RxDataScience Blog

The ETL (extract, transform and load) process by which organizations prepare data for storage is an essential part of modern database systems, particularly used for business intelligence applications. The problem is that it can be inefficient and slow — too slow for companies to do real-time and streaming analytics.

Within the first decade of the 21st century, the use of big data became very popular in many big industries. The methods for capturing big data have since evolved from traditional data lake systems to more integrated technologies that combine big data with all other systems within a company.¹

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.

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?”

Anonymous patient-level data (APLD) is data collected in real time from an individual patient. There has been an increasing interest in patient-level data, as researchers, healthcare providers, and pharmaceutical companies are realizing the potential of creating better comparisons of effective treatment outcomes by analyzing long-term data that represent individual patient-based experiences.

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.

Data-driven from the beginning

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

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.

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.

• 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