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What You'll Learn
Key takeaways
- Healthcare AI pilots stall on three compounding constraints: identity resolution, unstructured content, and governance controls.
- Patient 360, Member 360, and Provider 360 all solve the same problem — one person, many records — through matching, survivorship, and a golden record.
- HIPAA doesn't exempt AI agents: compliance has to live in the data layer, not in an after-the-fact review gate.
- Sequence matters — master identity in one domain first, govern unstructured content in the same layer, then let one AI use case prove the foundation.
Healthcare organizations are under more pressure than at almost any point in the last decade. There are tighter margins. More regulatory scrutiny. And a mandate to improve outcomes while cutting costs. All at the same time.
Most are trying to meet that pressure with data that isn’t ready for it. Patient information sits in an EHR. Claims data sits somewhere else. Referral letters, lab reports, and prior-authorization forms sit in yet another system. Each system works fine on its own. But none of them communicate with each other, and that gap is what gets in the way of better outcomes and lower costs.
This matters even more with AI entering the picture. Predictive analytics, automation, and clinical decision support only work when the data behind them is complete, accurate, and connected.
In healthcare and life sciences, where a wrong answer is a wrong clinical or regulatory decision, data foundation isn’t optional infrastructure.
Understanding the data challenge in healthcare & life sciences
Three things compound at once in healthcare and life sciences.
First, identity is genuinely hard.
The same person shows up as a patient, a health plan member, and sometimes a research subject, under slightly different names, birthdates, and IDs across systems. A patient who’s seen at three facilities inside the same health system can easily generate three separate charts.
Second, most of the highest-value information isn’t sitting in a column at all.
It’s in a clinician’s free-text note, a scanned referral, a pathology PDF, or a fax that got dropped into a document repository.
And third, none of this can be fixed with a “move fast and clean it up later” attitude.
Because PHI mishandled in production is a breach with regulatory and reputational consequences attached.
That combination is why so many healthcare and life sciences AI pilots stall between demo and production. Identity resolution, unstructured content, and the governance controls are the constraints.
How to solve the identity, unstructured data, & compliance Problems
Each of those three problems mentioned earlier has a corresponding fix. And all three have to work together.
Resolve Identity: Patient 360, Member 360, & Provider 360
A health system’s Patient 360, a payor’s Member 360, and a life sciences team’s Provider 360 are all solving the same underlying problem. The same person or entity exists as multiple records across systems. The fix is matching and merging.
- Probabilistic matching to catch records that likely belong to the same person.
- Survivorship rules to resolve conflicts when sources disagree.
- a golden record that updates continuously rather than getting reconciled once a quarter.
This is what Informatica MDM is built for. Once that record exists, it has to reach where decisions happen. That’s where Salesforce Data 360 fits. It surfaces the governed record directly inside the CRM and Agentforce, so a clinician, a claims processor, and an AI agent are all working from the same identity.
Govern unstructured clinical data
Most clinical detail lives in free text, PDFs, and scanned documents rather than structured fields. Extracting the useful parts, medications, diagnoses, lab values, and mapping them to vocabularies is table stakes at this point.
What determines whether that extracted data is safe to use is where the extraction happens. Snowflake’s Cortex functions can parse and structure unstructured documents natively inside the same governed environment where the rest of the patient record already lives. That matters because a document processed outside the governed layer is a document without lineage. No record of where it came from, no sensitivity tags, no way to confirm it’s been de-identified. Keep extraction inside the platform doing the governance, and the resulting data inherits the same access controls and audit trail as everything else.
Build compliance into the data layer
HIPAA doesn't exempt AI agents. An agent that queries lab results or drafts a clinical summary is handling PHI the moment it runs.
it’s bound by the same obligations as any other system that touches that data. None of that is pending or new. It’s what’s already required today.
Meeting that requires classification and access control to live in the data layer itself. Informatica’s data governance and catalog capabilities can automatically detect and tag PHI across structured and unstructured sources, enforce minimum-necessary access at the field level, and log every access, who, what, under which policy. That’s the difference between compliance as an ongoing control and compliance as a certification that goes stale the moment a new AI use case launches.
The right order to build a healthcare data foundation for AI
Most healthcare organizations fail because they try to do everything at once, or they start with the AI use case instead of the data underneath it. The sequence that works looks like this:
Master identity first, in one domain
Bring unstructured content into the same governed layer
If clinical notes and scanned documents get extracted and stored outside the MDM and governance framework, you’ve built a second source of truth by accident. Extraction and governance need to happen together.
Build compliance controls into the pipeline
PHI classification, minimum-necessary access, and audit logging should be part of how data moves, not a review gate that happens after a use case is built.
Connect the governed layer to where decisions happen
A perfectly governed record sitting in a data warehouse doesn’t help a clinician or a claims processor if it never reaches the system they work in. The foundation has to extend into the operational tools, care management platforms, CRM, prior-auth workflows, not just the analytics layer.
Let the first AI use case prove the foundation, then scale it
Pick one well-scoped, high-friction use case and use it to prove the foundation works before pointing five more agents at the same data. That sequence is deliberately unglamorous.
How LumenData helps build a data foundation for healthcare & life sciences
Trusted data is the difference between AI that helps in healthcare and life sciences – AI that harms
LumenData builds that trusted foundation: master the data, govern it end-to-end, then activate it where clinicians, analysts, and agents work.
We stand up Patient 360, Member 360, and Provider 360 programs on Informatica IDMC — resolving identity across EHRs, claims systems, and commercial CRMs into a single governed record.
We connect that governed layer to Salesforce Data 360 and Agentforce through MuleSoft — so the record an agent acts on is the same one a clinician or analyst would see.
Ready to build a solid AI-ready data foundation for your healthcare or life sciences organization? Connect with LumenData today.
About LumenData
LumenData is a leading provider of Enterprise Data Management, Cloud and Analytics solutions and helps businesses handle data silos, discover their potential, and prepare for end-to-end digital transformation. Founded in 2008, the company is headquartered in Santa Clara, California, with locations in India.
With 150+ Technical and Functional Consultants, LumenData forms strong client partnerships to drive high-quality outcomes. Their work across multiple industries and with prestigious clients like Versant Health, Boston Consulting Group, FDA, Department of Labor, Kroger, Nissan, Autodesk, Bayer, Bausch & Lomb, Citibank, Credit Suisse, Cummins, Gilead, HP, Nintendo, PC Connection, Starbucks, University of Colorado, Weight Watchers, KAO, HealthEdge, Amylyx, Brinks, Clara Analytics, and Royal Caribbean Group, speaks to their capabilities.
For media inquiries, please contact: marketing@lumendata.com.
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