According to a recent LinkedIn post from Ro, the company is highlighting the use of an in-house large language model–based tool to support side-effect triage for patients. The post describes how this AI-driven system is intended to identify and route patient-reported side effects to the appropriate care team.
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The LinkedIn post reports that, after implementation of the tool, median response times fell from under two hours to roughly 33 minutes, with urgent messages reportedly addressed in under 26 minutes. The company also cites more than a 70% improvement in care team response times across 24/7 operations.
For investors, the post suggests Ro is investing in proprietary AI infrastructure aimed at improving clinical operations and patient experience, which could enhance scalability and unit economics in a telehealth model. Faster triage and more efficient clinician workflows may also support higher patient retention and differentiation versus other digital health platforms.
If sustainable, the performance metrics described could indicate potential for lower support costs per patient encounter and better utilization of medical staff. However, the post does not provide details on regulatory considerations, clinical outcomes, or the financial magnitude of the investment, leaving uncertainty around the near-term impact on profitability and required ongoing AI spend.
Within the broader digital health landscape, the focus on an internal LLM-based system may signal a preference for owning key technology rather than relying solely on third-party tools. This approach could position Ro to build data-driven defensibility over time, but it may also entail higher R&D overhead and execution risk compared with off-the-shelf solutions.