This post is part of our Identity First. Everything Follows. blog series, in which we explore our response to CMS’s 2025 RFI in partnership with Snowflake.
Generative AI is making headlines across all industries. In healthcare, the most powerful AI models are driving care decisions. From predictive risk scoring to clinical decision support, AI systems are being asked to do more, faster.
But there’s a problem: many are built on fragmented identity data. And if the model doesn’t have the complete picture, how can it make the right call?
Misleading AI models are dangerous
The performance of any healthcare AI depends on two things: the data it is trained on and the data it uses to make decisions in the actual care setting. AI models can only learn to make safe, meaningful decisions when the training data reflects whole-person records. And once AI makes decisions impacting real-life patients, it needs complete, accurate, and up-to-date input to deliver on its promise of fast, reliable, and unbiased results.
When identity is incomplete, duplicated, or disconnected, AI models lose accuracy and can become misleading. And because AI is built to scale decisions, even small errors in identity management can become major problems when magnified across populations.
Let’s break it down:
• A model flags a patient as noncompliant with follow-up care.
The algorithm sees multiple missed appointments and assumes the patient is disengaged. But in truth, the patient did show up—those visits were just recorded under a duplicate profile. The result? Incorrect risk categorization, potentially punitive outreach, and erosion of trust in both the system and the care team.
• A readmission prediction model underestimates clinical risk.
A patient’s recent emergency department visits are linked to a different, unmerged record. The AI model misses half the story and classifies them as low-risk. Care teams rely on this output to prioritize resources, so this patient may slip through the cracks until a preventable crisis occurs.
• An equity initiative misrepresents neighborhood needs.
A social determinants of health (SDOH) dashboard maps household and demographic data inconsistently, linking it incorrectly across various members of the same family or community. health planners end up targeting the wrong zip codes, or worse, overlooking underserved populations altogether.
These are the real-world consequences of poor healthcare identity management, and AI systems can’t correct what they don’t recognize.
AI thrives on rich, connected data, and identity is what makes that connection possible.
Yet most healthcare systems still struggle with duplicate records, unlinked data, and siloed systems. Until identity is resolved at the source, even the most advanced models will be making educated guesses based on fragmented inputs.
Trusted identity is foundational AI infrastructure. And as health systems move to adopt more AI-powered tools, the first step is making sure the underlying identity data is clean, complete, and consistent across every touchpoint.
AI bias starts with identity gaps
Every AI model begins with data, and when identity data is flawed, bias is baked in from the start. When a patient’s demographic information is inconsistent, incomplete, or duplicated across systems, the AI doesn’t see the full person. It sees fragments. And that fragmented view produces distorted patterns, unreliable predictions, and inequitable outcomes.
Bias is introduced when information, often linked to underserved populations, is missing, inconsistent, or misaligned due to poor identity matching. This matters even more in areas where accurate, equitable care is critical, such as behavioral health, chronic disease management, maternal care, and outreach to historically marginalized populations. These are precisely the domains where identity data is most likely to be fragmented due to gaps in coverage, provider churn, or lack of interoperability across care settings.
Let’s take a closer look at how bias builds silently when identity data breaks down:
- In behavioral health, patients may receive services from community clinics, hospitals, and telehealth platforms that don’t share identity infrastructure. Without a unified record, their care histories look sparse or inconsistent, leading AI tools to deprioritize follow-up or misjudge severity.
- In chronic disease management, inconsistent identifiers across EHRs and payer systems may split a patient’s longitudinal record into disconnected episodes. Predictive models then underestimate risk, missing opportunities for early intervention.
- In underserved communities, patients are more likely to have incomplete records due to housing instability, name variations, or limited digital access. If AI models interpret missing data as disengagement, the system ends up penalizing the very people it’s meant to support.
Bias isn’t just a bug in the model; it’s a reflection of the identity blind spots in the data feeding it.
AI trained on distorted identity input will reinforce inequity instead of solving it. It might recommend fewer resources for people who need more support or miss patterns that would be obvious if the data were truly connected. Worse, because AI operates at scale, those errors multiply, affecting thousands of patients and widening disparities with each decision.
The fix isn’t just model transparency or fairness audits. It’s identity integrity. By resolving identity accurately at the source, health systems can ensure that every model begins with a complete, truthful picture of the patient. That’s the only way AI can support, and not sabotage, efforts to deliver equitable care.
The identity infrastructure that combats bias
AI in healthcare is only as reliable as the data it learns from. Whether it’s powering clinical predictions, population risk scores, or care coordination recommendations, algorithms need to be trained and deployed on complete, accurate patient data. When identities are fragmented, duplicated, or mismatched, AI inherits those flaws — embedding bias, missing risks, and generating recommendations that can’t be trusted.
That’s why a healthcare-focused master data management (MDM) solution is essential. To make AI fit for healthcare, your identity infrastructure must:
- Unify patient identity across all systems, linking records from EHRs, payers, call centers, and community partners into one trusted profile.
- Preserve accuracy despite demographic inconsistencies. Data should remain correct even when names, addresses, or other identifiers are incomplete, outdated, or entered in different formats.
- Deliver a complete, longitudinal view. AI models must be trained and deployed on full patient histories that integrate both clinical and non-clinical data, not fragments.
- Support defensible, transparent analytics. Identity must be accurate enough that results can withstand clinical, regulatory, and ethical scrutiny.
- Continuously enrich and maintain identity data. AI and advanced analytics require fresh, reliable data to prevent drift and bias over time.
With these capabilities in place, health systems can train AI on high-integrity data, avoid reinforcing inequities, and apply insights to the right individual in real time.
Verato delivers this foundation for trustworthy AI. Purpose-built for healthcare, Verato enables organizations to eliminate duplicates, reduce data bias, and power AI strategies built on truth, not assumptions.
Build smarter AI with trusted identity
Without unified identity, even the most sophisticated AI can misfire — flagging the wrong patient, overlooking a critical risk, or perpetuating disparities. With Verato, health systems gain the accurate, complete, and human-centered data AI needs to truly improve care.
Schedule a demo today to see how Verato helps you build safer, smarter AI strategies.
