Beyond DIY AI: Experiments are Easy, but Scaling is Hard

Key Takeaways

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An MIT study found that 95% of generative AI pilots fail. In healthcare, where accuracy and trust are non-negotiable, that statistic should give every CIO, researcher, and clinician pause.

With this kind of failure rate, it's easy to see why so many organizations try to “DIY” their own solution to test it more quickly. Building a quick LLM wrapper for chart abstraction feels simple at first: extract one value from one clinical note, and you’ve got a demo that works.

But scaling that workflow across patients, documents, and sites is an entirely different challenge. Add requirements like validation, traceability, and ROI measurement, and the homegrown approach quickly becomes complicated for even a single workflow.

That’s why we built Brim: a purpose-built, AI-guided chart abstraction platform designed to enable quick iteration, rigorous validation, and reliable scaling in clinical and research environments.

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The Illusion of Simplicity: Homegrown LLM Wrappers

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“We’re going to write a script to solve that ourselves.” – IT Manager

“Ok, but where is it?” – Clinician, researcher, or colleague waiting on results

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AI is an exciting development in healthcare data teams, partly because it's easy to try. You simply whip up a script, plug in an LLM, and extract a data point from a single note. It works for the demo. But the problems emerge as soon as you try to operationalize it.

• Scaling reality: Adding patients, documents, and abstraction variables multiplies complexity. Suddenly, you’re wrestling with rate limits, model context windows, and prompt tuning, all while trying to filter out hallucinations.
• Repeatability: Do you really want colleagues emailing you every time they need data abstracted? To avoid brittle email-based communication, you'd need to add integrations for data input and output, the ability for colleagues to modify prompts themselves, and more. Repeatability and automation are essential.
• Validation bottlenecks: New workflows need validation before they're productionized. Without a system, validation defaults to spreadsheets sent back and forth by email. Tweaking prompts becomes guesswork instead of a measurable process.
• Reliability: What happens when the model changes, data distribution shifts, or a new team member takes over? Without monitoring, you won’t know until it’s too late.

DIY wrappers are quick to build, but they’re brittle, and they rarely hold up under clinical-grade requirements.

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Scaling AI-Guided Chart Abstraction Across Patients and Sites

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Real-world healthcare data is messy, and scaling AI chart abstraction means dealing with that complexity head-on. It isn’t enough to simply prompt an LLM a few times; reliable abstraction requires infrastructure that enforces consistency and accuracy at every step. Data types, formats, and scope need to be tightly defined so that results are reliable and comparable across patients and projects. And as you scale, you hit practical constraints: context windows and rate limits become major bottlenecks when processing thousands of documents.

Abstraction also rarely happens in isolation. Clinical information is scattered across multiple notes, encounters, and care settings, so results need to be aggregated in a way that preserves accuracy and context. Different data points also need different strategies: a clinician wants the first data of diagnosis, but a list of every unique cancer site, and the latest date they've visited a clinic. These are all deterministic logical pathways that LLMs are not good at handling.

Another challenge is hallucination. Left unchecked, LLMs will fabricate outputs. Guardrails must be in place to filter and constrain responses so that only verifiable information in the desired format is returned.

Finally, every result has to be traceable. Clinicians and researchers need to see not just the extracted value but also the source text it came from. This evidence trail is what builds trust in the system. In the end, scaling chart abstraction isn’t about writing more prompts; it’s about building the scaffolding that makes prompts trustworthy.

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The AI Validation Wall: Golden Datasets, AI Evals, and Drift Monitoring

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Even if a team manages to abstract values at scale, a tougher problem remains: proving that it actually works. Demonstrations are easy, but reliable evaluation is where most efforts break down.

The first challenge is building labelled datasets. To measure accuracy, teams need carefully curated gold standards, and those take time, money, and expertise to produce. On top of that, ground truth isn’t established by a single reviewer. It requires multi-curator review, comparing multiple annotators and calculating inter-rater reliability to understand what “correct” even looks like in a messy clinical context.

Then comes the question of optimization. Prompts can’t just be written once and forgotten. They must be tested, refined, and reused across projects and sites. One-off tinkering doesn’t scale, and without systematic evaluation pipelines, organizations find themselves repeating the same trial-and-error process endlessly across different pilots.

Finally, even the best workflows don’t stay reliable forever. Models change versions and data shifts, which means performance can degrade silently unless it’s actively monitored.

This is the “validation wall” that threatens pilots. Without robust evaluation and monitoring, teams can’t prove value, can’t win stakeholder confidence, and ultimately can’t scale beyond the pilot stage.

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How Brim Powers Reliable Chart Abstraction at Scale

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Brim was designed to do what DIY approaches cannot:

• Standardized workflow builder: Non-programmers can design and run chart abstraction workflows in a repeatable, auditable way. Our API makes it easy to hook in existing workflows once you're ready.
• Built-in validation & AI evals: Compare outputs against validation datasets directly within the workflow, so you understand performance and improvement instantly.
• Clinician-in-the-loop feedback: Integrated review tools make it easy for experts to refine and trust outputs.
• Multi-site scalability: Reuse variable definitions and workflows across institutions, networks, and studies.
• Secure deployments: Run Brim on-premise and with your own LLM, keeping all PHI inside your firewall. Brim is SOC 2 Type II and HIPAA compliant.

With Brim, organizations move past one-off scripts and into sustainable, reliable AI-guided chart abstraction.

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Conclusion

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Building your own AI chart abstraction solution may seem simple, but hidden complexity derails most in-house efforts. From scaling to validation to ROI, the gap between a demo and a production-ready solution is wider than most teams realize.

Brim is built to bridge that gap, giving healthcare organizations the infrastructure to abstract clinical data accurately, securely, and at scale.

Sound interesting? Book a demo to see how Brim can operationalize AI for your use case.

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