Invitae

Stewart Fossceco

As Vice President of Quality Analytics at Invitae, Stewart Fossceco is responsible for driving quality and continuous improvement strategies for quality analytics, enabling the overall strategic plan, corporate quality policy, quality system objectives, and continuous improvement initiatives. He leads diverse, cross-functional teams across statistics, IT, regulatory, clinical, quality, supply chain, marketing, and external partners.

In an interview with Life Science Review magazine, Fossceco shares his valuable insights on how economics and outcomes research can enable personalized treatments for patients. 

In light of your experience, what are the challenges you've witnessed in the health economics and outcomes research space, and how do you address them? 

We are in an exciting time as we are just beginning to incorporate genetics and molecular information into our constructs for utilizing health economics, outcomes research and real-world evidence. To that end, we will utilize genetic testing to bring more focused- personalized treatment for patients that add greater value for them. Tests on a genetic and molecular level that will predict what treatments will work for patients is paramount to achieve these goals. We need platforms that can be used across multiple therapeutic areas, and therapies that can be rapidly personalized to deliver accurate and precise patient outcomes.

“I would advise young professionals to improve their quantitative skills, as we rely on large volumes of data to improve patient care”

In order to achieve that, we have to be able to gather the data that allows us to discriminate between which patients will benefit and those that will not. Historically, we've tried to develop drugs for large populations of people, and here we are talking about trying to narrow that down into those treatments that are very personalized for smaller groups of individuals, and even on an individual basis, which has the potential to drive up costs. Health economics and outcomes research, to this end, enable us to examine cost containment to benefit a much broader segment of demographics.

However, that's very difficult to do with clinical trial data alone. So, the collection of health outcomes data in the form of patient registries, clinical data collected through label extension, and also open-label studies that can go on for longer periods becomes very helpful in that regard. Those extended trials then begin to provide us with baseline information to evolve genetic panels.

Just as diagnostic platforms will evolve, so will the tools needed for genetic prediction through enhanced quantitative analysis methodologies utilizing these disparate information. As a result, we will be in a better position to separate randomness from the real signals.  We will also be able to create historical controls within the context of genetic and molecular information, which will ultimately help enhance medical decisions for patients' benefit. It will also help bring down medical costs over the long term because we will be able to point to patients who will receive the most value for any treatment. 

2. What are some of the points of discussion that go on in your leadership panel? What are the strategic points you go by to steer the company forward?

 We strive to enhance the patient experience, especially in the context of providing quality personalized treatment. One of the major challenges is to validate those markers that provide value to the patient and the medical providers. We spend a lot of time determining the data's underlying distributional quality and building better models to enhance predictive patient outcomes as we fit that data. We are then able to answer questions regarding where we need to go next and what gaps exist in our knowledge. This will allow us to better collaborate with physicians, colleagues, and partners to address tomorrow's needs.

What would be the single piece of advice you could impart to a fellow or aspiring professional in your field looking to embark on a similar venture or professional journey along with your service and area of expertise? 

I would advise young professionals to improve their quantitative skills, as we rely on large volumes of data to improve patient care. A continued challenge will be to offer a statement with high assurances that we’re making the correct decision, for example, that we are making a decision correctly 99.9% of the time thereby we can assess the relative risks of being incorrect with some probabilistic foundation. Regardless of any technology, it's important to understand the basic structure of the data, its origins, and what it is we're gathering to ensure it is, in fact, relevant and will support answering the question(s) of interest as teams work through what are very complex issues around the problem, data, and ultimately the patient needs.