How CHOP Is Building an AI-Ready Healthcare Organization with Data Intelligence and Trust

This blog shares takeaways from the HIMSS Whitepaper: Building an AI-Ready Healthcare Organization with Data Intelligence to Drive Clinical Success. Primary voices include Beth Senay (Director of Data Trust, Children’s Hospital of Philadelphia) and Abdul Tariq (Associate Vice President of Data Science, CHOP)

Most healthcare leaders agree that data quality matters for AI. But “quality” in a clinical analytics context—where we scrub anomalies and remove outliers—isn’t the same as AI readiness. For AI, “clean” can actually mean incomplete. If models never see the messy reality of care delivery—edge cases, rare events, transcription quirks, emerging patterns—they learn a simplified world and stumble at the bedside.

Gartner puts it plainly: AI-ready data must be representative of the use case, inclusive of “every pattern, errors, outliers and unexpected emergence” the model will encounter in the wild. For this reason, AI-ready data isn’t a one-time hygiene project; it’s an ongoing practice of aligning, qualifying, and governing data for specific AI uses. 

Below, we break down what “real, not just clean” looks like in practice—drawing on the HIMSS whitepaper and a joint interview with CHOP’s Beth Senay and Abdul Tariq—connecting it all back to Gartner’s guidance on AI-ready data for data & analytics leaders.

The misconception: “Cleaner data → better AI”

Traditional business intelligence favors tidy datasets. Analysts de-duplicate, impute, standardize, and trim outliers to produce metrics humans can trust and act on. But in model training and evaluation, those “imperfections” are often the signal:

• Outliers may represent rare yet critical clinical states.

• Errors and anomalies can encode operational realities (e.g., documentation lags or device quirks) that models must learn to handle.

• Emergent patterns (new codes, novel care pathways, seasonality shifts) are exactly what AI must recognize early.

Gartner’s guidance reinforces that AI readiness is contextual and iterative: the only way to prove readiness is to align data with a specific use case, qualify that data against confidence requirements, and demonstrate appropriate governance over time.

What “real data” looks like at CHOP

CHOP’s leaders have operationalized this shift—starting with a cultural and architectural foundation that treats data as an institutional asset and places stewardship close to the work.

One catalog, shared truth

CHOP’s branded data catalog (“Gene”) centralizes definitions, lineage, quality signals, and governance implications so clinicians, researchers, and operations can find and trust the same assets. “Working from one centralized catalog… builds confidence and accelerates insights,” says Beth Senay, Director of Data Governance and Literacy. When everyone can see certified sources, usage notes, and lineage, confusion drops—and trust rises.

Patient-centered custodianship

Rather than a “compliance gate,” CHOP’s Data Trust Office positions governance as an enabler. Senay describes the stance as patient-centered custodianship: the hospital is the steward of data owned by patients. 

That ethos is reflected in practical controls, including classification, least-privilege, role-based access to the Helix data warehouse, automated and retrospective quality reviews, and audit trails. Such controls enable teams to innovate responsibly without compromising privacy.