GNS Healthcare Blog

GNS Healthcare Primary Blog

By December 31, 1969

GNS Healthcare Blog

Data and AI Taking Center Stage at BIO 2018

“AI is your rocket ship and data is the fuel."

Michael Dell said this to customers and partners at his annual conference, but the message will be one of the key takeaways for the 16,000 attendees expected at the 2018 BIO International Convention to be held in Boston next week. More than 5,000 companies from 70 different countries will be represented at the gathering that brings together biotechnology and pharma leaders for a week of intensive networking to discover new opportunities and promising partnerships. Discussions cover a wide spectrum of life science and application areas...

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Now that we have data, how do we leverage it to deliver precision medicine?

 

The efforts to generate and gather healthcare data have paid off. Approximately 61% of Americans are using wearable devices to track their health1, nearly 87% of doctors use electronic health record (EHR) systems in their offices2, and as of May 2018, there are more than 96 thousand clinical trial sites in the US generating clinical data3. These millions of data points mean that as much as 30% of the entire world’s stored data is generated in the healthcare industry4. Due to this abundance, we are on the precipice of making precision medicine a reality by better understanding disease and...

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The power of physics and the complexity of biology: How AI is bringing them together

In the 1960s, physicists were trying to figure out what makes particles, such as atoms, electrons, and quarks, have mass. To answer this question, they examined existing known systems and developed complicated mathematical equations to explain and connect them, eventually coming up with something called the Higgs Field. Almost 40 years later, the Higgs Boson particle was proved to exist and is now an essential part of the particle physics model.

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How the next generation of data is moving the needle to make precision medicine a reality

The growth of “big data” has revealed one undeniable truth: the more data available, the better the insights and the more that you can learn.  That is certainly the case in the healthcare industry where biopharma, health plans and providers are generating, consuming and collecting more data than ever before.

Traditional data sources like electronic health system records (EHR), claims, and labs spurred a wave of population health efforts in the early 2000's. These programs promised to reduce costs and improve outcomes for patients, but were based on models of care for a hypothetical average...

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Harnessing New & Traditional Data to Discover Actionable Insights with AI

By Patrick Getzen, Chief Data and Analytics Officer of Blue Cross and Blue Shield of North Carolina

Clashes between the traditional and the novel are as old as time itself. Change is rarely welcomed, so anything new that comes along often faces a rough road to acceptance. However, that is not the case when it comes to the types of data that are now becoming available in the world of healthcare. The merging of new and traditional healthcare data promises to lead to a new era of discovery and offers significant benefits for patients, providers and payers.

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3 Ways Causal Machine Learning is Accelerating the Speed of Discovery in Health Care

Imagine if we could approach healthcare with the precision that retailers like Amazon and Netflix use to reach their customers. Imagine if we could grasp the holy grail of precision medicine and harness the ability to match patients with the specific treatment or intervention that is most effective for them as individuals.

How can we scale precision medicine so that every patient is provided with a personally optimized treatment plan? How can we turn data into models of disease progression and drug response, so we can discover novel biomarkers and accelerate drug discovery and development?

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5 Challenges to Applying AI and Machine Learning in Healthcare

While artificial intelligence and machine learning technologies hold plenty of promise in helping to improve patient outcomes and lower costs in health care, making effective use of these technologies requires expertise and experience in handling massive data sets and the tools that extract the right information to answer healthcare’s most difficult questions.

Julie Slezak, EVP of Clinical Analytics at GNS, highlights the key stumbling blocks she sees most often and how to overcome them. Following are five key challenges. Consider this a giant heads-up to anyone employing artificial...

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