Unique healthcare big data startups you should definitely know about

Unique healthcare big data startups you should definitely know about

Some unique healthcare big data startups can change the world

 

That big data has flooded the healthcare market is no news. What has been indeed remarkable is that apart from the startups that are working in the expected big data streams that aim to serve insurance companies, healthcare providers and pharmaceuticals with EMR, trial data capture and information management solutions, some unique ventures are using the power of big data, marrying it with communication technology, to address the R&D challenges related to diagnosis and analysis. Such startups do not make a lot of noise everyday with high valuations or increasing footprint, but nothing can deny that these are likely to have one of the most long lasting impact on our lives. I’ve identified and shared a list of four such startups below.

 

Glow

Glow is a mobile app that helps women manage their reproductive health. Headquartered in Shanghai, it is one of the two firms born out of the HVF Incubator of Max Levchin, former co-founder of PayPal. The app was initially designed to help women conceive by providing guidance based on their sexual activity, menstruation cycle, level of cervical mucus, body mass index and other factors. With time, the app functionalities have broadened to include pooled-risk funds for fertility treatments, conception prevention guidance and an educational feed for women undergoing pregnancy. A few months ago, the company had claimed that its app had already helped 25,000 women get pregnant. At the core of the product lies a big data and machine learning driven algorithm that helps women chart their menstrual cycles for good. Whether it is identifying the most fertile period for conception or identifying diseases such as polycystic ovary syndrome (highly correlated with irregular periods), women can access the analytics of their own data and crowdsourced data from others to make informed decisions. Reports indicate that the startup, which raised another $17m in its latest round of funding in late 2014, is also looking at health insurers as a potential customer segment. The firm had previously raised $6m of external funding. What would be interesting to see is that will we see such apps cutting down the market for relatively expensive in-vitro fertilization treatments.

 

Flatiron Health

Any sincere effort to help fight the global menace of cancer is always embraced. Flatiron Health, a New York based startup, is one such noble effort. Simply put, Flatiron uses big data analytics to accelerate cancer research. Deploying a data platform which it calls the “OncologyCloud”, the startup analyses real-time data from patient records to create a live dashboard of patient conditions in the oncology office. This could bring about a game changing acceleration in cancer research. Why? Because only 4% of cancer patients participate in data-generating clinical trials in the US right now. The startup has also shown appetite for integrating EMR records into its analytics model, reflected in its recent acquisition of Altos Solutions. Last known, more than 1500 clinicians researching at more than 200 cancer research centres have signed up for Flatiron services. In the future, the company aims to expand its base and build a repository of millions of patient data for global cancer research. The startup recently raised $130m of funding, led by Google Ventures, totalling its fundraising to $158m.

Another startup working on similar business model is CancerLinQ. CancerLinQ is an initiative of the American Society of Clinical Oncology (ASCO) and employs the SAP HANA platform. Its commercial product is expected for a launch this year. Another startup operating in the same domain is Palo Alto based Syapse, although it has not limited its activity to the oncology segment only. Syapse has developed what is calls the Syapse Precision Medicine Data Platform, which sources information from EMR, labs, etc., and performs analytics on it for medical professionals. Calling itself a genomic data analytics player, Syapse picked up $10m of funding last year.

 

BridgeCrest Medical

BridgeCrest Medical, a San Diego based startup, offers population health related big data services to infrastructure and mining companies operating in remote locations. Its data analytics solution aims to improve safety and health of workers operating in hazardous conditions. In other words, its cloud based analytics platform scans through tonnes of worker data and detects abnormal population health trends. It further identifies individuals with abnormal measurements, alerting hospitals and related medical workforce with the relevant datasets. In one of the landmark instances of steadfastness that brought the company to limelight, it partnered with medical diagnostics manufacturer JAJ International in December 2014 to bring first mobile testing kit for Ebola to market. BridgeCrest’s cloud based platform sourced patient health data from JAJ’s mobile health devices and wearables. The kit built tested patients for presence of various Ebola antibodies and antigens and registered a high rate of 82% accuracy. With the threats of such potential global epidemics looming large all the time, there is an undeniable and urgent need of such big data solutions, esp. in the developing and underdeveloped parts of the globe. In September 2014, BridgeCrest raised $30k in seed funding.

 

Butterfly Network

Butterfly Network, a Connecticut based startup, is aiming the redefine the medical imaging industry. The company was founded by bio-scientist and serial entrepreneur Jonathan Rothberg and a group of physicists and engineers from MIT’s Lincoln Laboratories. Its prototype product is an image scanner, about the size of an iPhone, and capable of replacing medical equipment for ultrasounds and MRIs with cheaper and faster alternatives. In number terms, compared to a conventional imaging set-up costing millions of dollars, Butterfly’s product will cost a few hundred dollars, operate at a 30-60 times faster rate than conventional equipment and will employ big data deep learning methods, raising its precision bar as well. Butterfly Network expects a commercial launch of its product by mid of 2016. In November last year, Butterfly Network raised $100m in funding.

 

Anubhav, a data scientist, writes about new developments and future trends in the machine learning and data analytics domain.
He can be reached at anubhav@thinkbigdata.in
Follow him on Twitter at: https://twitter.com/think_bigdata

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