other location.

The integration introduces a new option for Function members: either visit one of more than 2,000 lab locations or schedule a home blood draw. The diagnostic process, lab panels, and clinical interpretation remain unchanged across both pathways.

Getlabs brings an existing nationwide network of providers, alongside logistics software that uses AI-assisted routing to optimise scheduling and improve visit reliability.

The strategic move is not about adding new diagnostics. It is about controlling how those diagnostics are delivered.

Mobile phlebotomy and at-home diagnostics explained

At-home blood testing, often delivered via mobile phlebotomy, involves trained clinicians travelling to a patient’s location to collect biological samples, which are then processed through standard laboratory infrastructure.

This model decouples sample collection from clinical settings, allowing diagnostic testing to occur without requiring patients to visit hospitals or labs.

In Function’s case, the underlying infrastructure remains tied to large lab networks, while Getlabs replaces the fixed collection point with a distributed, on-demand workforce.

The result is a hybrid system: centralised analysis, decentralised collection.

What is at-home blood testing and how does it work?

At-home blood testing allows patients to have samples collected at their location by a trained professional, rather than visiting a clinic, with samples processed in standard laboratories and results delivered digitally within a health platform.

AI-driven preventive health platforms require continuous data

Function’s core product is a subscription-based platform offering more than 160 biomarkers per year, generating longitudinal datasets across metabolic health, hormones, inflammation, and early disease signals.

The company’s broader ambition is to build a continuous “health data layer” that feeds into its AI system, integrating lab results, imaging, wearables, and medical records to generate personalised insights.

This is where Getlabs becomes strategically important.

Preventive health platforms depend on repeated, consistent data collection. Yet adherence is fragile. Travel time, scheduling friction, and access constraints are among the main reasons people skip tests.

By removing those frictions, Function is not just improving user experience. It is increasing data frequency and completeness — the raw material for its AI models.

Controlling the full diagnostic stack

The acquisition follows Function’s earlier expansion into imaging, adding AI-powered scans to its offering.

Taken together, Function is assembling three layers:

• Data generation — blood tests, imaging, biometrics
• Data collection infrastructure — labs, now extended to the home via Getlabs
• Data interpretation — AI models translating signals into actionable insights

This vertical integration matters in a category where much of the underlying infrastructure is commoditised. Most competitors rely on the same lab networks and similar biomarker panels.

Owning more of the patient journey — particularly the physical interface — becomes a differentiator.

Decentralised diagnostics and the shift to access-first healthcare

The acquisition reflects a broader shift in healthcare delivery: diagnostics moving from fixed locations to distributed, on-demand systems.

In this model, access becomes the primary constraint to solve.

Function frames at-home testing as an access issue rather than convenience, highlighting barriers such as mobility, scheduling, and geography that reduce testing uptake.

This aligns with a wider trend across digital health:

• telehealth moved consultations into the home
• remote monitoring brought sensors into daily life
• now diagnostics are following the same trajectory

The implication is that healthcare infrastructure is being rebuilt around the patient, not the clinic.

Future implications for AI-driven preventive healthcare

Over the next five to ten years, this move points to a structural shift in how health platforms compete.

First, data density will become the primary competitive advantage. Platforms that can increase the frequency and consistency of real-world data collection will train more effective AI systems.

Second, physical infrastructure will re-emerge as a strategic moat. Despite the software-heavy narrative in health tech, the ability to collect biological data at scale remains a logistical challenge. Companies that solve this layer gain control over the entire data pipeline.

Third, preventive health platforms are evolving into full-stack systems — combining diagnostics, delivery, and intelligence into a single interface.

Function’s trajectory suggests a clear direction: healthcare shifting from episodic testing to continuous, AI-managed biological tracking, where the boundary between clinic and everyday life continues to dissolve.