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Machine learning is the new key to healthcare

‘Machine learning’ promises to transform healthcare, but there are many challenges on the way, writes ARTHUR GOLDSTUCK



One of the new tools already in use, Amazon Comprehend Medical, is already proving itself. Partovi describes it as “a highly accurate natural language processing service for medical text, which uses machine learning to extract disease conditions, medications, and treatment outcomes from patient notes, clinical trial reports, and other electronic health records”.

The beauty of Comprehend Medical is that requires no ML expertise, no complicated rules to write, no models to train, and it is continuously improving.

It is in use at the Fred Hutchinson Cancer Research Centre, home to three Nobel laureates, where interdisciplinary teams of world-renowned scientists seek new ways to prevent, diagnose and treat cancer, HIV/AIDS and other life-threatening diseases.

Matthew Trunnell, chief information officer at the institution, gives a powerful perspective on its use: “Curing cancer is, inherently, an issue of time. For cancer patients and the researchers dedicated to curing them, time is the limiting resource. The process of developing clinical trials and connecting them with the right patients requires research teams to manually sift through mountains of unstructured medical records to look for treatment insights. Amazon Comprehend Medical can reduce this time burden from hours to seconds. This is a vital step toward getting researchers rapid access to the information they need when they need it so they can find actionable insights to advance lifesaving therapies for patients.”

In South Africa, the Department of Health is working to create paperless hospitals in the country.

Partovi spells out the clear benefits: “Paper-based clinical records in healthcare facilities result in extended patient waiting times – sometimes between 60 and 80 minutes; reduced quality of care due to files being lost in overcrowded filing rooms; and increased litigation costs. Using Amazon Comprehend Medical, regional public hospitals implemented indexing systems for easier retrieval of scanned patient records.

“Called Hybrid e-scripting, the solution enables electronic data storage without typing, an e-sketch pad for electronically accessible medical diagrams, and easy-to-use automated pharmacy labelling to reduce medication dispensing time. The impact to implementing this solution was a 90% reduction in patient wait time for fulfilling prescriptions, a 10% reduction in patient hospital wait time, and a cost savings of R1- million in software licenses.”

And then there is the Covid-19 pandemic and the massive challenge of tracking exposure. History may well record this as one of the great triumphs of AI and ML. For example,  South African developer A2D24,  was in just three days able to develop and deploy an automated AI platform using Amazon Lex for a private hospital group, to inform anyone who has been in one of their hospitals of possible exposure to a confirmed COVID-19 patient.

The system automatically sends an alert message and conducts an SMS based triage where, each day, patients are asked questions about the type of symptoms they are experiencing and, based on the responses, the chatbot recommends what to do, and whether to seek further medical help.

“This application has helped provide critical care to thousands of patients and staff across the country, and potentially prevent many new infections,” says Partovi.

The fundamental issue of messy patient data remains, however, he says.

“A challenge to fully realising the potential of ML in healthcare is health data locked in unstructured medical text, which makes applying ML time consuming, costly, and sometimes impossible. Having patient medical data consolidated into a secure data lake, organized in a standard format, and properly indexed with key entities identified, is key to making this vast amount of information searchable and ready for applying ML learning on top, to derive insights and relationships for improving patient care.”

Get that right, and the long-term potential is incalculable.

Says Partovi: “We’re seeing a renaissance in healthcare as more of our customers leverage ML technologies to uncover new ways to enhance patient care, improve health outcomes, and ultimately save lives.”

  • Arthur Goldstuck is founder of World Wide Worx and editor-in-chief of Follow him on Twitter and Instagram on @art2gee

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