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Machine Learning and AI in Healthcare: Practical Applications (Part 2)

How can AI technology improve the healthcare industry?

Our last blog post explored the implications of a recent study on artificial intelligence (AI) and machine learning business application. While the healthcare industry can expect incredible benefits from the adoption of AI technology, it remains under-adopted and receives below-average attention when compared to other industries.

This post aims to highlight the areas within healthcare where AI has potential. This technology can improve operations, patient experiences, medical procedures, and solve regulatory challenges.

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Machine Learning and AI in Healthcare Coming Up Short (Part 1)

An independent study on machine learning and artificial intelligence (AI) was released by the McKinsey Global Institute (MGI) in June 2017, focusing on the following central question: “Is artificial intelligence the next digital frontier, and if so, are businesses ready for it?”

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Using Analytics to Address the Most Common Patient Journey Challenges

A patient’s journey as they navigate and choose from the many treatments and options available to them is highly unique. The sheer number of variables can be overwhelming. Aggregate patient journey information is locked in APLD (anonymous patient level data) sources like claims, prescription, and EMR data sets. Logically sifting through multiple multi-billion row data sets looking for actionable insights is important for today’s pharmaceutical manufacturers. It requires the most leading edge possible data infrastructures to handle at scale.

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Pharma Big Data Use Case: A Mid-Market Pharma Success Story

Data-driven from the beginning

RxDataScience began as a small team at a mid-market pharmaceutical manufacturer, led by Larry Pickett as CIO. 

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The Advent of Data Science

In the pharmaceutical industry, research into discovery of novel compounds to fill unmet medical needs has long been the driver of growth. This, combined with a strong commercial focus targeting the right messages to prescribers, solving medical problems, and improving patient outcomes, has led to unprecedented growth in the industry with patients as the primary beneficiaries. Now, a new discipline—data science—is emerging as a competitive weapon to disrupt the traditional approach to creating economic value and driving business growth for pharma companies.

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Q&A with Larry A. Pickett, Jr., CEO of RxDataScience Inc. Applying Data Science and Predictive Analytics to the Healthcare Data for Improving Patient Outcomes and Lowering Costs

Interview conducted by:
Lynn Fosse, Senior Editor
CEOCFO Magazine

CEOCFO: Mr. Pickett, what is the vision for RxDataScience?

Mr. Pickett: Our vision is to apply data science to healthcare data to improve patient lives worldwide. The focus is on applying advanced predictive statistical techniques to either very large or very complex healthcare data sets. The goal is for these efforts to lead to improved patient lives and patient outcomes. We are also hoping to use some of our algorithms and solutions to be able to lower, or help control, l the cost of care, which is a big issue for patients and the healthcare community.

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Kx technology to support RxDS as it moves to disrupt healthcare and pharma analytics market

  • Kx selected by RxDS as the platform for its data analytics solutions
  • Solutions based on Kx will unlock the data within pharma and healthcare companies to allow better and more rapid decisions
  • RxDS aims to disrupt market valued at US$ 24.6 billion by 20211
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