The HIMSS Global Health Conference and Exhibition has long served as a signal for where healthcare technology is headed. This year’s conference, attended by more than 24,000 healthcare leaders, reflected an industry that has moved past curiosity and into a more demanding phase of transformation.
Artificial intelligence (AI), interoperability, and digital infrastructure remained central topics at HIMSS26, similar to prior years. What changed at this year’s conference was the tone. The conversation was no longer focused on whether these technologies belong in healthcare. Instead, it centered on how organizations make them work in practice, responsibly, at scale, and inside the realities of day-to-day operations.
Across clinical, operational, and technology-focused sessions, one message was consistent. AI does not create value on its own. Organizations create value when their data, workflows, and operating models are prepared to use it.
Intelligence is no longer the challenge — activation is
One of the clearest themes at HIMSS26 was a growing recognition that retrospective analytics alone are insufficient for today’s healthcare environment. Dashboards and after-the-fact reporting still matter, but they do not drive change on their own.
Sessions focused on value-based care performance and clinical operations showed that improvement occurs when intelligence is embedded directly into workflows. When insights surface during pre-visit planning, at the point of care, or within routine clinical decision-making, teams act. When the same information arrives weeks later in a report, the opportunity is often lost.
This shift reframes how healthcare organizations should evaluate analytics investments. Accuracy and sophistication remain important, but timing, context, and usability are what determine whether insight leads to action.
Interoperability and AI have become inseparable
At HIMSS26, interoperability was no longer treated as a prerequisite to AI that must be completed first. Instead, it was positioned as a capability that evolves alongside AI.
Healthcare leaders acknowledge a shared reality. Fragmented, siloed data remains the primary barrier to scaling AI initiatives. At the same time, AI is changing how organizations approach interoperability by enabling the use of unstructured and multimodal data and by reducing manual data reconciliation.
The implication is significant. Interoperability is no longer just about data exchange. It is about creating a trusted, governed data foundation that supports analytics, automation, and operational execution across the enterprise.
Organizations that continue to treat interoperability as a compliance exercise risk limiting the impact of downstream technology investment.
AI is shifting from tools to operating models
Several sessions at HIMSS26 explored how AI is beginning to influence the healthcare operating model itself. Rather than focusing on individual tools or use cases, speakers described how intelligent systems are being used to redistribute work across care teams, administrative staff, and digital agents.
This shift is not about replacing clinicians. It reflects a practical response to workforce shortages and capacity constraints that cannot be solved through hiring alone. Repetitive, non-clinical work continues to consume time and attention that could be better spent on patient care.
The conversation around agentic AI was notably pragmatic. Leaders were candid about the need for governance, boundaries, and human oversight. Regulatory frameworks are still evolving, but delaying experimentation was viewed as a risk in itself. The emphasis was on learning how to integrate AI into workflows responsibly, not pursuing autonomy for its own sake.
Operational AI is delivering results today
While some sessions focused on future operating models, others highlighted where AI is already delivering measurable impact. Administrative workflows, supply chain operations, contact centers, and care coordination repeatedly surfaced as areas where AI is reducing burden and improving responsiveness.
What stood out was not the novelty of these applications, but their practicality. Successful efforts shared common characteristics:
- Clear problem definition
- Focus on reducing repetitive work
- Human oversight built into the workflow
- Change management treated as a core requirement, not an afterthought
In many case studies, adoption succeeded because solutions addressed immediate pain points. Perfect integration mattered less than tangible relief for staff and care teams.
Another unifying theme at HIMSS26 was the recognition that EHRs alone are not designed to support AI at scale.
Health systems described how they are moving beyond traditional EHR-centric architectures, toward AI-ready data platforms that unify clinical, financial, and operational data. These platforms support interoperability standards, enable governed experimentation, and reduce dependence on specialized data engineering teams.
The distinction is important. Cloud-hosted EHR has become increasingly common. Differentiation now comes from what organizations build on top of it, whether that is workflow-embedded decision support, unified patient access, operational automation, or research platforms that accelerate insight and the translation to care.
What this means for healthcare leaders
Conversations at HIMSS26 made clear that the industry has entered a more disciplined phase of digital transformation. The question is no longer whether AI has potential. The question is whether organizations are structurally prepared to use it.
For healthcare leaders, this means:
- Investing in data foundations that support trust, governance, and reuse
- Prioritizing AI where it reduces operational burden and improves execution
- Measuring success through adoption and outcomes, not experimentation
Technology can amplify impact, but only when it aligns with how care is delivered and managed today.
As the conversation around AI matures, organizations are beginning to recognize that sustainable impact depends less on the tools themselves and more on the foundation beneath them. Data quality, interoperability, governance, and workflow alignment are no longer supporting considerations. They are the work.
As Chas Busenburg, Senior Manager of Data & AI at MedInsight, put it:
“AI is not the differentiator. What makes the difference is having validated, trusted data; governance you can audit; and the ability to deliver answers at the moment of decision rather than in a report after the moment has passed. Without that, AI just makes noise faster. With it, the reality changes: a healthcare leader asks a question and, within minutes, gets the drivers, benchmarks, and actions. Not a dashboard. An answer.”
This perspective reflects the direction the industry is moving. AI is shifting from experimentation to expectation, and organizations that invest in readiness and execution will be better positioned to scale responsibly.
Enabling execution with MedInsight
Milliman MedInsight is well-positioned to support this shift from insight to execution. By helping organizations integrate, trust, and activate their data through a structured Data Confidence Model aligned with industry best practices, MedInsight strengthens the foundation beneath AI and analytics and ensures data readiness before insights are applied at scale. At the core is the ability to unify disparate clinical, financial, and operational data into a governed, interoperable foundation that supports trust, reuse, and scale. This approach enables healthcare leaders to operationalize analytics and AI across value-based care, clinical performance, and financial decision-making.
Through robust data platforms, advanced analytics, and deep healthcare expertise, MedInsight supports organizations as they:
- Break down data silos
- Embed intelligence into workflows
- Navigate uncertainty with confidence
As HIMSS26 demonstrated, the future of healthcare technology is not defined by tools alone. It is defined by how effectively organizations align data, operations, and accountability to turn insight into action. MedInsight is built to support that shift, helping leaders move from fragmented reporting to governed, workflow-ready intelligence that teams can use to execute with confidence.
