GNS Healthcare Blog

GNS Healthcare Primary Blog

By December 31, 1969

GNS Healthcare Blog

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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NASH: The Lifestyle Disease on the Rise and the Role of AI

When most people hear or read about liver disease they most likely think of hepatitis or perhaps alcohol-induced cirrhosis. Though serious diseases, there is a more prevalent liver disease that is on the rise and affecting an estimated 16 million Americans [1].

The disease, NASH (nonalcoholic steatohepatitis) is expected to be the primary reason for liver transplants by 2020. Patients that have it experience few or no symptoms—most don’t even know they have it until it leads to cirrhosis or liver failure.  Worse, there are currently no approved medicines to treat it. The cost of the...

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How Precision Medicine Is Driving the Conversation Across the Healthcare Ecosystem

Not so long ago key stakeholders in the healthcare industry tended to operate in their own separate worlds. Biopharma companies, health plans, providers and healthcare consumers certainly interacted, but in most cases they each focused on dealing with their own specific challenges.

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3 Ways Machine Learning is Transforming Drug Development

A recent study revealed that nearly half of all pipeline compounds and close to three quarters of oncology compounds are utilizing biomarker data during the drug development process. The same report indicated that investment in biomarker identification by biopharma has doubled over the past five years and is forecasted to increase over the next half decade. [1] 

Biopharma’s increasing reliance on molecular data (most commonly genomic and proteomic) and the identification of specific biomarkers in the drug development process should not be surprising. Healthcare is transitioning to...

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The Data Analytics Pyramid – Climbing to Optimization & Inference

The availability of data combined with the power of Artificial Intelligence (AI) is causing disruption and raising questions across a number of industries, including healthcare. The electronic medical record has provided digitized health information. Genomic data is now working its way into datasets. There have been impactful innovations in the areas of targeted intervention, drug and device development, software applications, population health approaches, collaboration among healthcare stakeholders and, precision medicine.

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How AI is Replacing Prediction with Discovery Using Data

Hardly a day goes by without someone publishing an article on how artificial intelligence (AI) is revolutionizing the healthcare industry. No doubt AI is impacting multiple areas of the healthcare landscape from biopharma to health systems, to heath plans to patients.

One recent survey reported that 90 percent of pharma companies believe that AI is critical to their success.[1] Another report has 42 percent of healthcare system leaders saying they have or are planning to add AI as a tool for disease management.[2] Some even suggest AI will eventually replace physicians when diagnosing...

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A Powerful Partnership: Artificial Intelligence and Longitudinal Data To Better Understand Multiple Myeloma

More than 30,000 people a year are diagnosed with multiple myeloma in the United States, making it the second most common type of blood cancer. Researchers have yet to find a cure, in part due to the diverse ways the disease manifests itself, but recent advances in cancer research have made it a treatable disease.

The availability of genomic data and power of artificial intelligence is driving more progress in the understanding of multiple myeloma.  Causal machine learning (causal ML) – a powerful form of artificial intelligence – has the ability to reduce the time to study disease and...

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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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Why Causal Learning is a Superpower for Healthcare

Most people have never heard of causal learning, but the concept is an important one for the future of healthcare because it holds the key to understanding and fighting disease and improving outcomes. However, to understand how and why causality is so important in the fight for better healthcare, we have to first understand machine learning.

You may be familiar with the term “artificial intelligence”, which is a broader umbrella term that machine learning falls within. An article in Xconomy published earlier this year goes into detail on artificial intelligence (AI) and starts by talking...

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