Artificial intelligence (AI) and machine learning are helping revolutionize healthcare, but a newer and more powerful digital application is entering the limelight.
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Deep learning is a subset of AI and machine learning, which is a broader category in the field of computer science. It offers benefit advisers promising new ways to help their employer clients improve healthcare services and consumer experiences.
The focus on this advanced technology, which is becoming an integral part of healthcare data analytics, is on processing massive amounts of information and spotting data patterns. By developing more reliable prediction models, the hope is that earlier intervention will help halt the progression of chronic and costly diseases.
Experts see tremendous potential in terms of streamlining both the delivery and operational efficiency of employer-provided healthcare benefits to improve outcomes and reduce costs.
Deep learning capabilities include analyzing medical imaging and diagnoses, as well as decreasing both the cycle time and cost in drug discovery, says Kerrie Holley, a technical fellow with Optum. He says other applications include reducing out-of-network care, improving auto-adjudicated claims, and creating risk and premium models that use a wider range of data than current actuarial models. Alex Ermolaev, director of AI for Change Healthcare and one of the most frequent speakers on AI in healthcare in the Silicon Valley, notes that deep learning also can improve the accuracy of reimbursement paid to physicians and hospitals, member identification and retention, fraud detection and population health management.
Alex Ermolaev, director of AI for Change Healthcare and one of the most frequent speakers on AI in healthcare in the Silicon Valley, notes that deep learning also can improve the accuracy of reimbursement paid to physicians and hospitals, member identification and retention, fraud detection and population health management.
While both machine learning and deep learning use artificial neural networks based on algorithms inspired by the brain, the latter involves more prolific or dense layers than the former, as well as more math and computer power. Driving forces of this technology include a single-chip processor used mostly to manage and boost the performance of video and graphics, known as a graphical processing unit, and backpropagation, a technique used to train classes of neural networks.
“Everything that we can do with machine learning we can do better with deep learning,” Holley explains. “Deep learning uses algorithms that have set new records in accuracy like in image recognition, sound detection and tone detection.”
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Using deep learning to better predict conditions for high-risk patients and make personalized treatment recommendations can bolster early intervention strategies, improve diagnostics and outcomes, and eventually reduce costs.
Deep learning techniques provide a powerful way to extract hidden correlations among vast amounts of data that may combine in complex ways to affect health outcomes without having to define features of the data upfront, observes Bonnie Ray, VP of data science at Talkspace.[…]