OpenEvidence’s latest $250M raise in September 2026 valued the company at $15B, implying approximately 50x its $300M annualized revenue. Byers Capital and Andreessen Horowitz led the round.

The financing followed three rounds in rapid succession: a $210M Series B at $3.5B co-led by GV and Kleiner Perkins in July 2025; a $200M Series C at $6.1B led by GV that October; and a $250M Series D at $12B co-led by Thrive Capital and DST Global in January 2026.

Sequoia Capital provided OpenEvidence’s first institutional financing with a $75M Series A at a $1B valuation in February 2025, announced alongside its NEJM Group content partnership. Before that, Daniel Nadler self-funded the company and raised a friends-and-family round in 2023.

With 1.5M new medical papers published every year—doubling the total body of medical literature every five years—doctors struggle to ingest the data needed to create up-to-date treatment plans. That challenge inspired the founding of OpenEvidence in 2021 as a search engine and chatbot trained on open-sourced medical papers and textbooks, purpose-built to answer complex clinical questions like "what antibiotic is best for a trach-dependent pneumonia patient with cerebral palsy"—with citations to the latest research.

The core workflow begins with physician verification through NPI scanning or hospital email confirmation, ensuring only licensed healthcare providers gain access. Clinicians then input natural language queries about patient scenarios, such as treatment options for specific conditions or drug interactions. The platform's retrieval engine searches exclusively through licensed medical content from sources like NEJM, JAMA, specialty guidelines, and drug labels. A medical-tuned large language model generates responses with inline citations, allowing users to tap on references to view original study abstracts or guideline paragraphs. The system includes built-in clinical calculators that auto-populate when relevant, enabling direct integration into clinical documentation. Color-coding indicates evidence strength levels to help clinicians assess recommendation quality.

OpenEvidence describes its AI as specialized for medical literature and medical data, with answers linked to supporting sources. The platform supports PHI input by covered entities under a HIPAA Business Associate Agreement, expanding beyond its earlier approach that excluded patient health information.

What began as a clinical search engine has expanded into a broader suite of tools layered on top of that core. OpenEvidence 2.0 extended beyond search to include administrative functions like generating prior authorization letters, patient instructions, and ICD-10 coding suggestions, along with workflow modules for order-set recommendations and discharge summary drafting, with mobile-first design optimized for bedside use during hospital rounds.

The platform's most recent layer centers on agentic and documentation workflows. DeepConsult, OpenEvidence's first AI agent purpose-built for physicians, handles multi-step clinical reasoning tasks that go beyond single-query search responses. Visits is a patient-visit workflow that transcribes encounters and enriches assessment and plan sections with up-to-date guidelines and research; it supports custom note templates, integrated editing and literature search, and lets clinicians organize and query patient documentation—free for verified U.S. healthcare professionals. Building on Visits, Coding Intelligence surfaces ICD-10, E/M, and CPT suggestions inline as clinicians document, and a partnership with Tandem brings prescription generation and prior-authorization submission directly within the platform. Rounding out the suite, the AI-Integrated Doctor Dialer expands an earlier iOS/Android calling feature into a unified HIPAA-secure communications layer that supports calling, messaging, faxing, and voicemail within the app—all with live clinical decision AI integrated throughout. Clinicians' personal phone numbers remain private, with caller ID set to their hospital or practice number, and calls can optionally generate a Visit note with real-time evidence integration. Since its limited release in late 2025, the Dialer has enabled 37 million minutes of doctor-patient communication.

OpenEvidence's enterprise footprint now extends beyond individual clinician use into health system-wide EHR deployments. Sutter Health announced plans to integrate OpenEvidence into Epic EHR workflows for its physicians in February 2026, enabling natural-language evidence search within existing clinical workflows. Mount Sinai Health System followed with an enterprise-wide deployment across seven hospitals, embedding OpenEvidence in Epic and extending access to physicians, registered nurses, and pharmacists (March 2026). Cedars-Sinai deployed OpenEvidence enterprise-wide (May 2026), extending access to physicians, nurses, pharmacists, and therapists; distinctively, Cedars-Sinai clinicians can query literature tailored to an individual patient's health profile—including prior procedures, comorbidities, medications, and allergies—pulled directly from the EHR, with patient data used only for individual care decisions and not stored by OpenEvidence. Cedars-Sinai also plans to layer its own internal care pathways and protocols into the platform, enabling clinicians to view external evidence alongside institutional guidance in a single interface.

OpenEvidence has also launched its own family of medical AI models: Osler for rapid clinical answers, Sackett for deeper evidence reviews, and Snow, the successor to DeepConsult, for comprehensive medical research reports. All three are free for verified clinicians, with responses taking roughly five seconds, 30 seconds, and five minutes, respectively. A fourth model, Darwin, targets more advanced medical research and is available through a limited research preview.

OpenEvidence provides free access to verified U.S. clinicians and monetizes through pharmaceutical and medical device advertising.

By initially requiring neither payment nor integration with patient records, it bypassed lengthy hospital procurement cycles and grew directly among physicians. That adoption has supported its expansion into EHR integrations and broader clinical workflows, with patient information now supported under HIPAA Business Associate Agreements.

The company's value delivery mechanism combines a specialized medical AI platform with content licensing agreements, creating a differentiated clinical decision support tool. Those licensing relationships span peer-reviewed journals (NEJM, JAMA), peer-reviewed biomedical literature through Wiley, systematic reviews and meta-analyses through Cochrane, and ACC-curated cardiovascular guidance through a strategic partnership with the American College of Cardiology. The content moat has since expanded across major specialty-society guidelines: a collaboration with the American Diabetes Association on Standards of Care in Diabetes, an AAO-HNSF partnership to help update otolaryngology guidelines with emerging evidence, integration of NCCN oncology algorithms, and a multi-year AUA urology guideline collaboration. Together these agreements deepen the evidence base underlying clinical Q&A responses and reinforce OpenEvidence's data moat at the point of care. The go-to-market strategy targets individual clinicians through direct app downloads and hospital system procurement. Targeted advertising to pharmaceutical companies and medical device manufacturers funds free access, while paid enterprise features represent a potential additional revenue stream.

Where UpToDate relies primarily on institutional subscriptions, OpenEvidence gives its chatbot away for free and monetizes through pharmaceutical and medical device advertising at CPMs reaching $1,000+. To strengthen its advertising infrastructure, OpenEvidence acquired Amaro, a GV-backed startup focused on advertising infrastructure and automation.

The current health system deployments at Sutter Health, Mount Sinai, and Cedars-Sinai operate on the ad-supported model; a separate non-ad-supported enterprise version is in development for large systems requiring greater customization, which would represent a distinct pricing tier above the current free and ad-supported tiers.

Epic and Oracle Health represent competitive threats through the integration of AI capabilities directly into electronic health record systems. Epic embeds generative AI into workflows such as patient messaging and clinical summaries. Its established EHR footprint gives it distribution within the systems clinicians already use.

Oracle Health's Clinical AI Agent, previously called Clinical Digital Assistant, integrates AI-assisted documentation and chart review into clinical workflows. These EHR-native tools compete for physician attention by reducing the need to switch to separate applications.

UpToDate serves more than 3 million clinicians globally, with approximately 80% of revenue coming from institutional customers. Its Expert AI product, launched in October 2025, positions it as an enterprise-grade alternative without advertising, backed by established clinical content and hospital procurement relationships.

Elsevier ClinicalKey and EBSCO's DynaMed are similarly upgrading their reference platforms with interactive chat features linked to structured medical monographs. These incumbents compete on evidence fidelity and institutional relationships but face user experience challenges compared to OpenEvidence's consumer-style interface design.

Glass Health focuses specifically on differential diagnosis generation and clinical order sets, targeting the diagnostic reasoning workflow that represents a core use case for clinical AI.

Hippocratic AI focuses on patient-facing agents for tasks such as outreach and follow-up. Its overlap with OpenEvidence is in automating healthcare workflows, rather than directly replacing physician-facing medical literature search.

OpenAI competes directly with OpenEvidence through ChatGPT for Clinicians, a free product for verified clinicians launched in April 2026. It follows the same bottom-up adoption model, with distribution through ChatGPT’s existing user base, including physicians already using it for clinical questions. OpenEvidence’s differentiation rests on its medical content partnerships, specialized models, and integration into clinical workflows.

Adjacent healthcare platforms are expanding into clinical research as OpenEvidence moves into their core workflows. AI medical scribes like Abridge are adding NEJM and JAMA research to the patient conversations they already capture, while Doximity is bundling search, note-taking, and calling around its physician network. All three are converging on an all-in-one assistant for doctors.

OpenEvidence's expansion beyond clinical search extends into diagnosis, treatment, and revenue-cycle workflows that hospitals pay significant per-seat fees to manage today.

Where the current ecosystem is fragmented—doctors jump between UpToDate for research, Lexicomp for drug data, Abridge for notes, and Cohere Health for prior auth—OpenEvidence aims to consolidate all of that into a single interface that responds in real time to complex queries, embedded directly into the clinical workflow via integrations with Epic, Cerner, and other EHR systems using HL7 FHIR APIs. These integrations unlock a new enterprise TAM where OpenEvidence can command 5–10x higher ARPU compared to its ad-supported model by moving from reference tool to real-time, in-workflow system.

The Veeva partnership to build Open Vista opens a new TAM in pharmaceutical commercial operations: clinical-trial matching, drug discovery intelligence, and adoption support for approved medicines. This positions OpenEvidence as infrastructure for life sciences companies—distinct from its physician-facing advertising model—and targets a customer segment with materially higher willingness to pay than individual clinicians.

OpenEvidence is developing its own treatments for rare cancers, expanding beyond clinical software into drug discovery. The ambition is to become an AI-native Regeneron: using a proprietary discovery platform to generate drug candidates and retain economics in those drugs, creating upside beyond its advertising business.

Regulatory liability: Clinical AI tools face increasing scrutiny from medical malpractice insurers and regulatory bodies as healthcare providers rely more heavily on algorithmic recommendations for patient care decisions. Any high-profile case where OpenEvidence's recommendations contribute to adverse patient outcomes could trigger liability claims and regulatory restrictions that fundamentally alter the company's business model and market access.

EHR integration dependence: OpenEvidence's expansion into EHR-based workflows depends on successful integration with dominant EHR platforms like Epic and Oracle-Cerner, which control clinical workflow access for most U.S. healthcare providers. These EHR vendors have strong incentives to develop competing AI capabilities internally or partner with established players like Microsoft, potentially blocking third-party integrations or demanding unfavorable revenue-sharing arrangements that undermine OpenEvidence's unit economics.

Content licensing costs: The company's competitive advantage relies in part on licensing agreements with major medical publishers—including NEJM, JAMA, Wiley, and Cochrane—but these content costs could escalate rapidly as publishers recognize the value of their data for AI training. Rising licensing fees combined with potential competition from publishers developing their own AI platforms could compress advertising-funded margins and make it more costly to maintain free clinician access.