With the flood of data pouring in from across the healthcare industry there are still a lot of questions surrounding how best to make sense of said data, and where to store the heaps of new information. 

Justin Helmig, chief marketing officer at data management and precision medicine company LifeOmic, told MobiHealthNews that both machine learning and cloud computing could be keys for this problem. Helmig will be one of six panelists presenting One Size Does Not Fit All: Advances in Precision Health and Medicine at Health 2.0 on Tuesday, Sept. 18. 

“I think it is a foundational [question]: how do these organizations handle the data? I think technology has had a bit of a lagger in the adoption of public cloud technology. Public cloud … really allows you unlimited storage that you can compute. That is something that has been missing historically,” Helmig said.  “So one of our customers — I was talking to the CIO and they have more of a traditional data enterprise warehouse, and the dean of the medical school was bantering back and forth with the CIO and was saying, ‘Hey, this can only handle like a couple thousand patients worth of genetic data and that’s not enough.’ So foundationally,  how do you store all this data in a place where it is performing and easy to analyze and easy to access? We believe there is no way to do that cost effectively with traditional on-premise software, so we feel the cloud is the first ingredient. The cloud can be stored and accessed and analyzed.”

But in addition to just storing the data there is also the issue of making sense out of the enormous amounts of information coming in. Helmig said that is where precision medicine and machine learning can come in handy. 

“You have this huge swath of data that is all very disparate,” he said. “So how can you build a platform and applications on a platform that can really help researchers and clinicians hone in on the right data. So we have invested a bit on our propriety machine learning technology.”

Machine learning has been a trending topic in the healthcare space and has been used on everything from research and development to diagnostic support. Although many in the industry have been skeptical of the hype around machine learning, many are looking at ways it can be used alongside physicians' care. 

Helmig gave a use case example of Alzheimer's and dementia testing. His company is working with a provider on a machine learning platform that will be able to look at datasets to identify a cost-effective model with a low false negative rate to screen people for Alzheimer's. He said that this is one area where it can be tricky figuring out who to screen. The provider has the option of screening everyone over 50 or 60, but on the other than that would be cost prohibitive. Even if machine learning isn’t entirely accurate on screening for Alzheimer's, it could narrow the pool of patients that need further screening. 

Helmig said that his company also works in the genomics space, which has become an increasingly popular area of focus for precision health and medicine. 

“For us we are trying to promote the vision of precision medicine and towards precision health,” Helmig said. “What we see is the ability to take all of this data and take individualized treatment.”

LifeOmic will also be making an announcement on Thursday about the launch of its LIFE apps blogging platform, which aims to educate patients using the Life apps, a precision health app, about ways to live a healthy life.