What we heard at HIMSS26 and what it means for healthcare leaders

The HIMSS Global Health Conference & Exhibition returned to Las Vegas in March 2026, bringing together thousands of healthcare leaders, clinicians, and innovators to explore the technologies and strategies shaping the future of health.

Across four days of keynotes, educational sessions, workshops, and conversations, healthcare organizations were clear in communicating their priorities:

• Accelerating AI adoption to improve decision-making and realize measurable results
• Maximizing technology ROI to protect margins and drive growth
• Modernizing data infrastructure to scale analytics and accelerate action
• Advancing interoperability to unify data and optimize performance

These priorities are not new, but urgency has intensified amid rapid advances in AI, sustained margin pressure, expanding value-based care models, and increasing demands to modernize data infrastructure to support interoperability and scalable analytics. Healthcare leaders recognize that these are now competitive requirements. The difficulty lies in achieving their goals within complex environments, fragmented systems, and constrained budgets.

Despite these challenges, achieving these goals is possible. Below, we examine how organizations are translating these priorities into operational action.

The AI conversation has evolved

Artificial intelligence was front and center at HIMSS26, reflected across keynote discussions and educational tracks focused on practical implementation, governance, and measurable impact. Healthcare leaders are no longer asking whether they should adopt AI. They are asking how to implement it responsibly and prove its value.

This shift in focus framed our session, Mastering Healthcare ROI: From Basic Analytics to AI-Powered Precision Targeting, where David Figueredo, Chief Innovation Officer at MedeAnalytics, explored how organizations are moving beyond static reporting to AI-driven precision targeting. The focus was not experimentation, but execution.

AI creates value when it identifies high-impact opportunities, whether improving MLR performance, optimizing reimbursement, strengthening quality scores, or reducing unnecessary utilization. AI then integrates those insights directly into workflows through precision targeting. This approach enables organizations to scale interventions across individuals and populations without compromising accuracy or performance.

In practice, this means aligning AI models to defined performance metrics, embedding next-best actions into operational workflows and measuring outcomes over time. Organizations that are seeing measurable impact are those treating AI not as a standalone capability, but a component of enterprise performance management.

The takeaway from HIMSS26 was clear: AI must be measurable, defensible, and aligned to enterprise performance objectives.

Maximizing ROI in a margin-constrained environment

Another consistent theme at HIMSS26 was financial pressure. Across sessions and peer discussions, healthcare leaders emphasized that technology investments must demonstrate a tangible return as value-based care models shift more financial risk to providers and reimbursement structures grow increasingly complex.

Proving ROI requires more than reporting activity. It requires directly linking analytics initiatives to financial, clinical, and operational outcomes. Organizations achieving measurable progress are approaching ROI with greater discipline. They are defining performance baselines, aligning analytics use cases to specific cost or revenue objectives, incorporating accountability into operational workflows, and measuring impact against defined financial targets over time.

Rather than evaluating tools in isolation, leading organizations are integrating analytics into broader performance strategies. As healthcare leaders assess their technology portfolios, the emphasis is shifting toward solutions that support measurable enterprise impact rather than isolated point capabilities.

Modernizing data to accelerate time to insight

Data modernization was another dominant topic, particularly as organizations seek to support AI initiatives and enable real-time action. Legacy architectures and fragmented systems often result in slow reporting cycles, delaying intervention and limiting proactive response to emerging performance risks.

In our second session, From Days to Hours: Scaling Employer Reporting and Data Processing at a Multi-State Health Plan, we highlighted how modern data strategies can dramatically compress reporting timelines. Organizations leveraging scalable cloud-native infrastructure and streamlined data processing are accelerating reporting cycles by standardizing data definitions, automating ingestion and validation processes, and consolidating disparate sources into unified platforms that support enterprise-wide visibility.

These foundational improvements enable faster, more consistent reporting and greater control across the enterprise. Leaders can respond more quickly to reporting needs, operational challenges, and emerging trends. Infrastructure modernization also supports the deployment of predictive models and AI-driven insights.

Speed alone, however, is not sufficient. Trusted data enables confident action, and that trust is built on unified, interoperable systems.

Advancing interoperability to unify performance

Interoperability remained a central topic, with sessions emphasizing FHIR standards, data exchange, and cross-system integration. As organizations accelerate AI adoption and data modernization, unifying clinical, claims, financial, and social determinants of health data has become increasingly urgent.

Despite industry progress, many organizations continue to operate across fragmented systems that limit visibility and create inconsistent data. Disconnected environments reduce confidence in analytics and constrain the impact of AI initiatives. When data remains siloed, AI models lack the comprehensive, standardized inputs required to generate reliable insights at scale, limiting trust in model outputs and the ability to deploy AI effectively across the enterprise.

Organizations focused on a strategic approach to interoperability are standardizing data models, harmonizing structured and unstructured data across formats, and establishing governance frameworks that promote consistency and transparency. Rather than treating interoperability as a compliance requirement, they are positioning it as a performance enabler.

When interoperability is addressed intentionally, analytics become more reliable, insights more actionable, and enterprise performance more transparent. Unified data environments strengthen cross-functional alignment, improve reporting accuracy, and enable scalable AI that drives measurable performance improvement.

Moving from strategy to execution

HIMSS26 reinforced an important shift in healthcare’s digital transformation journey. The industry is moving beyond pilot programs and proof of concepts and is now focused on operationalization by embedding intelligence into workflows, proving ROI, and scaling improvement across the enterprise.

Organizations successfully translating priorities into action share several characteristics:

• A unified data foundation
• Integrated workflows connecting insight to execution
• Measurable AI strategies
• Defined ROI frameworks

As the conversations in Las Vegas demonstrated, these capabilities are no longer aspirational. They are immediate performance requirements. Healthcare leaders should be assessing where gaps exist and aligning investments to measurable enterprise outcomes.

The path forward requires discipline, clarity, and a sustained commitment to measurable results. These principles will continue shaping healthcare performance well beyond HIMSS26.