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Big Data Application in Healthcare For Effective Patient Treatment

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

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Projected Pharmaceutical Data Analytics Trends 2018

Advanced analytics necessitate the use of complex statistics and mathematical systems to improve the business operations of a company. Evidence shows that companies that use advanced analytics have repeatedly reduced variability in product quality and simultaneously lowered costs and increased sales.

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Leveraging the Power of Real-time ETL for Better Pharmaceutical Insights

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.

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How the 21st Century Cures Act and Real World Evidence Impacts the Drug Approval Process

The 21st Century Cures Act

In late December 2016 and with only three days remaining in session for the year, the U.S. Congress passed the 21st Century Cures Act, now commonly referred to as the Cures Act. This act laid out goals to “accelerate the discovery, development, and delivery of 21st century cures.” The law authorized $6.3 billion in funding, mostly for the National Institutes of Health. The Cures Act was first introduced nearly two years prior by seven bipartisan cosponsors, many of whom resided on the Science, Space, and Technology Committee. The legislation was supported by pharmaceutical manufacturers, as it had several important measures that would allow the healthcare and pharma industry to make more informed decisions to ultimately improve the patient journey. President Obama said the Cures Act will “modernize research and accelerate discovery” using data so that “treatment and healthcare can be tailored to individual patients.” It is a highly significant piece of legislation that will impact every pharmaceutical company and the way in which they manage, analyze, and leverage their real world data.

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Data-Driven Decision Making in Healthcare with RxDataScience

Providing data-driven healthcare solutions for better patient outcomes

Earlier in the year, our CEO Larry A. Pickett, Jr. gave a presentation at the CED Tech Venture Conference on RxDataScience’s solutions. There, he discussed RxDataScience’s ultimate goal of providing data-driven decision making in healthcare for better patient outcomes and lower costs of care. To achieve that goal, we’re helping companies unlock the value from their data.

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Getting the Most Out of Longitudinal Patient Data

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.

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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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