OpenEvidence

Daniel Nadler is a Canadian-born entrepreneur who co-founded Kensho Technologies in 2013 while a PhD student at Harvard, developing an AI-driven financial analytics platform that S&P Global acquired in 2018 for approximately $550 million. He earned a PhD from Harvard University focused on the pricing mechanisms of credit derivatives and has pursued parallel careers as a published poet with his debut collection issued by Farrar, Straus and Giroux as well as a film producer on projects including Palmer and Motherless Brooklyn. Nadler has served on boards such as the Academy of American Poets and was named to the TIME100 Health list of influential figures in global health.

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Zachary Ziegler is a machine learning researcher who was a PhD student at Harvard University in Professor Alexander Rush’s Natural Language Processing lab, where he received the National Science Foundation Graduate Research Fellowship. He completed his undergraduate studies at Cornell University and was awarded the Barry Goldwater Scholarship for excellence in the sciences. His academic work centered on the mathematical foundations of large language models prior to co-founding OpenEvidence.

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Travis Zack is a physician-scientist who earned an MD through the Harvard-MIT Health Sciences and Technology program and a PhD in Biophysics from Harvard University. He completed his internal medicine residency and Hematology/Oncology fellowship at UCSF, where he remains an Assistant Adjunct Professor conducting research at the intersection of computational biology, oncology data science, and artificial intelligence applied to clinical notes and electronic health records. His prior work has included studies bridging machine learning with cancer therapy approaches and natural language processing in medical reporting.

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Evan Hernandez holds a PhD in Computer Science from MIT, where his research in Jacob Andreas’s lab focused on understanding what deep neural networks learn from large quantities of data. Prior to joining OpenEvidence he contributed to applied machine learning efforts, including work on models for complex reasoning tasks.

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OpenEvidence is an AI-powered clinical decision support platform and medical search engine that delivers real-time, evidence-based answers to clinical questions for verified healthcare professionals. It synthesizes peer-reviewed literature, guidelines, and data from exclusive content partnerships with The New England Journal of Medicine (NEJM), JAMA Network, National Comprehensive Cancer Network (NCCN), Cochrane Systematic Reviews, Wiley, and numerous medical societies, providing cited summaries, figures, tables, and full-text access directly at the point of care. The platform operates via web and mobile apps (iOS and Android), requires NPI verification for free access to U.S. clinicians, and is HIPAA-compliant and SOC 2 Type II certified. As of mid-2026, it is used by over 650,000 licensed U.S. physicians (part of ~860,000 total verified clinicians) across more than 10,000 hospitals and medical centers, supporting over 1 million clinical questions daily on average—with a record 1 million in a single day on March 10, 2026, and tens of millions of consultations monthly (including figures around 27 million referenced in a May 2026 announcement)—with cumulative support exceeding 200 million AI-powered clinical consultations from U.S. clinicians to date. Its structural advantages include deep content moats from journal partnerships and bottom-up adoption that bypasses traditional hospital procurement cycles.

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OpenEvidence Visits is an AI-powered ambient documentation and clinical intelligence tool that records and transcribes patient encounters in real time on web or mobile, automatically generates evidence-enriched clinical notes, and surfaces relevant guidelines and research during the visit. It functions as a digital clinical assistant by integrating transcription, customizable templates, real-time evidence surfacing into assessment and plan sections, post-encounter querying of notes with full patient context, and document management for a searchable repository of patient records. Launched in August 2025 and available free to verified U.S. healthcare professionals, it extends the core platform into the patient encounter workflow while maintaining HIPAA compliance and configurable data deletion policies that exclude PHI from model training. The feature supports seamless handoff to other OpenEvidence tools for editing, literature searches, or coding, positioning it as a core workflow extension with growing adoption tied to the platform's overall physician base of hundreds of thousands of daily users.

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Coding Intelligence™ is an AI feature that automates medical billing and coding by analyzing visit transcripts and generated clinical notes to produce automatic ICD-10 diagnosis codes, CPT code suggestions with sequencing for maximized reimbursement, and E/M level recommendations complete with supporting medical decision-making (MDM) rationale written directly into the note. Launched in March 2026 and integrated directly into OpenEvidence Visits, it applies automatically at the end of each encounter based on the full clinical documentation and latest guidelines, helping physicians capture appropriate reimbursement without additional manual effort after long shifts. Available free to verified U.S. clinicians as part of the Visits workflow, it addresses structural billing complexity in U.S. healthcare by embedding coding logic into the documentation process. This extends the platform from decision support into revenue cycle management, with traction mirroring the rapid growth of the parent Visits and core platform features among the hundreds of thousands of active physician users.

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The OpenEvidence Dialer (also marketed as AI-Integrated Doctor Dialer™) is a HIPAA-secure communications suite for doctor-patient interactions that enables privacy-centric calls with customizable caller ID (hospital or practice numbers), secure messaging, faxing, and voicemail directly within the OpenEvidence app or web platform. It integrates deeply with the clinical decision AI, providing real-time evidence-based support, automatic documentation, and note generation during or after calls to maintain workflow continuity. Wide-released in February 2026 and offered with unlimited daily minutes free to verified U.S. healthcare professionals, it unifies telemedicine and communications into the broader clinical platform, reducing reliance on fragmented third-party tools while enhancing pickup rates and privacy. Adoption benefits from the platform's established physician network, extending AI assistance from in-person and search use cases into remote patient engagement with structural advantages in compliance and integration.

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DotFlows is an AI-native customization feature that enables clinicians to create and share reusable natural language prompts (extending traditional dot phrases) to tailor how OpenEvidence responds, set workflow defaults, automate tasks, and customize output formats for specific specialties or preferences. Launched in April 2026 and available free to verified U.S. clinicians, it supports community-driven libraries of prompts for personalized clinical workflows while maintaining the platform's evidence standards and citations. This extends the core platform's flexibility for diverse practice styles without requiring separate tools, building on the established user base for rapid adoption in documentation, search, and other workflows.

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Voice Mode is a hands-free, speech-to-speech multimodal AI interface integrated into the OpenEvidence platform that allows clinicians to ask clinical questions verbally and receive concise spoken, evidence-based answers drawn from the same peer-reviewed sources as the core platform. Launched in May 2026 and available free across web and mobile apps to verified users, it supports interruptions, refinements, and toggling between voice and text while providing full written transcripts and citations for verification. Designed for real-world clinical mobility (e.g., during rounds, procedures, or commuting), it addresses workflow needs where screen interaction is impractical and represents an advancement in accessible clinical decision support interfaces.

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Vera Health operates as a dedicated AI-powered clinical decision-support search engine that retrieves and synthesizes answers from over 60 million peer-reviewed papers, guidelines, and care pathways, delivering concise, cited responses with transparent evidence grading for point-of-care use by licensed clinicians. It competes directly with OpenEvidence through a similar literature-grounded Q&A model optimized for speed and auditability rather than broad conversational generation, targeting the same US physician and healthcare professional buyers seeking rapid evidence synthesis. Structurally, its freemium model for verified clinicians leverages low-friction adoption while building traction via partnerships such as with the American College of Emergency Physicians, creating durable distribution in emergency and acute care segments. Strengths include purpose-built retrieval augmented by clinician input on evidence quality hierarchies and integrations that embed within clinical workflows, positioning it as a credible threat in accuracy-focused evaluations. Weaknesses center on its relative youth as a 2024-founded entity, limiting the depth of long-term institutional contracts compared to incumbents, and potential concentration risk in reliance on broad web-scale literature scraping versus exclusive journal partnerships. Overall, Vera's focus on verifiable sourcing and calculator tools provides a structural edge in transparency that appeals to risk-averse hospital systems evaluating AI tools. Its growth trajectory reflects the broader shift toward specialized medical AI engines that prioritize citation fidelity over generalist capabilities.

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Glass Health provides an AI clinical decision support platform centered on generating differential diagnoses, assessment and plan drafts, and evidence-based answers from patient presentations, alongside ambient scribing capabilities that integrate into EHR workflows such as Epic. It overlaps substantially with OpenEvidence in serving US clinicians for real-time point-of-care reasoning and reference queries but differentiates through a more workflow-embedded, multi-feature approach that extends beyond pure search into documentation and diagnostic structuring. Durable strengths include its optimization for frontier performance on clinical benchmarks like USMLE and NEJM cases, combined with EHR integrations that create stickiness in hospital environments and a freemium entry point that accelerates user acquisition among trainees and independent practitioners. Constraints arise from its narrower emphasis on diagnostic generation relative to comprehensive literature synthesis, potentially limiting appeal in subspecialties reliant on deep guideline or oncology content, and its beta-stage enterprise scaling which leaves it more exposed to competition from better-capitalized players. The platform's clinician-designed interface and real-time ambient insights represent a structural moat in usability that could compound with broader adoption. Investors should note its positioning as a complementary or alternative layer in mixed-tool environments where OpenEvidence excels in raw evidence retrieval. This dual focus on scribing and CDS creates a more holistic workflow threat than pure Q&A tools.

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Doximity leverages its position as the dominant US physician networking platform—reaching over 85% of American doctors—to deliver Doximity Ask, an integrated AI tool for evidence-based clinical question answering, citation-linked responses from thousands of journals, documentation drafting, and administrative support. This creates high direct overlap with OpenEvidence through shared targeting of verified US clinicians for point-of-care decisions, amplified by seamless access within an existing daily-use app that lowers switching costs dramatically. Network effects from its massive verified user base form a durable distribution moat and GTM advantage, enabling rapid feature rollout and data advantages for model refinement that pure-play AI startups lack. Strengths include HIPAA-compliant design, full-text journal linkages, and multi-purpose utility that bundles clinical AI with communication tools, fostering deeper workflow embedding. Limitations include potential perception as less specialized in deep medical literature curation compared to dedicated engines, and reliance on the broader platform's engagement dynamics which could dilute focus if network priorities shift. The combination of scale and zero marginal cost distribution positions Doximity as a formidable incumbent threat capable of capturing share through convenience rather than superior standalone accuracy. Its physician-verified approach adds a layer of trust that resonates structurally with risk-conscious buyers.

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Wolters Kluwer UpToDate delivers a long-established evidence-based clinical reference platform now augmented by UpToDate Expert AI, which generates conversational answers grounded exclusively in its expert-authored, peer-reviewed content library built over decades by thousands of clinicians. It overlaps with OpenEvidence in serving institutional and individual physician customers for high-stakes point-of-care guidance but benefits from deeper integration into hospital systems and a hybrid traditional-plus-AI model that emphasizes transparency and linkage back to original topics. Structural durability stems from entrenched subscription relationships with health systems worldwide, vast curated content depth that reduces hallucination risks through closed-system design, and CME credit features that align with professional requirements. Competitive strengths include rigorous editorial processes and broad specialty coverage that provide a moat against newer AI entrants lacking equivalent human oversight layers. Weaknesses include slower adaptation to fully generative interfaces relative to agile startups and higher per-user costs that may favor freemium alternatives in resource-constrained settings. The platform's evolution into AI while retaining its core reference authority makes it a persistent threat, particularly where buyers prioritize proven institutional reliability over cutting-edge features. Its scale in global deployments reinforces barriers through data network effects in content refinement.

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EBSCO DynaMed offers an evidence-based clinical decision support solution enhanced by Dyna AI, which uses retrieval-augmented generation to provide natural-language answers drawn solely from its expert-curated, graded content sets across DynaMed and related databases. It targets similar hospital and clinician segments as OpenEvidence for rapid point-of-care evidence retrieval, with strong overlap in evidence-focused workflows but differentiated by systematic grading and integration depth in institutional settings. Durable advantages include decades of content curation expertise, transparency in sourcing and AI usage aligned with regulatory expectations, and established EHR embedding that creates switching friction for large health systems. Strengths lie in its focus on actionable, personalized decision tools and early mover status in commercial AI CDS deployments among traditional providers. Constraints involve a more structured, less fluid conversational style than pure generative platforms and potential lag in adopting the broadest web-scale literature ingestion. The combination of curated quality control and workflow integrations positions DynaMed as a credible defender in enterprise evaluations prioritizing reliability and audit trails over speed of innovation. Its parent company's broader information services portfolio adds resilience through cross-selling opportunities and sustained R&D investment.

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OpenEvidence faces material ongoing litigation risks from active disputes alleging trade secret misappropriation and related claims against competitors, including a June 2025 suit in U.S. District Court for the District of Massachusetts against Doximity Inc. and its executives for purported prompt injection attacks by employees posing as physicians to extract proprietary AI code, alongside breach of contract, CFAA, and DMCA claims; a parallel February 2025 suit against Pathway Medical (later acquired by Doximity, with that case terminated in October 2025) on similar grounds; and counterclaims from Doximity accusing OpenEvidence of defamation and employee poaching attempts. These disputes, which have seen motions to dismiss, partial claim advancements by judges allowing most of OpenEvidence's claims to proceed alongside Doximity counterclaims in January 2026 orders, and continued docket activity into mid-2026, create substantial legal costs, management distraction, and precedent-setting exposure in a nascent area of AI reverse-engineering law that could weaken OpenEvidence's ability to protect its core model and data extraction defenses. The intensity reflects direct competitive pressure in the clinical AI decision support space where OpenEvidence claims superior engagement. No concrete, citable offsetting factors such as resolved claims, insurance recoveries, or demonstrated insulation from further suits appear in available reporting.

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OpenEvidence withdrew its platform from the EU and UK markets in April 2026, citing mounting regulatory uncertainty around the EU Artificial Intelligence Act and its treatment of AI systems influencing clinical decisions, limiting access for European clinicians and signaling structural barriers to geographic diversification beyond its primary U.S. focus. This exit by a company processing millions of monthly U.S. consultations highlights vulnerability to evolving high-stakes AI regulations that classify clinical decision support tools under stricter oversight regimes, potentially increasing compliance costs, requiring product modifications, or inviting similar scrutiny from U.S. bodies like the FDA for software as a medical device. The decision reflects a calculated prioritization of U.S. operations amid ambiguous rules on transparency, risk classification, and liability for AI outputs in healthcare. No specific mitigating contracts, regulatory clearances, or diversified international revenue streams are cited to offset the contraction in addressable market.

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OpenEvidence's primary monetization relies on targeted advertising from pharmaceutical and medical device companies served to verified U.S. physicians on its free freemium platform, with claimed high CPMs and rapid scaling to substantial annualized revenue run-rates exceeding $100 million, but this model introduces structural exposure to conflicts-of-interest perceptions, potential regulatory restrictions on pharma promotional activities within clinical decision tools, or shifts in advertiser spending that could pressure margins or user trust. The approach—separating ads from clinical content per company policy—has enabled bottom-up adoption across over 10,000 hospitals and a large share of U.S. physicians, yet any erosion from accuracy concerns, ad influence allegations, or broader healthcare marketing reforms would directly impact the economics supporting its high valuation trajectory. Enterprise licensing and API paths exist as supplements but remain secondary based on reported emphasis. Concrete offsets like long-term ad commitments or diversified non-ad revenue at scale are not detailed in available sources.

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OpenEvidence exhibits structural key-person risk centered on co-founders Daniel Nadler (CEO, with substantial equity ownership following prior Kensho exit) and Zachary Ziegler (CTO), whose combined leadership has driven content partnerships, product development, and investor relations through multiple large funding rounds in under a year; with reported team size in the range of 50-80 employees supporting ambitions for AI agent expansion and enterprise penetration, succession or departure scenarios would pose acute execution and continuity challenges in a specialized medical AI domain requiring deep domain and technical expertise. The founders' prior experience and personal stakes anchor the company's vision and momentum, but this concentration is amplified by the startup's youth (founded 2021-2022) and high-velocity growth trajectory. No publicly detailed succession plans, key-person insurance, or broadened executive bench with equivalent influence are referenced in sources.

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OpenEvidence's core value proposition depends on multi-year licensing agreements for full-text, figures, and guidelines from partners including NEJM Group (announced February 2025) and the JAMA Network (multi-year deal signed June 2025), plus NCCN and others, which supply the authoritative, hallucination-resistant data underpinning its AI responses; any non-renewal, material cost escalation, or disputes over usage rights could degrade answer quality, citation completeness, or differentiation versus competitors. These partnerships have been central to achieving high physician adoption and perfect USMLE scoring claims, yet the model carries renewal and pricing risks as publishers increasingly recognize AI training value. Sources note potential for rising licensing fees to pressure economics without offsetting long-term exclusivity or volume-based protections being specified.

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A June 2026 Nature Medicine study by NYU Langone researchers found that general-purpose models (Gemini 3.1 Pro, GPT-5.2, Claude Opus 4.6) outperformed OpenEvidence and UpToDate Expert AI across MedQA benchmarks, HealthBench alignment, and—most notably—100 real-world de-identified physician queries scored by 12 blinded US clinicians on correctness, completeness, safety, and clarity. Eric Topol, a prominent physician-scientist, called the results unanticipated in a widely shared post, noting general models' edge even on practical clinical tasks. Physician Joshua Liu analyzed the paper in detail, acknowledging study limitations like small rater sample size and potential training data contamination on benchmarks, while sharing his own experience that general models like Gemini often match or exceed OpenEvidence for many queries; he predicted limited adoption impact due to liability, BAA/HIPAA needs, and curated evidence access unique to specialized tools. Other independent clinicians split: radiation oncologist Brian Lawenda MD reported OpenEvidence superior for detailed, referenced answers on complex cases, while several MDs on X echoed that frontier models excel at reasoning, nuance, and clarity. Post-publication discussion among physicians has continued to highlight methodological questions (e.g., prompting/API differences, citation accuracy not evaluated) alongside the core findings, with some voices noting the debate underscores needs for better real-world evaluation frameworks rather than immediate practice changes. The study and responses generated extensive discussion, with some voices noting OpenEvidence's strengths in citations from peer-reviewed sources were not the primary evaluation focus.

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Practicing physicians across specialties frequently describe OpenEvidence as a transformative, frictionless tool for point-of-care questions, often superior to UpToDate in speed, search flexibility, and up-to-dateness. In NBC News interviews with over two dozen independent doctors (May 2026), infectious disease specialist Dr. Paul Sax called it "borders on miraculous" for customized answers unmatched by prior gold-standard tools; Harvard's Dr. Anupam Jena noted exponential voluntary adoption (nearly two-thirds of US physicians) for clinical decision support on comorbidities and non-specialty topics; and Sanford Health CMO Dr. Jeremy Cauwels highlighted its phone-friendly ease and time savings over other methods. X posts from physicians like hematologist @Papa_Heme praised its handling of complex differentials and plans (including recent complex case uses), while YouTube comments from MDs and primary care doctors called it a daily "game changer" or "amazing" for confirming conclusions and quick medication/side-effect lookups. Reddit threads in physician communities echo its value for synthesizing literature faster than manual searches, though adoption is framed as supplementing—not replacing—judgment. Recent clinician posts continue to affirm its practical utility in real workflows alongside the benchmark discussion.

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A recurring minority view among users highlights limitations including occasional hallucinations or overly strong conclusions on rare/edge cases, generic or rigid outputs, and concerns that heavy use could erode critical thinking or verification skills. NBC News reporting noted expert worries about incomplete answers and specific clinician examples of flubs (e.g., exaggerated medication risks). Reddit threads in r/emergencymedicine, r/hospitalist, r/physicianassistant, and r/Residency include reports of hallucinations on uncommon topics, preferences for general LLMs or alternatives due to better processing of complex problems, and titles like "Stop using Open Evidence" or "Alternatives to OpenEvidence?" citing citation and genericity issues. A Sermo poll context reflected broader physician caution about AI in high-stakes decisions, with most viewing tools as supplements rather than proven replacements. App and review feedback occasionally echoes specific recommendation concerns, such as outdated or debunked study references. The recent benchmark study has amplified some of these cautionary notes around evaluation rigor and real-world applicability.

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