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Healthcare Artificial Intelligence Making Sense of Data Flood

Healthcare Artificial Intelligence Making Sense of Data Flood

December 18, 2018 – Healthcare () and are providing tools to enable medical practitioners and researchers to make sense of the flood of medical data, according to study by Stanford Medicine.

SwissCognitive and other technical innovations are helping ensure data is appropriately cleaned, managed, and shared in the healthcare ecosystem.

“Many roadblocks and inefficiencies that have encumbered this digital transition are gradually being phased out and eliminated,” the noted report, The Democratization of Health Care.

The report cited an Accenture analysis predicting that the market for healthcare would increase at a CAGR of 40 percent and would reach $6.6 billion in 2021.

Accenture identified the following healthcare applications that are expected to generate the most annual benefit by 2026: -assisted surgery ($40 billion), virtual nursing assistance ($20 billion), administrative workflow assistance ($19 billion), fraud detection ($17 billion), dosage error reduction ($16 billion), connected machines ($14 billion), clinical trial participant identifier ($13 billion), preliminary diagnosis ($5 billion), automated image diagnosis ($3 billion), and cybersecurity ($2 billion).

algorithms have become highly sophisticated. For example, algorithms can quickly process hundreds of thousands of medical images.

In fact, a Stanford-developed algorithm for radiology can reliably screen chest x-rays for more than a dozen types of disease in seconds, the report noted.

“[In] the next 2 to 5 years there will be a lot of attention on data analytics and that will allow us to learn from large observational datasets. It will teach us what we do today which we don’t understand, how varied our practices are, and what outcomes we get with them,” Paul Tan, vice president and chief health transformation officer at IBM Watson Health, was quoted as saying in the report.

The Stanford report identified a number of near-term and long-term benefits from . will help healthcare predict and identify public health threats and outcomes for at-risk patients. It will also enable physicians to customer treatment plans and drugs to patients based on factors such as their genetic makeup, habits, and biometrics.

could help to facilitate virtual and mobile device care, providing patients access to a healthcare specialists at any time on any device.

It could also help slow the progression of chronic disease at a population level by ensuring that right care takes place at the right time and in the right setting.

could help reduce US healthcare costs by $150 billion by 2026. “From a business perspective, intelligent computing creates an opportunity for industry players to optimize savings and profitability while still taking advantage of growth,” the report observed.[…]

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