Staying on Track: Validating AI Abstraction as Processes Scale

Key Takeaways

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Creating an AI-driven abstraction process that works is already challenging because of the validation and iteration required. We covered this in our previous blog post: Making Progress: Metrics and Methods for Validating AI Abstraction.

Once you’ve validated your process in a pilot, the promise is clear: less time spent in manual chart review, more scale, and faster access to structured data. But how do you know your pipeline is still working over time? AI models evolve, data can drift, and the stakes are high. Missing patients eligible for a clinical trial or compromising the integrity of a registry is unacceptable.

The field is still developing, but best practices include monitoring key metrics for inputs and outputs, regular human review of a representative sample, and setting alerts when thresholds go out of bounds. Read on to learn more about the problem and how Brim can help.

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Chart Abstraction is Expensive, and AI can Solve That

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Chart review is one of the most expensive and time-consuming bottlenecks in clinical research and quality improvement. At large institutions, the cost of manually reading charts and transcribing data into structured fields runs into the tens of millions of dollars per year.

It’s also slow: abstraction can take 5 minutes for simple quality measures or multiple hours for complex registries. And even then, human abstraction isn’t perfect. A recent study reported that each manual chart review took about 30 minutes, and 36% of those reviews required correction afterward

Reducing that time frees up abstractors to focus on higher-value tasks. For research consortia, that might mean catching up on backlog across sites. In clinical workflows, saved time can translate to more time for direct patient care.

But to realize those gains, you need an AI pipeline that reduces, not increases, the human review burden. That requires rigorous validation upfront, and ongoing monitoring to ensure performance holds as the pipeline scales.

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Creating an AI-Guided Chart Abstraction Pipeline is Challenging

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The reality is stark: 95% of AI pilots fail. In healthcare, the stakes are higher, and the margin for error is lower.

Making AI truly useful for chart abstraction requires building confidence through validation at scale. That means:

• Creating a crisp, measurable problem statement.
• Articulating high but realistic expectations for agreement.
• Investing in a strong golden dataset.
• Iterating and refining quickly.

We explored these principles and how Brim is designed to quickly build, test, and productionize AI-guided chart abstraction pipelines in our earlier post on validation. But once you’ve built a reliable pipeline, new challenges emerge. AI abstraction is not a “set it and forget it” process; ongoing monitoring is essential.

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Data Pipelines Can Experience Drift from a Variety of Sources

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One study defines data drift as “differences between the data used in training a machine learning model and the data applied to the model in real-world operation”.

For this definition of drift, differences in data over time can come from:

• Shifts in patient populations (e.g., new demographics or comorbidities).
• Differences across sites in documentation styles or note templates.
• Clinical context changes (e.g., new treatment guidelines or terminology).

Even with this definition, drift can represent either a meaningful change in the underlying data, or a change in the context that might merit overhauling a chart review process. Identifying the drift and the cause are both challenging.

When adding AI to chart abstraction, signals of drift can also arise from changes in the foundation models or routine adjustments in the prompting system under the hood. These shifts can lead to subtle but significant differences in abstraction performance.

Without monitoring, drift can silently erode the accuracy and reliability of your abstraction pipeline.

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Best Practices to Manage Data Drift

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So how do you avoid the cost of drift? For ongoing workflows, this means ongoing vigilance. One of the simplest and most effective practices is to monitor the characteristics of the uploaded clinical notes for large changes in characteristics. If, for example, the notes that once averaged several pages suddenly shrink to just a few sentences, that may signal a shift in documentation style or workflow. Even small changes in inputs can cascade into big differences in abstraction outcomes.

Monitoring the structured outputs of your pipeline can also detect drift. If the distribution of abstracted values—say, the proportion of patients coded as eligible for a registry—changes dramatically from one month to the next, the pipeline owner should be notified. Shifts in output data could represent true changes in the population, but they could also indicate changes in how the pipeline is functioning. Research is underway to design and improve these drift detection systems.

Another approach, inspired by best practices for manual chart review processes, is to regularly review a representative sample of records. In human abstraction quality assurance, reviewing even 5% of patients can surface recurring errors or inconsistencies before they spread unchecked. While this level of review may not be practical for every workflow, for high-stakes use cases it is often worth the investment.

Finally, successful pipelines benefit from automated alerts tied to key thresholds. Defining acceptable ranges for accuracy, completeness, or error rates, and creating alerts when those ranges are exceeded, ensures problems are caught quickly. In especially critical applications, those alerts can be designed to halt the pipeline entirely until an issue is investigated, protecting both patients and data integrity.

The best practices are still under development, but include:

• Monitor characteristics of input (unstructured) data for large changes
• Monitor characteristics of output (structured) data for large changes
• Consider human review of a representative sample
• Create automated alerts, and in critical cases, allow them to halt the pipeline.

Taken together, these practices create a layered safety net. By keeping an eye on inputs, outputs, samples, and thresholds, teams can stay confident that their AI abstraction pipeline continues to deliver reliable results, even as models and data evolve.

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Brim Can Help

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At Brim, we know that AI chart abstraction doesn’t stop at the pilot. Our platform and team are designed to help you scale with confidence.

Automate with support: Our Solutions Team partners with health systems, research consortia, and registries to design abstraction systems, create validation frameworks, and monitor drift.

Or innovate with our self-serve tools: Brim makes it simple to upload a validation dataset, review outputs, and set up monitoring pipelines with the Brim RESTful API.

Ready to see how Brim can keep your AI abstraction pipeline on track? Book a demo.

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