Healthcare is drowning in a deluge of data. Decision-makers must somehow make sense of a heterogeneous array of information — demographic, clinical, patient-generated, treatment and outcomes data. The latest waves of information also include data from mHealth and genomic sources. It’s not hard to imagine that many in the healthcare industry suffer from information overload and struggle with a bit of ‘analysis paralysis.’ How can organizations make sense of all this big data and actually harness it to improve healthcare and outcomes?
One company helping answer this question is GenoSpace, an ambitious genomic and health data management startup based in Cambridge, Mass. Its current chairman, John Quackenbush, and CEO, Mick Correll worked together in the Center for Cancer Computational Biology at Dana Farber before co-founding the company in 2012. Contracts with notable customers like the Multiple Myeloma Research Foundation (MMRF) and PathGroup funded GenoSpace before the first round of outside funding in 2014.
It was around that time that GenoSpace hired Daniel Meyer, an entrepreneur with a background in venture capital, as Chief Operating Officer. According to him, it was GenoSpace’s ability to attract high-quality customers early on (a rarity for most early-stage companies in life sciences) that convinced him to join. Recently, we sat down with Meyer to learn more about how GenoSpace helps healthcare organizations make sense of all the big data.
Tell us about what you do at GenoSpace.
When you’re dealing with genomics and other biomedical data, there are a variety of different users and reasons for their use. So you could have an institution that has users engaged in research, clinical development, lab medicine and clinical care. They have different software application needs that cut across the same or similar data sets. One of the things we try to do is develop the tools, the interfaces and the experience that will enable all of those different people to get the most from the data.
Could you go over your major offerings?
We have three primary categories of offerings: analysis and interpretation of a single assay result together with phenotypic and other clinical data, interactive analysis of data from many individuals as a group, such as from a large observational study (where we really excel is when a customer has integrated demographic, clinical, genomic, treatment, outcomes and other data) and enabling patients to directly report and interact with their data. We’ve created software applications and web-based sites for patients to upload their data, track their results and better understand their condition. Although we have a core competency in genomic data, we do not only deal with genomic data. Research and clinical care rarely rely solely on a single data type.
Now that Obama has announced the Precision Medicine Initiative expanding genomic study, do you also expect your work to expand?
We think it’s a fascinating announcement and those are the types of initiatives we support. One of the interesting things is that we have customers right now solving many of the problems that the initiative will face. For example, we have been working with Inova, a healthcare system based in Northern Virginia that serves more than two million people per year in the metro DC area. They have been collecting a rich set of whole-genome sequencing data together with structured clinical data on thousands of people. Their data management and analysis needs map directly to those of precision medicine initiatives like the one announced by the White House.
I’d imagine that you’d have greater demand on the private side.
We have spent most of our time there. Our first clinical lab customer, PathGroup, is delivering industry-leading molecular profiling across a wide geographic footprint, including to some big cities in their coverage area and also smaller cities and towns. Our ability to help them bring academic-quality medicine to community oncology is a huge impact. Roughly 85% of oncology patients are treated in a community setting. If you’re only deploying in major cities with academic medical centers, you’re missing out.
What are your next plans? Any new projects or goals?
We are looking to expand to different customer use cases. That can be in terms of the therapeutic indication, such as rare diseases, neurologic or cardiac disease. But it can also be integrating different kinds of data. We have a lot of experience working with demographic, diagnostic, treatment and outcomes data together with genomic results, and there are more opportunities to expand.
Are you also working on using machine learning to do predictive analytics?
We think about that a little differently. There’s supervised analysis, the user asking questions and getting answers about the data, and there’s unsupervised analysis. For many of our customers, they’re not looking for a black box. Our goal is not to replace molecular pathologists, but to work hand in hand with them to make sure their work is better, more operationally efficient and more sustainable, particularly if it’s a commercial entity.
That last piece is underappreciated by a lot of folks. We do a lot of work in genomics and in precision medicine and there’s a lot of science and advanced technology. All that work is lost in most settings if you don’t deliver it properly. You have to understand the science and the innovation, but also how to get it in the hands of people who can impact patients. That’s a big part of what we do.
Any final thoughts?
One of the fun things about being here is we have folks with a lot of different capabilities—in software engineering, interactive design, data science, etc. For a lot of the interesting problems that people are trying to solve in medicine, it takes that interdisciplinary team approach as opposed to a whole bunch of people with the same type of experience.
To learn more about GenoSpace, visit their website at genospace.com or follow them on Twitter at @GENOSPACE.
This article was originally published on MedTech Boston.