Healthcare’s interoperability agenda has been remarkably successful. Data that once existed in silos can now move across providers, payers, laboratories, pharmacies and digital health platforms with far greater ease than before.
The opportunity now is to use it better.
Connectivity alone delivers limited value without the right core foundation, context and coordination layered on top. As AI, automation and increasingly autonomous workflows become central to healthcare operations, organizations are discovering that connected data is not enough. AI can only be as effective as the quality, context and trustworthiness of the information behind it.
Healthcare Has More Data Than Ever. Better Decisions Remain Elusive.
Most healthcare organizations today are awash with data yet starved of decision-ready intelligence. Clinical, operational, financial and patient-generated information exists in abundance, but much of it remains fragmented, stripped of context and disconnected from the workflows where decisions are made.
Part of the problem is structural. Many interoperability programs were designed to meet exchange and compliance requirements rather than improve outcomes such as length of stay, cost or patient experience. Interoperability must evolve from data exchange to decision enablement.
The missing layer is the ability to turn connected insights into action. Organizations creating measurable value are those that make information usable at the point of decision-making, reducing effort, delays and uncertainty.
The Last Mile Is About Intelligence
Interoperability creates value only when information reaches the right person, in the right form and at the right moment. That requires more than integration. It requires a context layer that assembles fragmented data into a complete picture of the patient or member.
It is what transforms information into something usable. It combines risk scores, care gaps, prior interactions and business rules into a single view, allowing users to understand what action is needed next. This is the last-mile gap: the distance between data that exists across systems and intelligence that surfaces within the workflow where action is taken.
Consider a regional payer that had integrated multiple claims but still left care managers and call-center staff without usable context. Decisions were delayed, care gaps went undetected and opportunities to intervene early were missed. When a unified longitudinal record brought together enrollment history, claims data, care gaps, and prior member interactions within the same workflow, care managers no longer had to reconstruct context before every conversation. Care coordination improved, along with patient outcomes and cost efficiency. The data had always been there. What changed was the ability to deliver information in a form that supported action.
Provider organizations face a similar challenge. By unifying quality, outcomes, physician performance and resource utilization data, leaders can make better-informed decisions about care delivery and capacity.
None of this works without a common language for data. Information may move across systems and still fail to support decisions if coding, terminology and metadata remain inconsistent. Standards such as LOINC (Logical Observation Identifiers Names and Codes) help make information comparable and computable, while provenance signals establish where it came from and whether it can be trusted. Without that foundation, organizations continue to rely on manual validation, duplicate testing and workarounds. The industry is also facing growing pressure to address these gaps. Regulatory initiatives aimed at improving interoperability and streamlining prior authorization are raising expectations around data accessibility, transparency and decision speed. At the same time, healthcare organizations are accelerating AI adoption, exposing weaknesses in data quality, governance and operational readiness. As a result, interoperability has become a business priority that directly affects outcomes, financial results and the ability to scale AI effectively.
Why AI Is Driving a Data Maturity Conversation
AI is forcing a new test of interoperability: whether the information moving across systems is reliable enough to support decisions. As organizations expand AI across care delivery and operations, they are finding that results depend less on model sophistication than on the quality, traceability and governance of the underlying data. Explainable AI starts with information that can be trusted, understood and traced to its source. The organizations seeing the strongest results are strengthening their data foundations first.
A decision-ready foundation requires:
- A longitudinal person-centric record spanning clinical, claims, behavioral, social determinants and device data.
- Terminology that is normalized and computable, not just viewable.
- Intelligence embedded directly within workflows.
- Provenance and governance on every data element.
- A closed loop where actions feed outcomes, which in turn refine models, creating a system that improves continuously.
The business value is becoming increasingly clear. Providers combining post-acute and payer feeds are improving discharge efficiency and reducing readmissions. Payers using rules and AI for real-time decisioning are accelerating prior authorization. Pharma and MedTech organizations are using multi-source data to identify patient cohorts more precisely and generate real-world evidence.
McKinsey’s report noted that access to longitudinal, high-quality clinical data is likely to emerge as a core competitive advantage.
Why Decision Strategy Will Define the Next Decade
For years, healthcare leaders have invested in data strategy. The next decade will belong to organizations that master decision strategy.
Data strategy focuses on collecting, integrating, governing and managing information. Decision strategy focuses on outcomes. It asks a fundamentally different question: Which decisions matter most and how can data improve them?
For healthcare executives, two priorities stand out.
- Focus interoperability investments on the outcomes that matter most: quality, cost and patient experience.
- Invest in a governed, scalable data foundation with shared ownership across data leaders.
That foundation must also learn. Actions should feed outcomes and outcomes should refine the rules, workflows and models that follow. The goal is a decision system that improves continuously, not a one-time integration.
Connectivity has laid the foundation. The next phase of healthcare transformation will be defined by how effectively organizations act on the information they already have.
The leaders that pull ahead will not be those with the most data or the most advanced AI. They will be the ones who consistently make faster, smarter decisions. In the end, interoperability is not a technology advantage. It is a decision advantage. And over the next decade, that distinction will matter more than ever.
