A Guide to Chart Abstraction

Chart abstraction is the process of transforming unstructured data in patient medical records into structured data that can be used for clinical workflows, clinical trial eligibility, registries, research, and more. It’s central to many activities in healthcare in the age of EHRs, yet it has many challenges, including high cost, inaccuracy, and inflexibility.

Key Takeaways

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• Chart abstraction is essential to clinical workflows, clinical trial eligibility, registries, and research.
  
• Manual abstraction is expensive, time-consuming, and prone to error.
  
• AI offers speed and flexibility, but also comes with risks around accuracy and data privacy.
  
• AI-guided abstraction with Brim combines the speed of AI with the accuracy of human oversight.
  
  

What is Medical Chart Abstraction?

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Chart abstraction is “the process of collecting important information from a medical record and transcribing it into discrete fields”. 

In today’s healthcare systems, where a wealth of patient data is captured in unstructured clinical notes, abstraction is what makes that data usable. Whether you’re trying to build a registry, match a patient to a trial, or manage clinical risk, abstraction creates actionable structured fields from rich clinical narratives.

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Why Chart Abstraction is Critical for Healthcare

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With the rise of electronic health records (EHRs), healthcare now holds more data than ever, with a lot of it in free-text form. To make it usable, especially across systems or populations, it must be abstracted.

Some examples:

• Clinical Workflows: For example, in an ambulatory surgery center, abstracted data helps identify high-risk patients who should be rescheduled in a higher-acuity setting.
  
  
• Registries: Registries like those run by AACR or in cardiology require standardized data fields derived from unstructured notes to power research and best-practice improvement.
  
  
• Clinical Trial Matching: Only 3–5% of eligible patients enroll in trials, often because eligibility data is buried in notes and matching relies on individual clinicians.
  
  
• Research: For example, chart data powers drug repurposing and real-world evidence studies. You can learn more about this kind of use case in this Brim case study.
  
  

What is the Chart Abstraction Process?

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Chart abstraction is traditionally a labor-intensive process involving multiple steps:

1. Define What You Need: First, decide what variables or fields you need abstracted, for example: diagnosis date, lab results, medication history. These fields often come with detailed semantics, explanations, and examples to ensure that the abstraction team gets correct and consistent data.
   
   
2. Assemble a Team: Bring in trained abstractors—clinicians, researchers, or support staff—who understand both the domain and the context of the records. Small projects might be completed by a clinician or a student, with large projects requiring many abstractors working in parallel.
   
   
3. Search and Extract: Abstractors comb through each patient record, identify relevant evidence, and enter structured values into REDCap, Excel, or custom databases. This process may require working in multiple pieces of software simultaneously, and doing some copying and pasting.
   
   
4. Validate: To ensure consistency and accuracy, abstracted values are often reviewed against a “golden” dataset or adjudicated between abstractors. This process is not possible in every project.
   
   

What are the Primary Chart Abstraction Challenges?

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Cost and Time

Chart abstraction is expensive. One retrospective study estimated it would take 8–25 full-time staff to capture just one type of cancer recurrence at a single hospital.

This is unsustainable in an environment where hospitals already face workforce shortages and budget constraints. If the cost of accurate data is this high, we run the risk of not getting the data we need. 

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Inaccuracy

Manual abstraction isn’t perfect. A review of 93 papers found error rates ranging from 2 to 5,019 errors per 10,000 fields. That variation speaks to how dependent abstraction is on both individual judgment and consistent interpretation, as well as human factors such as boredom and fatigue.

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Inflexibility

The California Healthcare Foundation described how “data entered manually can be input into the EHR in a structured way that allows providers to act upon it and that can drive alerts and care protocols.” But the key issue is: what data? 

The definition of “important data” changes depending on context. A value that matters for patient care might not be relevant for a registry. This lack of standardization makes it difficult to scale manual abstraction across diverse use cases.

Preferred definitions can also vary between practices and providers, or evolve over time. Manual abstraction requires costly updates to training and even past data to shift definitions.

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Can Chart Abstraction Be Outsourced?

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Chart abstraction can be outsourced, but the solutions vary in accuracy and savings.

• Outsourcing to Students, Nurses, or External Vendors: This can ease the burden on clinicians, but still carries high time and cost requirements. Accuracy may decrease depending on how close the abstractor is to patient context.
  
  
• Traditional NLP: Natural language processing can automate parts of the process, but requires technical expertise and is hard to adapt when your data needs change.
  
  
• AI-Guided Abstraction with Brim: Brim offers the best of both worlds. You define what you need using no-code tools, and Brim’s AI drafts abstractions in minutes. Your team can review as much or as little as needed depending on your use case—yielding 95%+ accuracy with a 75%+ reduction in time even if you review every value.
  
  

Brim’s approach to abstraction enables digital transformation for organizations, at a fraction of the cost of manual abstractors. 

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Chart Abstraction and AI

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AI can supercharge chart abstraction with its scale and contextual awareness. However, AI comes with risks that must be properly addressed.

• Data Privacy: Many AI solutions require PHI to be sent outside the health system, increasing regulatory risk by sending the data to a different surface.
  
• Accuracy and Cost: Complex reasoning with large language models is error-prone. It is also costly because it requires the use of the most advanced models and possibly fine-tuning as well.
  
  

Brim solves both of these problems because it is designed specifically for chart abstraction.

• Data Privacy by Design: We deploy in your environment, so no PHI ever leaves your network. We also use foundation models so that your institution can limit the surfaces to a few trusted providers.
• Modularity for Traceability: We deploy a modular abstraction design to break complex questions into smaller, easier-to-answer pieces. This enables high accuracy while using fast, cost-effective models like GPT-4o Mini.

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Bottom Line

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Chart abstraction is the hidden engine behind research, registries, clinical trials, and countless care decisions. But traditional approaches are too slow, costly, and brittle to scale. AI offers a faster, more flexible path, but only if implemented with care for accuracy and security.

Brim gives you a better way:

• Define your abstraction needs with no-code tools.
  
• Let our AI generate high-quality abstraction drafts in minutes.
  
• Review only what matters, saving time while improving consistency.
  
  

Whether you're running a retrospective study, building a registry, or identifying trial candidates, Brim gives you the speed of AI with the reliability of expert review. Request a demo to see how Brim can supercharge your use case.

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Chart Abstraction FAQ

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What does abstraction mean in a database?

Abstraction in a database context refers to summarizing or restructuring data to make it easier to query and interpret, such as turning a patient’s narrative chart into specific, searchable data fields.

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What is a good example of an abstraction?

An example would be extracting the diagnosis date of diabetes from a physician’s note and storing it as a structured field like first_diabetes_diagnosis_date = 2021-03-12.

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Can AI improve chart abstraction?

Yes, AI can dramatically speed up abstraction and reduce cost. When combined with human review, it can also maintain or even improve accuracy.

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