Clinical Data Abstraction Market Map: Finding the insights needle in a data haystack
This is part of Elion’s weekly market map series where we break down critical vendor categories and the key players in them. For more, become a member and sign up for our email here.
Every patient’s medical record holds a treasure trove of untapped information—data that could unlock better treatments, enhance patient outcomes, and drive clinical research forward. However, this data often remains inaccessible, buried within the unstructured formats of electronic health records and paper charts. This is where clinical data abstraction becomes crucial.
When Accurate Data Really Matters
Clinical data abstraction is the meticulous process that transforms raw, unstructured data into actionable insights, playing a vital role in quality improvement, research, and innovation. Yet, the journey to achieve this is anything but straightforward. Clinical data abstractors must navigate outdated systems, fragmented patient records, and endless PDF charts, often juggling multiple machines as they work to extract and structure data. The stakes are high: Missing even a single piece of information can lead to incomplete data, skewed results, or even compromised patient care.
Clinical data abstraction is essential for:
Registries: Populating data for cancer, trauma, stroke, and cardiac event registries.
Regulatory reporting: Submitting data required by agencies for infectious disease, adverse events, or public health compliance.
Clinical trials: Gathering and submitting necessary data for clinical trial research.
Quality measures: Reporting for programs like CMS QPP or internal quality improvement initiatives.
Administrative abstraction: Optimizing healthcare operations and resource utilization.
As optical character recognition (OCR) technology has dramatically improved and large language models (LLMs) have become more adept at converting unstructured data into structured formats, automating much of this manual task has become a real possibility. However, current frontier models still lack the clinical expertise needed to make consistent, accurate judgments, so several vendors have developed tuned models to assist in this process. These models suggest or tag structured forms based on clinical data.
Differentiating Clinical Data Abstraction Vendors
Some vendors have prioritized autonomous workflows while minimizing the risk of hallucinations. These include Mendel AI, which focuses on clinical research, and John Snow Labs, Dyania Health, and Pharos, which cover registries, regulatory reporting, quality measures, and administrative abstraction. Meanwhile, companies like Carta Healthcare offer AI-enabled workflows designed to assist rather than replace human clinical data abstractors, with solutions available as software-only or as part of a managed services approach. On the other hand, fully managed services like QCentrix and HealthCatalyst provide tech-enabled, outsourced clinical data abstraction.
The Future of Clinical Data Abstraction
This technological evolution arrives at a pivotal moment. Most clinical data abstraction functions are decentralized, and the introduction of these tools is likely to draw increased attention from IT departments, eager to unify software across different departments. With approximately 70% of hospitals performing these functions entirely in-house, it will be crucial to carefully pilot and implement these tools across all clinical data abstraction groups. With that said, we think making clinical data abstraction cheap, reliable, and easy will go beyond registries and clinical research; it will become a regular progress for improving processes across the entire health system.