Human Expertise at Disposal to Ascertain Which Discoveries In AI &...
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Human Expertise at Disposal to Ascertain Which Discoveries In AI & ML are Worthy

By Pharma Tech Outlook | Friday, April 05, 2019

It has been well established that, even with the advent of Electronic Health Records (EHR), today physicians spend their most of the time on administrative tasks, mainly documentation. Emergency department physicians actually spend 43 percent of their time entering data into a computer, which translates into about 4,000 clicks in a 10-hour shift, according to The American Journal of Emergency Medicine. As AI technologies evolve and take hold in the setting of the exam room, doctors need not spend most of their time documenting every patient visit detail. This facilitates the physicians to spend more time face-to-face with patients while in the background, technologies such as ambient speech and applications powered by deep learning operate, gather, and screen the information to make it workable.

As the business model in healthcare overhauls from fee-for-service to value-based, outcome-oriented care, pre-FDA approval success in controlled clinical trials isn't enough to add a drug to a form or get an approved device. So instead, payers want a real-world setting to see the value. Machine learning gives that evidence by combining medical and pharmacy data to show how outcomes, such as total care costs, or the rate of hospital admissions, and emergency room visits, differ between the drug of one organization and that of a competitor over two to three years. With this in-hand data, sales enabling teams can demonstrate to payers and providers how the offering of the organization enhances results and reduces risks for different populations, making it easier to increase sales.

When a drug comes off-patent, organizations in the life sciences can use machine learning to realize which providers or patients are less willing to move to a generic based on past patterns. Then the organization can focus solely its efforts on persuading others to remain with the drug in order to increase their sales and safeguard the brand.

Yet another area where machine learning can indeed help organizations in the life sciences is their efforts in sales and marketing. It can ensure that these organizations reach the right doctors at the right time with the right message. It can also ensure that the message was delivered in the way that each physician can best consume it. Through machine learning, organizations in the life sciences can uncover which messages to deliver to whom and when to act as a true, value-added partner instead of simply being perceived as a product seller.

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