Interoperability and AI: Industry Perspectives and Best Practices

Why It Matters

Effective interoperability is the linchpin for reliable AI outcomes, directly influencing cost efficiency and patient care across the healthcare ecosystem.

Key Takeaways

• Interoperability remains prerequisite for trustworthy AI models
• Robust data platforms lower AI deployment risk
• Ethical AI governance drives stakeholder confidence
• Phased AI rollout maximizes return on investment
• Cloud‑native collaborations accelerate innovation cycles

Pulse Analysis

Interoperability has long been the Achilles’ heel of healthcare analytics, with fragmented electronic health records and disparate data standards hampering the training of reliable AI models. As regulators push for greater data exchange, organizations that invest in standardized APIs and cross‑institutional data sharing gain a competitive edge, enabling richer datasets that improve model accuracy and reduce bias. The panel underscores that without seamless data flow, even the most sophisticated algorithms falter, making interoperability a strategic imperative rather than a technical afterthought.

Building a solid data foundation is equally critical. Snowflake’s cloud‑native architecture combined with IBM’s consulting expertise offers a blueprint for integrating legacy systems, ensuring data quality, and establishing governance frameworks that support scalable AI workloads. Modern data platforms provide elastic storage, real‑time analytics, and built‑in security, allowing healthcare entities to curate patient‑centric datasets while complying with HIPAA and emerging privacy regulations. The speakers emphasize that a well‑engineered data layer not only accelerates model development but also simplifies ongoing maintenance and auditability.

Finally, the conversation shifts to ROI and sustainable AI adoption. Organizations are urged to start with high‑impact use cases—such as readmission risk scoring or imaging triage—where measurable outcomes justify investment. Incremental pilots, coupled with clear performance metrics and ethical oversight, help demonstrate value to executives and regulators alike. By aligning AI initiatives with broader business objectives and patient‑outcome goals, providers can unlock cost savings, improve care quality, and position themselves at the forefront of the digital health transformation.

Interoperability and AI: Industry Perspectives and Best Practices

Tuesday, February 17, 2026

30 minutes

Before healthcare organizations can embark on a meaningful AI journey, they need to first consider if their data and interoperability strategy is positioning them for future success.

Join this panel discussion with industry innovators and experts from Snowflake and IBM/Hakkoda to hear best practices for embarking on your data and AI journey in healthcare.

Learning Objectives

During this session, subject‑matter experts will discuss:

• Perspectives on the state of interoperability and AI maturity in the industry

• Best practices for creating a robust data foundation to underpin your downstream AI initiatives

• Key considerations for getting started with AI programs to maximize ROI

The views and opinions expressed in this content or by commenters are those of the author and do not necessarily reflect the official policy or position of HIMSS or its affiliates.

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Speakers

Cathy Reese

Senior Partner, Data & AI Leader for Healthcare, Life Sciences, State & Local Gov, Education, IBM Consulting

Cathy Reese is a Senior Partner and Data & AI Leader at IBM Consulting, overseeing Healthcare, Life Sciences, State & Local Government, and Education across the Americas. She has 20 years of experience helping public and private sector organizations apply data, automation, and AI to improve decision‑making and outcomes. Her work focuses on modern data platforms, ethical AI, and driving real adoption at scale.

Jesse Cugliotta

Global Industry GTM Lead, Healthcare & Life Sciences, Snowflake

Jesse Cugliotta is the Global Industry Lead for Healthcare & Life Sciences at Snowflake. He has 20 years of experience working with clients in the Healthcare & Life Sciences industries with a specific focus on driving new capabilities with data. He holds a graduate degree in Engineering from the University of Pennsylvania and has spent his entire career in Data & Analytics.