Since the beginning of the digital transition in healthcare, unstructured data has presented a major challenge.
For one thing, there’s a lot of it, and perhaps more importantly it’s tricky to determine how best to sift through and organize it.
That’s the backdrop for the recent unveiling by Google Cloud of two AI tools designed specifically to help both healthcare organizations and researchers scan, organize and analyze large amounts of unstructured text.
According to a company blog post, one of the tools, the Healthcare Natural Language API, will enable users to “better coordinate valuable medical insights that are captured in unstructured text, such as vaccinations or medications, that may be overlooked as patients move through their healthcare journeys. This solution can drive measurable outcomes by lowering the likelihood of redundant bloodwork or other tests, reducing operational spending, and improving the patient-doctor experience.”
As Andreea Bodnari, a product manager at Google Cloud, explained in the post, ”For healthcare professionals, the process of reviewing and writing medical documents is incredibly labor-intensive. And the lack of intelligent, easy-to-use tools to assist with the unique requirements of medical documentation creates data capturing errors, a diminished patient-doctor experience, and physician burnout."
The new API is designed to streamline the overall process by identifying “medical insights in documents, automatically extracting knowledge about medical procedures, medications, body vitals, or medical conditions. By using machine learning, the API identifies clinically relevant attributes based on the surrounding context. For example, it discerns medications prescribed in the past from medications prescribed for the future and it picks up the likelihood of a specific symptom or diagnosis, as captured in language nuances. It can also distinguish medical insights that pertain to the patient from information that pertain to a patient’s relative.”
The company describes the other tool, dubbed AutoML Entity Extraction for Healthcare, as “an easy-to-use AI development platform that broadens access to AI across users with various technical backgrounds.”
In other words, the tool is intended to help less experienced users train their own machine learning analysis models, having, for example, a tool that extracts information on patients' relevant gene mutations, or on socioeconomic factors.
To help deliver these solutions to providers, payers, and life science companies nationwide, the company says it’s partnering with a number of key solutions providers. SADA, for example, a Google Cloud solutions provider, believes the new tools will be able to help healthcare customers implement medical analysis projects in days, not weeks.
“The richest information about the health of a patient is typically not found within the structured fields of a medical record system. Instead, it is contained within the lengthy free-text notes that a clinician either types or dictates into the medical record in the course of care,” said Michael Ames, Sr. Director Healthcare and Life Sciences at SADA.