By Clay Ritchey, CEO
AI in healthcare has reached a crossroads. On one side, it promises a new era of care delivery and patient experience. AI agents, for example, can send the right care reminder at the right moment, flag a risk before it escalates or choose whether to send a text or email about a prescription refill.
But when the system doesn’t know “who is who” at every touchpoint, predictions go astray, actions are misread and engagements are lost. The promise of a better future remains within reach, but only if healthcare organizations solve a long-standing challenge—trusted identity.
The Identity Problem Holding AI Back
Healthcare leaders are investing in AI tools that promise to transform the delivery of care, yet 85% of AI projects never make it to production, with poor data quality a leading factor.
The reason is that AI is only as strong as the data it learns from. Healthcare is data-rich, but insights-poor, generating an estimated 30% of the world’s data. And while hospitals produce 50 petabytes of data per year, they only use about 3% of it.
When consumer, patient and provider records are siloed across a complex ecosystem of systems of record (EHRs), systems of experience (CRMs) and systems of insight (data and analytics), with no trusted identity to unify a 360-degree view of the person and their relationships, technology doesn’t see a whole person—it sees fragments.
Imagine patients receiving a flu shot reminder from a clinic repeatedly after they have already scheduled and received their shot, while another patient is flagged for a condition they don’t have because of mismatched identities. In both cases, the system fails them, not because of algorithms, but because of bad data.
The costs of getting identity wrong are high. Dirty data drains organizations of an average of $12.9 million per year. A CMS audit, meanwhile, found that 55% of provider records contained inaccuracies or missing information, and 32% of customers would stop engaging with a brand after a single poor experience. According to an Accenture study, younger patients are six times more likely to switch providers.
Solving Healthcare’s Identity Problem: The Three Cs Of Trust
Healthcare has wrestled with identity problems for decades. Every new system, merger or digital initiative has compounded the complexity, creating a patchwork of partial records that rarely align. Until organizations address this blind spot, AI’s promise remains unrealized. Once identity data is trusted, complete and current, AI can begin to deliver the value—and future—that leaders expect.
Trusted identity rests on three pillars: completeness, correctness and context. Together, these “three Cs” provide what AI needs to move from promise to reality.
1. Completeness
Patients don’t live in fragments, but their data often does. Records scattered across EHRs, CRMs, cloud data platforms, claims and labs create only partial views of a person—and with increasing complexity of joint ventures, mergers and acquisitions as well as surgery and ambulatory clinic expansions—harnessing a complete view of a person is more difficult than ever.
2. Correctness
Accuracy matters. When records are outdated and information is wrong, engagement dwindles and clinicians may lose confidence in the tools meant to support them. But when data is correct—when we truly know “who is who”—trust rises, service improves and patients feel recognized and understood.
3. Context
Identity data is more than just demographics. It consists of real-world factors like social determinants of health, behavioral insights and even household information. 2020 research (gated content) from the American Hospital Association shows that up to 80% of health outcomes and needs are shaped by non-clinical factors like lifestyle, housing, food or transportation. Without context, AI misses the forces that truly shape a person’s well-being. With it, AI guides care in ways that acknowledge patients’ real lives, not just their charts.
Each pillar matters on its own. Together, they unlock what leaders expect from AI innovation: accuracy that inspires confidence, decisions that adapt in real time and trusted relationships among patients, clinicians and the systems that connect them.
Building The Future Of Healthcare On Trusted Identity Intelligence
Identity isn’t background infrastructure. It’s the foundation for everything. Healthcare identity intelligence makes this possible by establishing a single, trusted source of truth for identity across the enterprise. These solutions bridge the gap between systems of record, experience and insight, enabling a complete and reliable view of consumers, patients, providers and partners.
With this groundwork in place, AI and other strategic data-driven initiatives operate dependably. Predictions improve, outreach becomes more relevant and clinicians and patients gain confidence in the insights it provides.
Using master data management (MDM) and AI tools, for example, Banner Health (a Verato customer) is able to deliver a single brand experience for patients across its facilities, joint venture partners and affiliated clinics, even on different EHRs. After mapping out their desired care journeys and the experiences they wanted to curate, it was clear that their AI and other investments would be unsuccessful without the foundational identity intelligence infrastructure enabling a complete and trusted 360-degree view at every touch point across the journey.
Success at scale requires focus and expertise, but above all, it requires making identity central to every digital and AI strategy.
Once identity is solved, possibilities multiply. For example, AI could identify when a patient might miss an appointment due to transportation barriers and automatically arrange a ride, preventing a gap in care. These are the kinds of opportunities that emerge when identity becomes trusted. That future is within reach, and trusted identity makes it real.