Data & AI Healthcare Online schedule

At a high level, the session would explore how healthcare organizations move past isolated AI pilots and into real enterprise adoption, what that actually requires at the executive level, and where things most often break down. I typically focus on governance, accountability, and the operational realities of scaling AI across clinical and administrative environments, rather than theory. I have examples to share in healthcare and pharmacy, which are transferable to any industry, especially since healthcare is highly regulated.

• Why the industry still struggles to translate massive AI investment into measurable impact—and where the biggest gap lies.
• What clinical and operational workflows are seeing the clearest return from AI (documentation, RCM, care management, RWE) and what remains overhyped.
• What must be true for leaders to trust AI in high-stakes environments: governance, oversight, data quality, and outcome validation.
• How can healthcare enterprises scale LLMs safely and responsibly without slowing innovation?
• What priorities executives should set over the next 12 months to accelerate adoption and improve outcomes.

• How workforce shortages, burnout, and administrative burden are driving the urgency for automation—and where AI can realistically help today.
• Which automation use cases deliver meaningful ROI (coding, prior auth, scheduling, claims, bed management) and which are still immature.
• What risks arise when automating clinical or operational tasks, and how can organizations maintain patient safety and quality of care?
• How predictive analytics can support staffing, capacity management, and reducing avoidable delays.
• What guardrails and governance are needed to ensure AI augments—not replaces—clinical judgment and human expertise.

• What do current FDA, ONC, CMS, and state-level rules mean in practice for LLMs, CDS tools, and automation models?
• How should organizations manage hallucination, drift, bias, and version control—especially for clinical LLMs?
• What does a responsible AI program look like inside hospitals, payers, and life sciences companies, and who should own it?
• How can governance accelerate innovation rather than act as a bottleneck?
• What should a rigorous vendor evaluation and continuous monitoring process include in 2026?

Moderator: Jason G. Cooper, Chief Data, Analytics & AI Officer – CONCENTRA

Panelists: Bicckie Solomon, Director of Pharmacy / Residency Program Director PGY2 HSPAL - HCA FLORIDA NORTH FLORIDA HOSPITAL

Ellie Norris, Director, Business Strategy & Partnership, Clinical Development & Digital Solutions – MERCK

• What “AI readiness” actually means in a healthcare context (beyond buzzwords)
• Common gaps provider organizations face when trying to scale AI
• Data governance as an enabler, not a blocker
• Where healthcare data should live today: on-prem, cloud, hybrid, warehouses, files, and clinical systems
• The role of data quality, lineage, and ownership in clinical and operational AI use cases
• Practical lessons from building AI and data operations inside a healthcare system
• A simple readiness framework leaders can use to assess whether they are truly ready to scale AI

• How AI assistants, chatbots, and navigation tools are reshaping access, triage, and patient support—and what results are emerging.
• How personalization, segmentation, and risk stratification can improve engagement, adherence, and outcomes.
• What parts of the patient journey can realistically be automated or streamlined without losing the human touch?
• How AI impacts equity, privacy, and trust—and what must be done to mitigate widening disparities.
• What ROI metrics should leaders use to evaluate patient-experience AI investments in 2026 and beyond?

Panellists:

Panellists:

Sruthi Gopalakrishnan, Chief Data and Analytics Officer - WellBe Senior Medical

Matt Dixon, Director, Data Platform - NORTHWELL HEALTH

Mohan Krishna Mannava, Data Analytics & Business Intelligence Leader – TEXAS HEALTH

The problem:

Most companies are considering adopting AI or are piloting AI, but within weeks post-deployment usage stalls, with no efficiency gains or ROI. On the scale of months, genAI and especially agentic AI change rapidly—new tools and versions, drift, and reordered workflows—but humans adopt new tech over years, not months.

The solution: The Continuous Implementation Framework

• Treating AI as part of an adaptive operating strategy, not a one-time launch event
• Strategies for aligning leadership on the roadmap and strategy: Rethinking workflows, roles, collaboration models, and decision-making structures for an AI-enabled enterprise, and creating buy-in throughout the organization
• Strategies for aligning IT, data teams, clinicians, quality, and compliance around shared AI goals
• Adopting the right metrics and developing a plan in case usage stalls
• Creating a governance strategy that evolves as AI changes
• Collecting data beyond overall usage: Periodic employee surveys and interviews with formal feedback looks
• Creating an AI-ready workforce with ongoing staff microlearning's, formal feedback loops, and strategy readjustment
• Examples of typical AI adoption scenario vs. adoption with the Continuous Implementation Framework

Discussion Themes:

• What does a truly AI-enabled health system look like, and what capabilities will define the next generation of care?
• What a multimodal AI (imaging + clinical notes + labs + genomics) could transform diagnosis, treatment selection, and monitoring.
• How the clinician, AI partnership will evolve: What tasks shift, what remains human-led, and what becomes hybrid?
• What bottlenecks, data availability, workflow integration, reimbursement, governance—will matter most in the next 3–5 years? What bottlenecks exist, thinking about opportunities/challenges, i.e. modernized systems, efficient workflows, before building AI.
• What signals indicate an organization is ready for the next phase of AI adoption?
• What emerging capabilities could reshape patient care, research, or operations by 2030?

Geoff Montgomery, Senior Director, Artifical Intelligence, Data Operations, Network and Infrastructure, IT Security – VAIL HEALTH

Matt Dixon, Director, Data Platform - NORTHWELL HEALTH