Healthcare organizations have arguably more information about their customers (i.e. patients) than any other industry. Data from lab systems. EHRs. Pharmacy records. Claims files. Even wearables data. They have data all over the place.

And yet many of these data sources live in discrete silos. Most organizations lack a singular, cohesive source of truth that brings all this information together to create a 360 degree view of their patient. And because of this, their ability to truly fulfill the promise of elegant, coordinated care is severely undermined.

Which is a problem. According to McKinsey, 25-30% of healthcare spending in the US (nearly $1 trillion per year) is wasted. And as much of a third of that is a function of data fragmentation. All these disconnected systems raise the cost of care, impact diagnosis accuracy, and harm long-term outcomes.

Every leader in healthcare knows the pain of this, and most of the reasons why. Legacy EHRs weren’t built to talk to each other. Incentives to share are weak. Every new point solution adds another silo.

But it doesn’t have to be this way. While it may not be possible to get a 100% accurate picture of a patient, we can make significant strides in this direction. In this article we discuss how to do exactly that.

‍The 3 Pillars of an AI-Ready Platform

There are three essential elements to creating an AI-Ready platform that provides a complete picture of the patient. When combined, these pillars form what researchers call a “unified patient-intelligence layer”: a shared data environment where AI models can interpret information in real time across systems.

A Unified Data Foundation

Easier said than done. You know this already and it’s still an issue. But the fact remains having a single data foundation is critical for enabling anything else you’re trying to do. This single repository needs to include clinical data from EHRs. Claims data from payers. Imaging. Devices.

The playbook that works looks something like this:

• Map and prioritize the mess. Create a simple inventory of every data source. Every EHR, lab system, imaging platform, claims platform, etc. Label each by owner, format, update frequency, and business value.
• Pick one or two high-impact use cases. For example, reducing duplicate imaging, or closing care gaps that require data from multiple systems. Solving a concrete problem builds momentum and exposes what integrations really matter.
• Connect logically, not physically. You don’t need to move everything into one database. Use APIs or streaming to create a logical layer that links existing systems around shared identifiers. That gives you the benefits of unification needing a full rebuild.
• Set ownership early. Assign data stewards for key domains (patient identity, claims, orders) and define what “clean” data means up front.

Easy Connectivity with Existing Systems

Sometimes organizations think the answer is to consolidate by minimizing the number of vendors or platforms. Or that they need to rip out their old systems wholesale. But there’s nothing wrong with having multiple vendors and multiple tools, each of which is best in breed. And it’s not a given you need to fully modernize every system in your stack. What you do need is to be able to connect these systems together in a way that keeps your various systems of record intact, but synchronizes them around shared identifiers.

A practical path here looks like this:

• Build a “connect once” layer. Use APIs or event-streaming tools to connect systems through a single integration layer rather than building one-off connections between every pair of platforms. This lets data flow in real time without forcing every vendor to rebuild their interface.
• Standardize identifiers, not vendors. Agree internally on a shared patient ID schema, provider ID schema, and key code sets (LOINC, SNOMED, RxNorm). These act as translators between all your systems. Once those are consistent, data can stay where it lives but still be useful.
• Make it modular. Treat integrations like reusable assets. When a new data source comes online, you should be able to plug it in much more easily.
• Keep vendors honest. Require open APIs and documented data-export capabilities in every new contract. Make interoperability a purchasing requirement.

Designing for Clinical Adoption

AI only works if your clinicians actually use it. Build workflows around actual clinician use cases rather than your IT architecture.

Some suggestions on how to do that:

• Start with real workflows, not ideal ones. Follow your team around. Map how they actually work. Watch how information moves between people, departments, and systems. If the new tools add friction to the way things are currently done, it probably won’t stick.
• Surface information in context. Present the right data at the right moment. When you stitch all this data together, you risk overwhelm. Design clinician views that map to their specific roles in the organization. Make the other stuff accessible, but not the default view.
• Automate the low-value steps. Look for opportunities to reduce friction from their day-to-day work. Pre-fill common fields. Trigger reminders. Summarize lengthy patient histories. Small time-savers add up.
• Pilot with champions, not committees. Early adopters matter. Find a handful of clinicians who already drink the Kool-Aid and want better data. Involve them in an iterative process to build stuff that works for them.
• Measure success in minutes, not metrics. The best adoption metric is time saved: on charting, coordination, or patient prep.
• Lean on pre-built workflows. You don’t need to build from scratch with AI. There are now many AI solutions that integrate directly into clinical workflows, population health programs, and operational processes. That already understand the unique complexities of patient care and regulatory requirements.

Achieving The Promise of The 360° View

A clear, connected view of the patient that gives every clinician, care manager, and analyst access to the same story at the same time can transform the way you provide care. Getting there comes from a sequence of choices. Mapping and linking the data that already exists. Connecting systems so information can move freely. And designing workflows that make patient context instantly available where care happens.

When those pieces come together, care teams are able to see the whole person (the clinical record, the social factors, the prior authorizations, the device data) in one place. And once that happens, the promise of coordinated, intelligent care stops being theoretical and becomes the standard of practice.

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