80 to 90 percent faster patient onboarding. 30 to 50 percent more monitoring throughput. No additional staffing. That is what one healthcare client saw after we built them a governed data warehouse platform purpose-designed for 24-hour patient monitoring. This is what we deliver. The AI use case and the data layer it runs on, same team, same engagement, no handoff.
The AI is rarely the problem. The data underneath it is. Patient records sit in EMRs, provider data in credentialing systems, locations across hospitals and clinics. AI built on that fragmentation produces confident, hard-to-detect errors at scale, which is why most healthcare AI never makes it past pilot. We solve both halves in one engagement, designing and deploying production AI on data foundations we have been mastering for healthcare clients across multiple implementations. AI is the outcome. Data mastery is the method.
Infoverity’s expertise has greatly changed the landscape of data at SSM.
"We have not only mastered 4 domains in less than 18 months, but we have also been able to create a strategic data roadmap for the next 2 years. The team has helped us build a best-in-class MDM and Data Governance platform, while continuing to accelerate our development of an integrated, self-service data ecosystem for SSM to utilize. We would not be where we are in our data journey without Infoverity as a strategic thought and implementation partner."
94%
of health system CIOs say delays in AI adoption put their organization at a direct competitive disadvantage, even as average hospital margins sit below 2.5%. Source: Qventus
68%
of CDOs now hold explicit AI responsibilities, up from 41% just two years ago. The mandate has shifted from data stewardship to AI enablement, almost overnight. Source: Data Driven Daily
76%
of organizations say their AI governance does not fully keep pace with how employees are actually using AI, exposing them to compliance and other risks at scale. Source: Informatica, CDO Insights
Most health systems hire a data firm to fix MDM and a separate vendor to build AI. The handoff is where projects stall. We do both in one engagement. Unified master data, governed lineage, and the AI use case on top.
We qualify data inputs before a model is trained, not after it produces unreliable outputs. Patient and provider master data profiled against the AI use case it has to support. Gaps resolved before they reach production.
We are positioned to deliver the AI use cases healthcare actually needs: patient risk stratification, readmission prediction, network leakage detection. Every model runs on a governed data foundation we build, not on the data quality problem we hand back to you.
HIPAA compliance and AI governance are not separate workstreams. When patient and claims data is properly mastered and lineaged, audit trails are an output of the architecture, not a retrofit we charge you for later.
Most healthcare and life sciences AI programs stall on one of these four questions. Here is what we do about each.
We start with the use case, not the platform. ROI quantified up front. Prototypes in six to eight weeks. Production deployment on a timeline measured in months. Our healthcare accelerators absorb the heavy lifting on data, match rules, and EMR integration that usually stalls these programs.
We design AI governance and data governance as one program. PHI safeguards, access controls, and lineage are built in before the first model goes live. Your AI operates inside governed boundaries. You can prove it.
Many healthcare organizations face similar challenges. Discover how Infoverity helped improve outcomes through Data Governance & Self-Service using Databricks and Informatica MDM.
Analysts should not have to file a ticket to find data. We build governed self-service environments where clinical and operational teams query a single trusted catalogue, with AI-assisted search and natural-language querying built in.
We build governed data platforms with embedded dashboards and real-time KPI logic for 24/7 clinical operations. One healthcare client cut onboarding 80 to 90 percent and lifted monitoring throughput 30 to 50 percent.
We are positioned to deliver demand sensing, capacity forecasting, and labor demand prediction for healthcare operations. Every model runs on the same patient and operational master data foundation your clinical AI depends on.
Prevent duplicate patient creation in real time through "search before create" integration with EMRs and registration systems. The result is the governed patient identity every downstream AI model needs to produce reliable outputs.
Patient access and network adequacy applications depend on accurate, complete provider data. We build the provider data management infrastructure that powers consumer-facing directory applications and the network leakage models that recover provider revenue.
Prior authorization and claims processing automation depend on clean policy, member, and clinical reference data. We build the data architecture that supports straight-through processing, reducing cost per claim and the administrative load on clinical teams.
Ready to put your healthcare AI into production, not just planning?
A 30-minute conversation. We will discuss the AI use cases on your roadmap, the data work each one needs, and how to sequence both for measurable ROI.
No obligation. A 30-minute conversation to understand where you are and what your data and AI goals require.
