New Health Data Alliance Plans to Advance Care with Machine Learning

machine learning partnership

The Pittsburgh Health Data Alliance is partnering with Amazon Web Services to leverage data-driven advancements in medical technology.

In August 2019, the Pittsburgh Health Data Alliance announced that it will be partnering with Amazon Web Services (AWS) to integrate Amazon’s machine learning capabilities into healthcare services. The PHDA — which includes University of Pittsburgh Medical Center, University of Pittsburgh, and Carnegie Mellon University — hopes to leverage the partnership to improve the efficiency of a number of processes related to patient care, including diagnosis, treatment, and disease prevention.

Here’s what medical marketers need to know about this initiative, as well as the current state of machine learning in the healthcare industry.

Machine Learning Drives Innovation

The PHDA currently compiles a vast amount of patient data — with personal identifying information removed — in order to track patterns in how patients are treated and diseases are prevented. By employing AWS’ machine learning and cloud computing capabilities, the hope is that all this data — which contains health and imaging records, genome profiles, and prescriptions — can be synthesized more efficiently into health services and treatments.

“We believe that machine learning can significantly accelerate the progress of medical research and help translate those advances into treatments and improved experiences for patients,” said Swami Sivasubramanian, vice president of machine learning for AWS.  

The PHDA’s Pilot Projects

The partnership between the PHDA and AWS will begin with eight projects, with the intention of testing machine learning integration across several different areas of healthcare. 

One project will attempt to use machine learning to create an algorithm that determines the best way to treat abdominal aortic aneurysms. According to the PHDA’s press release, this condition is the thirteenth leading cause of death in western countries. At present, the only way to predict the risk of rupture for abdominal aortic aneurysms is by measuring the aneurysm’s diameter and growth rate.

“With the latest advances in machine learning, we are developing an algorithm that will provide clinicians with an objective, predictive tool to guide surgical interventions before symptoms appear, improving patient outcomes,” said David Vorp, associate dean for research at Pitt’s Swanson School of Engineering and the John A. Swanson Professor of Bioengineering.

Another team will use machine learning to create algorithms and software tools to develop a better understanding of how tumor cells evolve. Other projects receiving support from AWS include creating an individualized risk score for breast cancer recurrence on a per-patient basis and identifying medical diagnostic error through the development of an automated diagnosis coding engine that mines patient data from medical records.

“This collaboration with AWS complements the unique strengths of the PHDA’s founders and will provide unparalleled resources to our researchers,” said Tal Heppenstall, who is president of UPMC Enterprises, a major funder of the PHDA. “By leveraging AWS machine learning and artificial intelligence services, we can help Pittsburgh become the premier hub of technology innovation in health care, drawing innovators from companies big and small to join us in this critical effort to revolutionize the delivery of health care.”

Machine Learning Applications

While partnering with Amazon Web Services is a bold step forward for medical innovation, there are a number of ways that AI is already being incorporated into the medical field. A number of leading hospitals and medical centers are committing to using AI in healthcare research, and some estimates predict that $35.8 billion will be spent on AI technology in 2019. 

For instance, Benevolent AI, a company based in the U.K., is using AI technology to sort through disparate medical studies and papers. The company’s goal is to draw connections between data points that could potentially lead to research breakthroughs, including the discovery of new drug treatments

The costs of implementing AI can be steep, and there are certain regulations that will need to be ironed out in the coming years. However, the benefits of using AI in healthcare are becoming more and more evident. By embracing AWS’ powerful technology, the PHDA is setting the groundwork for widespread adoption of machine learning, and ensuring that the future of medicine looks brighter than ever.

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