Chart Abstraction is Infrastructure

Across large academic medical centers, chart abstraction is happening everywhere... and nowhere.

A clinical trials team is building its own abstraction workflow for eligibility screening.

A registry group is hiring manual abstractors to comb through the EHR.

An informatics department is experimenting with large language models (LLMs) against exported notes.

A quality improvement team is running its own scripts.

All of them are trying to solve the same problem: extracting structured insight from unstructured EMR data.

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The problem isn’t that institutions are investing in AI.

The problem is that chart abstraction is being treated as a isolated projects or pilots instead of infrastructure.

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Chart Abstraction Is Foundational to Modern Research

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Chart abstraction (or chart curation) is the process of extracting structured, usable data from clinical notes and other unstructured fields in the EHR.

It powers:

• Clinical trials (eligibility, endpoints, adverse events)
• Retrospective research
• Clinical registries
• Quality improvement initiatives
• Operational clinical workflows
  

Every large institution runs hundreds of abstraction-driven efforts at any given time. And in nearly all of them, unstructured data is the bottleneck.

Large language models and artificial intelligence have made it technically feasible to scale chart abstraction in ways that were previously impossible. But feasibility alone does not equal institutional readiness.

AI-powered abstraction only works at scale when it is validated, monitored, governed, and secure.

That’s infrastructure.

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The Hidden Cost of Department-Level AI Experimentation

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Many institutions are currently in a phase of decentralized experimentation. Individual departments are piloting AI tools for chart abstraction within their own use cases.

On the surface, this feels innovative. In practice, it creates fragmentation.

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For Research Leadership: A Growing Governance Problem

From a CIO, CMIO, or research dean perspective, fragmented AI abstraction creates:

• Multiple security review surfaces
• Inconsistent PHI handling approaches
• Duplicate vendor contracts
• Repeated AI committee evaluations
• Inconsistent validation methodologies
• Difficulty articulating institutional risk posture
  

IRBs and AI governance committees are increasingly scrutinizing how artificial intelligence is used in clinical workflows and research. When every department uses a different abstraction approach, each pipeline must be evaluated independently.

This multiplies regulatory burden and increases institutional risk.

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For Research Operations & Data Teams: Reinventing the Wheel

From an operational lens, fragmentation creates different pain:

• Abstraction definitions recreated and re-optimized for every project
• No shared abstraction libraries
• No standardized validation framework
• Manual review handled differently each time
• No monitoring once pipelines are deployed
• No cross-project performance comparison
  

Each team solves the same abstraction problem from scratch.

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Fragmentation doesn’t just slow institutions down.

It makes validation harder, and risk harder to control.

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What It Means to Treat Chart Abstraction as Infrastructure

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If chart abstraction is infrastructure, what does that actually look like?

It means building a centralized, institutional abstraction layer rather than dozens of independent pipelines.

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1. One Set of Plumbing with the EHR

Infrastructure begins with standardized access to the EMR.

Instead of each team exporting data differently or experimenting with ad hoc connections, the institution builds:

• A secure, privacy-first abstraction environment
• Standardized EHR data pathways
• Deployment inside the institutional firewall
• Organizationally approved LLM endpoints
  

This reduces risk surface and ensures consistent data handling.

For more on security-first architecture, see our post on SOC 2 and institutional deployment at Brim.

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2. Institutional Governance & Access Control

A centralized abstraction layer enables:

• Single sign-on (SSO)
• Role-based access
• Institutional project request workflows
• Consistent audit trails
  

AI committees and IRBs evaluate one well-understood system, not dozens of disconnected tools.

Over time, governance becomes more efficient because the system itself is familiar.

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3. Shared Variable Sets & Institutional Knowledge

When abstraction is infrastructure, knowledge compounds.

Institutions can maintain:

• Shared variable libraries
• Standardized definitions
• Version control
• Reusable abstraction logic
• Documented validation metrics
  

Instead of every research team defining “disease progression” differently, the institution can reuse and refine validated variables across clinical trials, registries, and retrospective research.

Abstraction becomes an institutional asset, not a one-off deliverable.

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4. Validation & Monitoring as System Features

Validation cannot be an afterthought.

Infrastructure-grade chart abstraction includes:

• Built-in validation tooling
• Structured manual review workflows
• Performance tracking
• Drift monitoring
• Clear reporting of sensitivity, specificity, and agreement
  

Without this, LLM-based abstraction is guesswork.

For more on validation frameworks, see our post on metrics and methods for validating AI abstraction.

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Proof Point: 100+ Projects at Vanderbilt

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What happens when chart abstraction is treated as infrastructure rather than as a departmental experiment?

At Vanderbilt, more than 100 projects have run through a centralized abstraction system spanning clinical trials, registries, and retrospective research efforts.

That scale is not possible with isolated pilots.

It requires:

• Shared tooling
• Central governance
• Institutional security controls
• Knowledge sharing
• Familiarity with AI oversight committees
• Consistent validation standards

When abstraction becomes a core capability, the institution gains:

• Speed (new projects start faster)
• Confidence (governance familiarity increases)
• Reuse (variables and workflows compound)
• Institutional trust
  

This is what it looks like when abstraction is infrastructure.

Read more about our institutional deployment at Vanderbilt Medical Center.

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Why DIY AI Is the Wrong Institutional Strategy

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Large language models are easier to access than ever. That accessibility has made it tempting for teams to build their own abstraction pipelines.

But access to LLMs is not the hard part. And a flashy AI demo often doesn't translate to reliable results at scale.

The hard parts are:

• Validation at scale
• Monitoring over time
• Secure deployment
• Institutional governance alignment
• Variable standardization
• Auditability
  

DIY AI approaches often underestimate these requirements.

As discussed in Health Affairs and other forums, healthcare AI is increasingly criticized for being fragmented and insufficiently validated. When institutions adopt AI in 40 different ways, oversight becomes impossible to standardize.

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The real risk is not adopting AI - it's adopting AI in 47 different ways.

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Questions Institutional Leaders Should Be Asking

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For research leadership:

• Do we know how many chart abstraction pipelines currently exist across our institution?
• Are we applying a consistent validation methodology?
• Is our AI governance committee repeatedly reviewing similar tools?
• Do we have a standardized abstraction layer connected to our EHR?
• Can we confidently describe our institutional risk surface?
  

For research operations:

• Can we reuse validated variable definitions?
• Do we have built-in manual review workflows?
• Are we monitoring abstraction performance over time?
• Is training centralized and documented?
• Can we compare performance across projects?
  

If the answer to most of these is “not yet,” the institution likely has pilots, but not infrastructure.

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The Institutional Abstraction Layer

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Brim was built to serve as an institutional abstraction layer for chart curation.

It provides:

• Human-in-the-loop AI workflows
• Structured validation and manual review tooling
• Secure, privacy-first deployment inside institutional firewalls
• Organizationally approved LLM integration
• Reusable variable libraries
• Governance-ready architecture with SSO and audit controls
  

Brim is designed specifically for:

• Clinical trials
• Registries
• Retrospective research
• Operational clinical workflows
  

Rather than enabling isolated experimentation, it allows institutions to centralize abstraction as a core capability, with governance, validation, and reuse built in.

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Stop Running Abstraction Pilots in Silos

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Chart abstraction is not a niche workflow; it is a foundational research capability in any institution that relies on EHR data.

Treating it as a series of isolated AI pilots creates fragmentation, multiplies regulatory burden, and increases risk.

Treating it as infrastructure creates scale, reuse, institutional knowledge, and governance clarity.

Stop running abstraction pilots in silos. Start building chart abstraction as institutional infrastructure.

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Book an institutional demo to explore how a centralized abstraction layer can work inside your environment.

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