A New Interoperability Strategy in the Age of Analytics and AI

Why It Matters

Without data‑ready interoperability, health systems cannot fully leverage AI, risking wasted investments and slower care improvements.

Key Takeaways

• 86% say interoperability without data prep limits AI value
• Redefine interoperability to include data preparation for analytics
• Identify platform features that support AI‑ready data pipelines
• Build a business case for upgrading interoperability solutions

Pulse Analysis

The latest HIMSS Market Insights study underscores a pivotal shift in healthcare IT: 86% of senior leaders now consider raw connectivity insufficient for extracting value from AI and advanced analytics. This sentiment reflects a broader industry realization that data must be cleansed, normalized, and contextualized before it can fuel predictive models or real‑time decision support. As providers pour capital into AI initiatives, the bottleneck has moved from algorithm development to the quality and readiness of the underlying data streams.

Legacy interoperability platforms, often built around point‑to‑point exchanges and HL7 v2 messaging, struggle to meet the rigorous demands of modern AI workloads. They lack built‑in data profiling, transformation, and governance tools needed to deliver a consistent, analytics‑grade data lake. InterSystems’ speakers highlight emerging capabilities such as automated data mapping, semantic enrichment, and scalable FHIR‑based APIs that can bridge disparate clinical and operational sources while preserving data fidelity. Organizations that adopt these features can reduce data latency, improve model accuracy, and accelerate time‑to‑insight across care pathways.

For executives, the imperative is clear: develop a business case that quantifies the ROI of an AI‑ready interoperability stack. This includes measuring cost savings from reduced manual data wrangling, revenue gains from predictive population health programs, and risk mitigation through more reliable reporting. By aligning technology upgrades with strategic objectives—such as improving patient outcomes, enhancing operational efficiency, and meeting regulatory mandates—health systems can justify the investment and position themselves for sustained innovation in the analytics era.