Cynthia Miller, MD, MPH, FACP | VP, Medical Director, Access Experience Team
Greg Gregory, PhD | Executive Vice President, Partner
Sam Vaudrey | Senior Director, Innovation and Technology, Medical Communications
Here's a scenario to consider.
Your medical affairs team has spent the last two years running a pivotal cardiovascular outcomes trial. The results are strong, superior to the current standard of care. You publish in a top-tier journal, build your MSL training around the key findings, and align your publication plan to the clinical narrative you want to own.
Then a physician opens their EHR, types up a question about cardiovascular outcomes in your therapeutic area, and gets back a summary built largely around a competitor's three-year-old study.
Not because your trial was poorly designed. Not because the journal wasn't prestigious. But because the platform the physician is using hasn't indexed your study yet. Or because the query they used didn't surface it. Or because there are a dozen other reasons you have no visibility into.
This is happening. And most pharmaceutical companies aren't looking at it.
OpenEvidence, an AI-powered medical search engine designed to help clinicians find evidence-based information for treatment decisions, now reaches over 430,000 U.S. physicians daily, roughly 40% of the U.S. physician population. In January 2026 alone, it facilitated more than 20 million clinical consultations. Recently, it announced a collaboration with Sutter Health to help clinicians quickly find and use up-to-date clinical information, covering 14,000+ affiliated physicians in a single integration. The trajectory is clear.
We are not in a world where physicians are occasionally browsing an evidence app out of curiosity. We are in a world where health systems are increasingly using AI in clinical decision-making. AI-powered evidence platforms are becoming embedded in clinical workflow, surfacing curated literature at the point of care when a physician is deciding how to diagnose and what to prescribe.
For pharmaceutical companies, this introduces a new, largely unmonitored variable: how their clinical evidence appears inside the tools physicians now use to make decisions. This can impact launch, utilization, and market share.
It's important to be precise about what the risk is, because it's easy to frame this wrong.
OpenEvidence is not a promotional channel. It synthesizes clinical literature (e.g., studies, guidelines, systematic reviews) in response to physician queries. Drug names surface only when supported by underlying evidence.
The risk, then, is not that the platform is saying something unfavorable about your drug. The risk is simpler: your most important evidence may not surface prominently, or at all, when physicians search.
Indexing timelines are not always visible. Search functionality does not necessarily allow confirmation that a specific article is retrievable. Metadata structure, content partnerships, and query phrasing all influence what surfaces in response to a clinical question.
The result is a visibility gap between the evidence you generate, and the evidence physicians actually see. You may be investing heavily in evidence generation, publication planning, and MSL training without a clear feedback loop on whether the evidence your strategy depends on is discoverable in the tools physicians are using in real time.
The stakes are higher than they might appear because of when this happens.
Research consistently shows that 40% of clinical questions go unresolved due to time pressure, and primary care consultations average under eight minutes. Physicians aren't going home to read the full study. They're querying a platform between patients and acting on what surfaces.
Meta-analyses of clinical decision support tools have found that automatically providing information, rather than requiring physicians to actively seek it, is strongly associated with behavior change. An EHR-embedded evidence platform is exactly that. The answer appears in the workflow in seconds, shaping clinical thinking in a way that a journal article behind a paywall does not.
There's a second-order effect that often goes unexamined.
Traditional payer Pharmacy & Therapeutics (P&T) committees operate on formal evidence dossiers (e.g., AMCP format, health economic models, budget impact analyses). That process is well understood and has established workflows on both sides.
But hospital and IDN P&T committees may operate differently. They tend to be more clinician-driven, with less reliance on formal dossiers and more reliance on clinical peer input and accessible evidence summaries. It is a reasonable hypothesis that these committees are using AI evidence platforms as a reference point in formulary decisions.
If that's true, an incomplete evidence profile on OpenEvidence doesn't just affect individual prescribing. It affects formulary access in health systems where formulary restrictions matter.
The companies that will excel in AI are the ones that treat evidence visibility not as a one-time audit, but an ongoing part of how they manage their evidence strategy.
That means a few things in practice.
It means understanding what physicians see when they query your therapeutic area systematically, across the range of searches a physician might realistically run, and comparing that to what your medical strategy says they should see. The gap between those two things is where share is at risk.
It means optimizing the publication strategy for platform discoverability. Journal selection, abstract structure, keyword choices, and metadata affect how quickly and prominently a study surfaces on evidence platforms. It's a different discipline from traditional publication planning, but it's an increasingly necessary one.
It means equipping MSLs with an honest understanding of what physicians see when they search, so field conversations are grounded in the evidence environment the physician is navigating, not the evidence environment your team assumes they're in.
And it means monitoring OpenEvidence and similar platforms consistently, so you know when something changes, when a competitor's new study starts dominating a key query, when your recent data finally indexes, and when an algorithmic change affects how evidence ranks.
None of this is about manipulating platforms or inserting promotional content where it doesn't belong. Evidence platforms are clinical tools. Physicians trust them precisely because they're not promotional.
What we're describing is ensuring your clinical evidence, the studies you've already published, and the data you've already generated are visible, accurate, and current in the platforms physicians use. Correcting factual errors. Accelerating indexing. Optimizing metadata. Aligning field messaging with what physicians are encountering.
That's evidence stewardship. It sits squarely within medical affairs and evidence strategy, and it has a clear compliance path.
OpenEvidence is the largest player right now, but it's part of a broader shift. Clinical AI tools from multiple vendors are entering health systems (e.g., ChatGPT for Healthcare, Claude for Healthcare). Epic is integrating multiple AI partners. The EHR is becoming the primary clinical interface, and it is crucial that pharmaceutical companies understand how evidence surfaces within it.
The companies that develop the capability to navigate this before it becomes standard practice will have a meaningful advantage in how their evidence is used when it matters most.
Evidence generation, evidence synthesis, and medical communication have been core competencies in this industry for decades. The discipline that's missing is ensuring that evidence, once generated and synthesized, reaches the physicians making decisions.
Precision AQ helps ensure evidence investment translates into real-world clinical use. By bringing together EHR strategy expertise, medical communications leadership, and evidence strategy, we help clients understand how their science surfaces in AI-driven clinical workflows.
Contact our team to learn how we can help you close gaps that may undermine adoption, access, and impact.
OpenEvidence synthesizes published clinical literature, including studies, guidelines, and systematic reviews, to deliver evidence-based answers to physicians at the point of care. It only surfaces drug names when supported by underlying evidence, which means visibility depends on indexing, metadata, and search relevance.
A study may not surface because indexing timelines vary, metadata may not align with platform requirements, or a physician’s query may not match the terminology used in the publication. These factors create a visibility gap between the evidence a company generates and what physicians actually see in real time.
Physicians increasingly rely on EHR-integrated tools like OpenEvidence, which deliver evidence summaries in seconds during time‑pressed consultations. When information appears automatically in workflow, it has a greater influence on diagnostic and prescribing decisions than traditional journals or static resources.
Hospital and IDN P&T committees often rely on accessible evidence and peer input rather than formal dossiers alone, making AI evidence platforms a likely reference point. If a therapy’s most important data does not appear or is incomplete, it may quietly shape formulary outcomes and access decisions.
Teams can monitor what physicians actually see when they search, optimize publication metadata for discoverability, and ensure field teams understand platform outputs. Ongoing evidence stewardship helps close gaps that can undermine adoption, utilization, and clinical impact.