Anticipatory Medicine Is Here: Why Pharma’s Next Competitive Edge Lives Inside the EHR

The new context for medicine: the EHR as operating system

For a century, breakthroughs like antibiotics, imaging, and genomics redefined the practice of medicine. Today, the defining context is digital: the electronic health record (EHR). What was once a passive repository is becoming an active, predictive operating system for care -- where ambient documentation, AI agents, and population-scale models shape how health care providers (HCPs) document, decide, and deliver care.

For commercial, insights, and analytics leaders in pharma, biotech, and medtech, this shift isn’t an IT side story; it’s a go-to-market reality. The next decade of influence will be won or lost where clinicians click -- inside AI-augmented EHR workflows that prioritize anticipatory care over reactive care.

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Why scale matters: platform gravity and Epic’s momentum

Scale is strategy in platform markets. In 2024, Epic recorded its largest net gain in hospital market share on record, adding 176 facilities and nearly 30,000 beds, widening its lead over Oracle’s Cerner business, per KLAS-cited reporting. That scale compounds: as more systems adopt Epic, more decisions, documentation, and patient engagement occur inside Epic workflows, thus accelerating feature adoption and shaping clinical norms.

This matters to biopharma industry leaders because influence follows adoption. If AI-driven suggestions, documentation, and order sets surface inside the EHR, then brand strategy, evidence strategy, and access strategy must adapt to the logic of those systems.

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From rule-based flags to simulated futures: a decade-long arc

Long before today’s foundation models, clinical teams were already using EHR data to reliably phenotype conditions such as hypertension, combining codes, meds, vitals, and NLP to improve identification accuracy. That early work foreshadowed the current leap: moving from static rules to sequence models that learn from longitudinal patient journeys.

Epic’s new Comet exemplifies the step-change. Trained on time-ordered clinical events drawn from Epic’s Cosmos network, Comet simulates plausible patient futures -- estimating risks like readmission, ASCVD, extended length of stay, or emergence of conditions (e.g., pancreatic cancer). Early evaluations suggest Comet outperforms many single-task models across diverse scenarios. The shift is profound: from best guesses to probabilistic scenario planning at the point of care.

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Interoperability becomes infrastructure: TEFCA via Nexus

Another accelerant: interoperability that actually works at scale. More than 1,000 hospitals and 22,000 clinics using Epic are now live on the Trusted Exchange Framework and Common Agreement (TEFCA) through Epic Nexus, which has been designated as a Qualified Health Information Network (QHIN) by the federal government.

TEFCA is the nationwide framework, created under the 21st Century Cures Act, that standardizes how health information is securely exchanged across providers. By lowering long-standing barriers to interoperability, especially for rural and underserved communities, TEFCA reduces fragmentation and ensures a more complete picture of the patient journey is available at the point of care.

For pharma, biotech and diagnostic companies, this shift means access to cleaner, broader, and more timely data. That data becomes the raw material for real-world evidence (RWE) studies, for structuring outcomes-based contracting with payers, and for more precise targeting of care gaps where interventions can make the greatest impact.

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Agentic AI lands in workflows: Art, Emmie, Penny

Epic’s recent wave showcases how agentic AI embeds directly in clinician and patient experiences:

• Art (clinician assistant): ambient documentation and charting; pre-visit prep; smart summaries; answering questions in-workflow.
• Emmie (patient concierge): inside MyChart, helping interpret results, propose appointments, and nudge evidence-based next steps.
• Penny (revenue cycle): coding suggestions and denial appeal letters -- migrating administrative friction out of HCPs’ day.

Together, these agents reshape attention: what surfaces, when it surfaces, and how it’s acted upon. If you’re not optimizing how your therapy appears to an AI colleague sitting beside the clinician, you’re competing with one hand tied behind your back.

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Proof the “last mile” is real: ambient AI is going enterprise

It’s not just the EHR vendors. Health systems are moving fast to offload documentation and streamline workflows with ambient AI scribes. Case in point: Ardent Health, a 30-hospital system, piloted Ambience Healthcare across 17 specialties and seven languages and reported:

• ~45% reduction in documentation time (Epic UAL data) and ~5 hours/week saved per clinician,
• 70% of clinicians felt reduced cognitive load and better focus,
• 100% of pilot clinicians continued using it long-term, with 90% encounter utilization, and
• Gains across all eight Press Ganey patient-experience categories.

Leadership’s takeaway: “Don’t take this away.” When ambient AI removes clerical burden and fits the workflow, adoption can go viral. That adoption is the on-ramp to broader agentic automation where recommendations, orders, and coding are increasingly AI-mediated.

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Oracle’s counter-thesis: “AI-first” EHR and an open agentic stack

Meanwhile, Oracle is making an assertive play. The company announced a next-gen, “AI-first” EHR rebuilt on Oracle Cloud Infrastructure and a semantic database designed for real-time agentic AI -- paired with a knowledge graph to unify clinical and payer context. Their pitch: AI that’s built-in, voice-first, and open for third-party agents and models, with native capabilities spanning prior auth, real-time claims adjudication, patient portal AI explainability, and even embedded clinical trials (with ambient capture) planned on the roadmap.

Market share data show Oracle has ground to regain, but the technical direction -- agents working off live, evergreen data with payer and clinical rules -- signals where EHRs are headed as a category: from databases to reasoning platforms.

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Key implications for pharma, diagnostics, and medtech

The commercial playbook must bend to a world where AI intermediates attention:

1. Design for the agent, not just the HCP. Your clinical narrative, evidence, and safety context must be machine-readable and workflow-ready so that an AI assistant can retrieve, summarize, and surface it at exactly the right moment (e.g., at order entry, after a lab result, during prior auth).
2. Treat CDS surfaces like premium shelf space. Clinical decision support (CDS), order sets, and in-basket guidance are becoming precision retail endcaps. The question is no longer, “Did the rep deliver the message?” It’s “Did the EHR present the right evidence to the right clinician, in the right context, with minimal clicks?”
3. Partner on interoperability-driven RWE. TEFCA’s data liquidity and Cosmos-scale networks enable faster, broader observational studies. And soon, simulation-based scenario testing. Co-develop pragmatic evidence packages that feed the same models clinicians will rely on.
4. Win the prior-auth and revenue-cycle battle upstream. With agents like Penny (Epic) and Oracle’s autonomous reimbursement, coverage rules and coding logic move into the foreground. Equip your access teams with AI-ready dossiers that map indications, codes, payer policies, and outcomes evidence to reduce friction the moment an order is placed.
5. Reimagine patient engagement inside the portal. MyChart and Oracle’s upgraded portal now explain diagnoses and results in plain language and propose next best actions. Patient education isn’t a standalone website -- it’s an EHR-native, AI-mediated dialogue. Align your patient materials to that conversational context.
6. Embed clinical trials where clinicians actually work. As EHRs integrate trial matching and data capture (voice-first, ambient), the distance from clinic visit to trial enrollment shrinks. Sponsor systems that auto-surface trial options when a patient’s profile matches inclusion criteria; design protocols to minimize workflow drag.
7. Measure what matters in the new funnel. Replace vanity “reach” metrics with EHR-proximate KPIs: CDS exposures, order set uptake, time-to-therapy, prior-auth pass rates, and adherence trajectories -- segmented by site, specialty, and equity outcomes.

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A practical roadmap to get started (next 6–12 months)

EHR Readiness Audit

• Map your top 5 brands (or indications) against the EHR touchpoints that govern decisions (labs, imaging, prior auth, order sets, CDS hooks, in-basket messages).
• Identify gaps: where does your evidence fail to surface at the moment of decision?

AI-Consumable Evidence Packaging

• Convert pivotal data, guidelines, dosing, monitoring, and safety into structured, linkable artifacts (think: FHIR-adjacent schemas, concise Q&A, and machine-fetchable citations).
• Pilot with 1–2 health-system partners to validate retrieval quality inside agent workflows (e.g., Art, ambient scribes).

Access & Rev Cycle Integration

• Co-develop payer-specific authorization playbooks that AI agents can use to suggest codes, documentation phrases, and medical necessity logic.
• Instrument denial analytics to learn which documentation elements agents must emphasize.

Portal-Native Patient Education

• Rewrite patient-oriented materials for agent-friendly explainability: short, structured answers that map to likely follow-ups in portals like Epic's MyChart or Oracle’s patient app.
• Test comprehension and intent-to-act within portal flows (screening prompts, refill nudges).

EHR-Embedded Trials

• Prioritize 1–2 protocols for EHR-embedded identification and ambient data capture.
• Align inclusion/exclusion logic with real-world chart data (less bespoke data entry, more signals already in the EHR).

Governance, Privacy, and Bias

• Work with health-system partners on model governance: performance by subpopulation, explainability at the point of care, and escalation paths when model guidance conflicts with clinician judgment.
• Make equity a first-class KPI—measure, report, improve.

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What to watch next

• Simulation-first care planning: As Epic's Comet-type models mature, care teams will plan against ranges of futures (not single predictions). That favors therapies with clear risk-benefit narratives mapped to specific trajectories and co-morbidity profiles.
• Agent marketplaces and toolboxes: Expect growth in EHR-native “app stores” for agents (documentation, coding, prior auth, adherence coaching) -- with governance rails and standardized APIs.
• Voice-first clinical trials: If Oracle’s roadmap holds, trial participation becomes a workflow, not a referral. Sponsors that design for ambient capture will compress timelines and broaden access, especially in community settings.
• Interoperability beyond treatment: TEFCA will expand beyond treatment to public health and patient access at scale, enabling richer longitudinal evidence streams.

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The call to collaborate

This is a moment of convergence: interoperable infrastructure, ambient automation, and foundation models trained on billions of clinical events. The winners in life sciences will be those who co-design with EHR platforms and health systems -- not as vendors vying for clicks, but as partners building the new language of clinical decision-making: predictive, explainable, workflow-native.

The story isn’t “AI versus clinician.” It’s AI with clinician -- inside the EHR, at the moment of decision, tuned to the realities of coding, coverage, and capacity. Ambient documentation eases the day; agentic assistants elevate the decision; interoperability stitches the journey. For pharma, biotech, and medtech, the mandate is clear:

• Design for the agent.
• Prove value along the trajectory, not just the visit.
• Make access logic first-class content.
• Meet patients where they already are: in the portal, in plain language.

Anticipatory medicine isn’t hypothetical. It’s live, NOW, in pilots, rolling out across networks, and diffusing through agentic tools that HCPs and patients actually like using. The strategic question for our industry isn’t “Will EHR AI change prescribing?” It’s how quickly...and whether your brand, evidence, and access strategy are already wired for the AI colleague in the room.

Let’s build for that reality.