COTA, Inc., an oncology real-world data and analytics company, announced a new partnership with Google Cloud to develop algorithms that will extract and make sense of unstructured data from electronic health records (EHRs). The companies will apply machine learning and natural language processing to curate text fields like clinician notes, transforming them into structured fields that can be used for research and analytics.
The EHR has revolutionized the way healthcare providers capture data, but challenges related to transforming raw, unstructured health data into fit-for-purpose real-world data remains a massive challenge today. Together, COTA and Google Cloud are entering into a technology partnership to tackle this challenge head-on, with the goal of fueling a new era of transformative innovation in oncology research and cancer patient treatment.
Recommended AI: Consider Your DOOH Buying Methods Wisely: Direct Sales vs. Programmatic Buying
While the vast majority of critical health data is now created and stored digitally, much of the information is still generated in an unstructured format. Free-text clinical notes and PDF documents remain largely invisible to algorithms that can mine structured data fields for key insights into patient care. As a result, clinicians and researchers may be unable to generate a complete picture of the patient’s journey and could miss opportunities to advance the standard of care and treatment options.
“Imagine a scenario where we can be alerted, in real time, to new diseases or receive signals from geographies where patients are experiencing better outcomes, or poorer outcomes, so that we can take action quickly,” said Miruna Sasu, President and CEO at COTA, Inc. “In order for this to become our reality, we must leverage technologies to ingest healthcare data responsibly, accurately, and expeditiously. We are delighted to partner with Google Cloud to combine our respective strengths in technology and data science with the ultimate goal of improving care for patients.”
Today, many leading real-world data companies — including COTA — leverage clinicians to manually curate oncology real-world data. While this is a reliable and trusted near-term solution to overcoming the challenges associated with abstracting and curating unstructured oncology data, it makes scaling this approach across vast amounts of data both time- and resource-intensive.
Recommended AI: Understanding the Role of AI in Gaming
In collaboration with Google Cloud, COTA will look to augment manual, human-led abstraction with technology-first abstraction and curation best practices. This approach will, over time, provide access to even more advanced data elements that may be buried in unstructured notes. For example, next-generation genomic sequencing is becoming particularly important for personalizing cancer care. However, the reports providers receive from the genetic testing labs are often in a PDF format. Traditional tools like optical character recognition (OCR) can’t accurately “read” the text in these PDF images, so this data often goes unused today.
“We are collaborating with COTA to build a series of new natural language processing models tailored specifically to unstructured oncology data, including emerging data such as genomic sequencing,” said Shweta Maniar, Director of Life Sciences Industry Solutions at Google Cloud. “By training these algorithms specifically on oncology information, we will partner with COTA in generating a much more complete understanding of what is happening in the cancer care setting and how a patient’s unique clinical history may impact their response to treatment.”
Recommended AI: “Bitcoin Has No Intrinsic Value”. Then What Gives Bitcoin Value?
[To share your insights with us, please write to sghosh@martechseries.com]
Comments are closed.