Where AI Is Really Showing Up in Academic Medical Centers
Academic medical centers (AMCs) sit at the intersection of care delivery, research, and education. That complexity makes them natural laboratories for artificial intelligence – and also makes it harder to separate what’s real from what’s still aspirational.
I co-created this piece with “Veronica” to lay out how major health systems and AMCs are deploying AI today in business-support and operational domains: patient access, call centers, the digital front door, marketing and growth, revenue cycle, enterprise operations, and documentation. It draws on public announcements, vendor case studies, and early results from leading institutions, with links so your teams can dig deeper.
Why Operations-Focused AI Matters for Academic Medicine
Most of the headlines focus on clinical AI – imaging models, decision support, specialty tools. But the clearest, near-term ROI is showing up in operational and administrative workflows:
- Documentation complexity in teaching environments, with residents, fellows, and attendings in the room.
- Intricate revenue cycles blending commercial and government payers, GME funding, and research billing.
- Clinical trial recruitment, where AI-driven matching can materially accelerate enrollment and improve diversity.
- Workforce sustainability, as AMCs work to reduce burnout while sustaining education and research missions.
For AMC leaders, the strategic question is no longer “Should we invest in AI?”
It’s “Which AI investments directly relieve our highest-pressure operational constraints?”
1. Patient Access & Call Centers
What AI Is Doing Now
In access centers and call hubs, AI is being used to:
- Automate common intents via virtual agents.
- Route calls based on intent and complexity, not just phone trees.
- Offer self-service scheduling and status checks.
- Provide real-time knowledge assistance to live agents.
Notable Deployments
Microsoft Copilot Studio healthcare agents
- Cleveland Clinic participated in a private preview to build agents that answer patient questions and navigate services.
- The healthcare agent service formally launched in October 2024.
- It is now in public preview with healthcare-specific safeguards.
Epic Cheers call management
- Yale New Haven Health used Epic Cheers to activate more than 9,000 patients on MyChart in under five months.
- Denver Health later reported 19,000 activations over nine months.
- Epic positions Cheers as a combined CRM and call management platform.
Microsoft/Nuance contact center AI
- Microsoft and Nuance are using AI to deliver real-time knowledge assistance for agents, including in healthcare contact centers.
Cedar’s AI voice agent
- Cedar has launched an AI voice agent focused on billing questions, with personalized financial pathways and self-service options.
What’s Different for AMCs
Academic access centers handle a different mix of demand: clinical trial inquiries, research-related questions, complex specialty referrals, and second opinions. That requires more sophisticated intent classification and routing – and integration with research recruitment systems and specialty service-line workflows, not just generic call deflection.
2. Digital Front Door & Messaging
What AI Is Doing Now
AI increasingly sits between patients and clinicians in digital channels, helping to:
- Intercept portal/MyChart messages and categorize them.
- Answer FAQs and navigation questions.
- Route messages to clinical vs. administrative pools.
- Use ambient AI to generate drafts of notes and messages based on conversations.
Notable Deployments
Providence “Grace” and “Provaria”
- Providence uses AI messaging platforms that categorize 6–7 million patient messages a year.
- Case studies show significant deflection away from physician in-baskets.
Microsoft healthcare agents at Cleveland Clinic
- Cleveland Clinic is using Microsoft’s healthcare agents to support message deflection and navigation across their digital front door.
Ambient documentation at scale
- Northwell Health announced a system-wide deployment of Abridge’s ambient AI in October 2024.
- The rollout is part of a $1.2B unified Epic EHR program with ambient AI as a core element.
- Abridge’s announcement highlights scale and early impact.
- Providence is pursuing similar capabilities with Microsoft.
Microsoft Dragon Copilot
- Dragon Copilot blends ambient listening with traditional dictation and was introduced to the market at HIMSS 2025.
Impact on burnout
- Abridge-cited research suggests ambient AI can reduce documentation-related burnout by up to 67%, a critical factor in academic settings.
What’s Different for AMCs
Teaching workflows require AI to:
- Recognize multiple speakers and attribute content correctly.
- Reflect trainee vs. attending roles in the record.
- Support richer documentation that preserves clinical reasoning and teaching points.
For AMCs, the success metric is not just time saved, but preserving – and even enhancing – the educational value of documentation.
3. CRM, Marketing & Growth
What AI Is Doing Now
Marketing and growth teams are using AI to:
- Build predictive segments and next-best-action models.
- Orchestrate campaigns across email, SMS, portal, and paid media.
- Connect EHR, billing, and digital touchpoints into a unified consumer view.
Notable Platforms
Epic Cheers CRM
- Epic Cheers weaves together tens of thousands of data elements to guide patients through omnichannel journeys.
- Implementation resources show how health systems are using Cheers to personalize outreach and grow service lines.
Salesforce Health Cloud + Einstein/Agentforce
- Salesforce outlines how AI supports personalization and campaign optimization for healthcare.
- The same capabilities help build a unified consumer view across EHR, billing, and portal data.
- Agentforce extends AI into agent workflows for healthcare contact centers.
Privacy-first personalization
- Marketing leaders are leaning toward “privacy-first” personalization with AI, emphasizing governed environments and clear consent models.
What’s Different for AMCs
Academic centers have additional growth vectors:
- Clinical trial recruitment – identifying eligible patients and tailoring outreach for research participation.
- Specialty service-line growth – many AMCs are regional or national referral centers for complex care.
- Research and education branding – messaging must differentiate between care-seeking, trial participation, philanthropy, and academic reputation.
4. Revenue Cycle & Finance
What AI Is Doing Now
Revenue cycle is one of the most mature AI domains. Systems are using AI to:
- Predict claim denials and underpayments.
- Automate prior authorization and eligibility.
- Prioritize and route work queues by expected impact.
- Transform the patient financial experience with personalized options.
Key Platforms and Partnerships
R1 RCM + Palantir R37 AI Lab
- R1 and Palantir announced the R37 AI lab in March 2025.
- It is built on R1’s data set of 180M+ payer transactions and 550M encounters per year.
- R1 serves 94 of the top 100 U.S. health systems.
- “Agentic RCM worker” capabilities are expected to reach production in the second half of 2025.
- R37 is positioned as a new operating layer for healthcare finance with a defined platform architecture.
AKASA GenAI
- Cleveland Clinic and AKASA announced a strategic collaboration in April 2025.
- Case studies highlight improvements in POA capture and other metrics.
- Cleveland Clinic expanded the deployment to coding and CDI across the enterprise.
- Coverage in Becker’s and HFMA underscores the financial impact.
Waystar AltitudeAI
- Waystar’s AltitudeAI focuses on denial prevention, reimbursement recovery, and prior auth automation, with case studies citing major reductions in denial-prevention work time.
Cedar
- Cedar’s AI platform supports patient-friendly billing experiences, including personalized payment plans and intelligent billing agents, built on hyperscaler and communications partnerships.
Market Context
- AKASA/HFMA research cited by the AHA suggests about 46% of hospitals use AI in some part of the revenue cycle.
- Other surveys indicate that roughly 74% of hospitals use some form of RCM automation.
- HFMA polling confirms that most organizations are piloting or scaling AI in RCM, with guidance on how to apply AI responsibly.
What’s Different for AMCs
AMCs bring additional complexity:
- More intricate payer mixes, including GME and research funding flows.
- Robust financial assistance and charity-care programs.
- A need to maintain a clean separation between clinical and research billing.
Finance leaders should probe how platforms handle teaching cases, research-related services, and multi-entity structures before scaling.
5. Enterprise Operations & Planning
What AI Is Doing Now
Beyond individual departments, AI is becoming the analytical engine for system-level operations and “control rooms”:
- Optimizing surgical, inpatient, and clinic capacity.
- Forecasting demand and aligning staffing and supply chains.
- Coordinating backlogs and waiting lists across hospitals and regions.
Palantir and System-Level Planning
Palantir Foundry
- Palantir’s work with the NHS shows Foundry being used for elective wait-list management, theatre scheduling, staff rostering, and pre-op readiness.
- The NHS Federated Data Platform brings together data for system-wide planning and resource allocation.
- Chelsea & Westminster and other providers are highlighted on Palantir’s UK healthcare site.
R1 + Palantir
- Health systems partnering with R1 for revenue cycle services effectively get Palantir “under the hood” for financial and operational modeling.
UK public-sector applications
- Local authorities such as Coventry are using Palantir to support case-note summarization and operations in social services.
What’s Different for AMCs
Academic operations introduce further complexity:
- Balancing clinical volume with teaching time and academic commitments.
- Longer, more variable OR cases driven by training and research protocols.
- Rotating resident and fellow cohorts aligned with the academic calendar.
AI-driven planning tools can help align clinical access, educational experiences, and research requirements—if configured with those constraints in mind.
6. Documentation & Administrative Load Reduction
What AI Is Doing Now
Across the enterprise, AI is being used to reduce manual administrative work:
- Ambient scribing during visits.
- Auto-drafting notes, letters, and responses to patient messages.
- Summarizing long records or cross-encounter histories.
Illustrative Examples
Northwell Health + Abridge
- Northwell announced a system-wide deployment of Abridge’s ambient AI in October 2024.
- The initiative is part of a broader digital transformation anchored in a unified Epic platform.
- Abridge reports supporting over 50 million medical conversations annually.
- Business Wire coverage provides additional detail.
Providence ambient documentation
- Providence continues to expand ambient documentation as part of its AI strategy.
Waystar + Iodine Software
- Waystar’s acquisition of Iodine Software brought AI-powered documentation improvement and CDI into its portfolio.
Microsoft Dragon Copilot & Nuance DAX Copilot
- Dragon Copilot merges ambient listening with traditional dictation.
- Nuance DAX Copilot data is now integrated into Microsoft Fabric for analytics and governance.
What’s Different for AMCs
In academic settings, documentation does more than record what happened; it is a teaching and research tool:
- Notes must capture supervision, teaching points, and trainee contributions.
- Cases may contribute to registries, trials, and outcomes research.
- Complex cases often require more detailed articulation of clinical reasoning.
Any ambient or generative documentation solution for an AMC should be evaluated against educational, research, and legal standards – not just time savings.
7. Research & Clinical Trials
What AI Is Doing Now
AI is increasingly central to research operations:
- Screening EHR data to match patients with active trials.
- Predicting which patients are likely to enroll and complete protocols.
- Monitoring protocol adherence and safety signals.
- Automating data abstraction from clinical records into research databases.
Emerging Platforms
Microsoft ecosystem
- Clinical trial matching is one of the scenarios supported in Microsoft’s healthcare agent service.
- Copilot Studio for healthcare includes trial matching and recruitment workflows.
- The Microsoft Cloud for Healthcare scenario library outlines research-related use cases.
Epic research capabilities
- Nearly 800 patients created research volunteer profiles via MyChart in the same program, as described in Epic’s case study.
Why This Matters for AMCs
Clinical trial recruitment and execution are core to academic medicine’s differentiation. AI can materially reduce the manual burden of chart review, speed up enrollment, improve diversity, and tighten the connection between care and research.
8. Medical Education & Training
What AI Is Doing Now
AI is beginning to support the education mission through:
- Simulation and AI-powered clinical scenarios.
- Analysis of trainee performance and learning gaps.
- Embedding evidence-based resources at the point of care.
- Personalized learning paths for residents and fellows.
Key Integrations
UpToDate + Microsoft
- Wolters Kluwer is integrating UpToDate into Microsoft’s healthcare agent service in Copilot Studio.
- The same integration connects UpToDate with Dragon Copilot, keeping evidence-based content close to the documentation workflow.
What’s Different for AMCs
Academic centers can use these tools not only for clinical decision support, but also to:
- Track competency over time.
- Support supervision and feedback.
- Demonstrate outcomes for accreditation and GME requirements.
9. Specialty-Specific AI Applications
Where AI Is Showing Up
Specialty domains are seeing rapid AI adoption:
- Radiology – triage, reading assistance, structured measurements.
- Pathology – digital pathology for cancer detection and grading.
- Genomics – variant interpretation and treatment matching.
- Cardiology – ECG and echo interpretation, risk scoring.
- Oncology – treatment protocol matching, molecular tumor boards, trial matching.
Microsoft Imaging Foundation Models
- Microsoft introduced new medical imaging foundation models in October 2024.
- These models are available via Azure AI Studio.
- A partnership with Paige extends these capabilities into digital pathology.
Why This Matters for AMCs
Specialty AI investments should align with an AMC’s centers of excellence. The clinical, academic, and reputational value of leading in oncology, neurology, or cardiology can be amplified when specialty AI tools are integrated into a broader data and operations platform.
10. Data Governance & Research Ethics
Core Questions for Academic Medicine
AMCs face unique governance and ethics questions:
- How are clinical and research data separated and governed?
- What consents are required for AI-assisted care, research use, and secondary data use?
- How do tools handle data sharing across multi-entity systems, affiliates, and networks?
- What are the regulatory implications for AI/ML tools used in both clinical and research contexts?
- How transparent and auditable are models and training data?
Microsoft Data Governance Capabilities
- Microsoft Fabric healthcare data solutions support claims, SDOH, and conversational data.
- These tools help centralize operational and research-relevant data with appropriate controls, as noted in Fierce Healthcare coverage.
- Microsoft Purview provides healthcare-focused security and compliance templates.
Policy and Governance Frameworks
AMCs should formalize policies for:
- When and how AI-generated insights are used in research vs. clinical care.
- How AI tools are documented in IRB protocols and consent language.
- Vendor data-use agreements that address research data explicitly.
Palantir’s Role as a Data Operating System
Palantir is best understood not as a point solution, but as a data and decision backbone that can sit beneath many of these use cases:
- R1 + Palantir R37 – health systems outsourcing RCM to R1 benefit from Palantir-powered financial and workflow optimization inside R37.
- NHS system-wide planning – Foundry is being used across the NHS to align demand, capacity, and workforce across multiple providers.
- Adjacent public-sector workloads – local authorities such as Coventry use Palantir for case-note summarization and decision-support in social services.
For AMCs, Palantir is one model of a governed data operating system that can support revenue cycle, operations, research data integration, and cross-institutional collaborations—if paired with robust governance and change-management.
Strategic Considerations for AMC Leaders
What Makes AMCs Distinct
- A triple mission of care, research, and education.
- More complex cases and documentation standards.
- Higher expectations around evidence, transparency, and ethics.
- Distributed networks of clinics, affiliates, and research partners.
Questions to Ask Vendors
- How does your tool handle teaching scenarios and trainee participation?
- Can your platform support research workflows as well as operations?
- How do you maintain a clean separation between clinical and research data?
- What evidence do you have from academic medical centers or complex IDNs?
- How do you integrate with our existing research infrastructure and data warehouses?
- What governance frameworks and audit capabilities are built in?
Implementation Guidance
- Start with high-impact, operationally focused use cases (call center automation, ambient documentation, denial prediction).
- Engage respected faculty and clinical champions early in design and governance.
- Design AI programs with teaching implications in mind from day one.
- Measure both operational and educational outcomes.
- Align AI deployments with GME program requirements and learner experience.
Additional Resources
Industry organizations
- Epic UserWeb and EpicShare (customer access required)
Vendor documentation
News and analysis
How Endeavor Can Help
At Endeavor Management, we work with academic medical centers and integrated health systems to translate this rapidly evolving AI landscape into clear strategic choices, investment roadmaps, and measurable value.
If your leadership team is exploring where to start—or how to scale responsibly—we’d welcome a conversation about how to align AI investments with your unique mix of clinical, research, and education priorities.
Let’s Talk
We will help you overcome strategic challenges to realize the business value you seek.