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From scanned medical records to precise medical analytics in an hour

Published On
April 13, 2021
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The FDA has requested the removal of a drug from the market because it contains a high level of NDMA that may cause cancer.

A mass tort lawsuit against the drug manufacturer was submitted.  An enormous number of plaintiffs took part, arguing that they have suffered from cancer as a result of using the drug.

An enormous number of plaintiffs = a tremendous amount of medical records.

Reviewing hundreds of medical records and extracting thousands of medical data points in an hour.

One of our clients asked us to analyze a few hundred medical records of different plaintiffs to extract all references of drug names, oncology and oncology-related conditions, and procedures (with the specific dates of occurrence).

Using DigitalOwl's AI solution, we reviewed and summarized all the cases in less than an hour.

DigitalOwl's unique NLP engine extracts all the key medical information and filters it by medical field.

Fast turnaround time, high accuracy, and meaningful analytics

The use of advanced Natural Language Processing (NLP) technology, like DigitalOwl's, enables not just fast turnaround time (almost on demand) and a high level of accuracy of the medical record summary, but also the capture and reporting of the actual medical data points.

Having all the medical information such as conditions, medication, body parts, procedures, and dates as structured data points allows us to generate analytics that answers questions like:  How many plaintiffs suffer from oncological conditions?  How many plaintiffs have a specific kind of cancer?  How many plaintiffs were diagnosed with cancer after taking the drug, and how many before?  What is the average elapsed time from first taking the medication to first oncological diagnosis? How many plaintiffs have a family history of cancer?  And more.

A sample scatter graph representing the % of the oncological findings that accrued after using a specific drug vs. other medications (the graph was made using the extracted data points).

Fast turnaround time for accurate summaries of plaintiffs’ medical records is a powerful tool in the litigation toolbox. The analytics that can be produced from the summaries' data points can be a game changer.

Yuval Man
Co-Founder & CEO
About the author

As the Co-Founder & CEO of DigitalOwl, Yuval Man empowers insurance companies to unlock the full potential of their medical data for better outcomes by harnessing the transformative powers of AI to streamline and elevate the review of medical data.