GNS Healthcare Blog
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.
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 .
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...
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.
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. 
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...
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.
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. Another report has 42 percent of healthcare system leaders saying they have or are planning to add AI as a tool for disease management. Some even suggest AI will eventually replace physicians when diagnosing...
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.
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?
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...