GNS Healthcare Blog

GNS Healthcare Blog

GNS Healthcare Primary Blog

By December 31, 1969

The Curse of Dimensionality

 This is the first of a series on the question of data availability, structure, value and challenges.

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If at First You Don’t Succeed…Learn from Clinical Trial Failures

The oft-told story is that it took Thomas Edison’s 10,000 attempts to perfect the invention of the light bulb. He is famously quoted as saying of his many prior unsuccessful efforts, “I have not failed. I’ve just found 10,000 ways that won’t work.”

There is an ongoing discussion as to whether the biopharma industry should adopt a similar attitude when it comes clinical trials. A recent article in BioCentury outlined the clinical trial tug-of-war between traditional frequentists and Bayesians who have been making their way into the arena, starting first in the medical devices field....

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Celebrating International Women’s Day: Healthcare Innovators

Tomorrow, March 8th, is International Women’s Day. While a public holiday in just a few countries, it is a day to celebrate the success and power of women from gaining suffrage to working their way into the board room. As such, we wanted to recognize some of the women in healthcare (and take it from us we could have featured hundreds if we had the room) who are leaders in their fields and driving innovation within their respective organizations, and healthcare as a whole.

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Lowering Barriers: How Real-World Data and AI Is Transforming Drug Market Access Strategy

This is the third in a three-part series on the impact of AI on the product development life cycle of drugs from discovery, to clinical trials and market access.  

Regulatory approval of a drug does not immediately translate into premier market access or projected results. The reality is that the pressure on pharma companies from health plans and pharmacy benefit managers (PBMs) to provide evidence-based research on treatment efficacy and effectiveness before including drugs on their formularies is only increasing.

The earlier pharma companies can answer these asks, the better. The goal is...

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The New World of Drug Development: Transforming Clinical Trials with AI

The following is the second in a three-part series on the impact of AI on the product development life cycle of drugs from discovery, to clinical trials and market access.

The success of clinical trials relies heavily on having enough of the right patients willing and able to participate. Unfortunately, achieving the levels of recruitment necessary to conduct successful trials is difficult – particularly in cancer and rare diseases trials.

One recent survey revealed that only one in three cancer patients were likely to enroll in a clinical trial. Less than half of those surveyed viewed...

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The Promise and the Power of In Silico Clinical Trials

Clinical leaders at Dana-Farber Cancer Institute for Neuro-Oncology were becoming more frustrated with the lack of treatment options for patients suffering from glioblastoma (GBM). The aggressive form of brain cancer, which recently claimed the life of Senator John McCain, is one of the deadliest forms of cancer and has no known cure. Most people diagnosed with the disease die within 15 months and a meager 15 percent survive past five years¹.

Not satisfied with the slow pace of traditional randomized controlled trials studying GBM, Dr. Brian M. Alexander at Dana-Farber decided to act. A...

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Talent, Data, and Tech: An Inclusive Approach to Drug Development

Interest in applying artificial intelligence and machine learning to healthcare has reached a pinnacle. Every day there are a slew of new articles and reports discussing the various ways that algorithms can transform the industry and there are many interesting technologies being developed and deployed—from digital health technologies that aim to make care delivery more patient-centric to machines that use image recognition to identify tumors before the human eye can see them.

A big area of potential growth for the use of AI is in precision medicine— the ability to customize the treatment...

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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 value-based...

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