Ten emerging healthcare data analytics trends for 2016

Top 10 trends for data driven healthcare analytics in 2016

Top 10 trends for data driven healthcare analytics in 2016

2015 was an eventful year for data science in healthcare. Round the year, investors kept showing faith in innovative data driven healthcare ideas. Market and innovators continued exploring how mobile technology could be leveraged to improve user healthcare. Global commitment to better healthcare, from both the governments as well as industry giants, strengthened. I foresee 2016 to continue carrying the momentum of this year. Several ideas and solutions that received initial support from the medical community will go mainstream next year as new paradigms will keep emerging. In today’s post, I have highlighted key data driven healthcare analytics trends we are likely to witness in 2016.

Wearables market will continue to search for its flight path
Wearables devices haven’t hit off well in the market so far. But its utility for care-giving and patient monitoring is undeniable. And despite low current adoption levels, this market is bound to surge. Trends suggest that compared to positioning such devices as a lifestyle health monitor device, this product market has more utility in critical care patient needs. In summary, this market is likely to be driven not by the millennials but by the patients. In 2016, care-giving for patients will be the prime theme of numerous wearables devices. Innovative biosensors with artificial intelligence capabilities will be ideas worth investing in. Success of these devices will also be dependent on the extent of useful integrated user interfaces and innovations in closed loop action systems providers are able to come up with.

“Network Graph” for healthcare data visualization will become a buzzword
With the opportunity to work with massive data sets, the need for large network graph visualization tools for healthcare research is increasing. Cytoscape, an open-source data graph project backed by a consortium of academic institutions, has witnessed good adoption rates among the medical science research community.  Lumiata is another venture that has been employing graph visualization for real-time patient and diagnostic analysis. In 2016, I foresee more players enter this market. Also, the adoption rate and demand for such tools from the medical research community will go up, making “network graph” a buzzword.

Non physician caregiver’s demand will continue to rise, contributed partly by telemedicine
Federal data has already suggested how the number of non-physician caregivers is rising in the US, mostly due to shortage of doctors. With Obamacare significantly increasing the insurance coverage segment, this need will increase further. But another demand driver will be telemedicine services. Serving as productivity enhancement tools, these services can improve patient adherence and engagement, improve communication with the caregiver and help manage the activity of the non-physician caregivers. With the aid of data analytics, new technology solutions that will limit the intervention of physicians to need-based actions will become more prominent. Such data driven telehealth services and self-medication ideas will also demand need for auxiliary skills such as administrating remote monitoring and conducting data analysis, bringing the data professional into the ambit of caregiver. An example of such business models is Naperville based PhysIQ, which recently received an FDA 510(k) clearance for its personalized physiology analytics system. It has already tied up with Samsung to take its product to the masses. Its platform can work with wearable health device or a physiological sensor.

Data and wearables driven mobile apps will continue to challenge conventional practices
We’ve already seen how startups such as Glow are challenging conventional market for relatively expensive in-vitro fertilization treatments with its mobile app. Recently, I also talked about other upcoming mobile startups that plan to employ lifestyle changes as an effective tool to manage diabetes. With access to patient health stats on a continuous basis and having the unique power to communicate with them at will, mobile apps have greater ability to drive adherence. In 2016, more such ideas on lifestyle solutions for treatment management will see light of the day.

Virtual personal assistants for healthcare will attract everyone’s attention
Patient adherence is a major issue inhibiting optimal care. We are witnessing several startup ideas, ranging from dosage wise medication shipment (PillPack) and apps to monitor patient adherence (AiCure) in this segment. However, with many seeing virtual assistant as the future of human-machine interaction, there isn’t a reason why data-driven analytics cannot be leveraged for creating virtual assistant for patients. Not only will virtual assistant help with patient adherence, they will also collect important patient data of significance to the caregiver. I foresee 2016 to be a year when we’ll see significant startup activity in healthcare virtual assistants domain.

User experience for dashboard solutions for medical community will emerge as a key product differentiation
Conventionally, user experience has been focused towards consumer interfaces only. But in the fast evolving market, amidst cut-throat competition, new entrants focus on improvements in user experience as a vital differentiation, and rightly so. 2016 will see startups and service providers placing a renewed focus on user experience of data tools for the medical community. Dashboard solutions with readily available APIs to connect with local systems, with real-time analysis and ability to operate with different data formats will be preferred.

Data analytics players will renew their focus on healthcare institutional solutions
Startups such as Flatiron Health and Oncora Medical are building data platforms for real-time data analysis from patient records to create real-time R&D dashboards. Flatiron focuses only on oncology and has already signed by 1500 clinicians. Such models are gaining in traction. Next year, I foresee such startups to gain in customers and entry of generic data analytics providers into this lucrative domain. Focus will also be on creating integrated cloud-based solutions for diagnostics, patient care, discovery, etc.

Medical imaging analytics will be seen as a potential game changer 
Butterfly Network, which will launch its commercial product in 2016, has developed an image scanner, about the size of an iPhone, and capable of replacing medical equipment for ultrasounds and MRIs with cheaper and faster alternatives. The upcoming product claims to employ deep learning techniques for higher precision analysis of imaging data. It would be interesting to see how the medical community responds to their product. I foresee a winner here. I also believe advances in machine learning methods for image analytics will witness significant commercialization potential in the coming year.

Genomics research ventures will drive machine learning driven drug discovery initiatives
Machine learning for drug discovery isn’t a new trend. Berg Pharmaceuticals, Numedii, Insilico Medicine, Calico, Cypher Genomics, DNAnexus, etc. have already made significant headway. Also, many models have evolved ranging from patient data to genomic informatics. 2016 will see higher participation from genomics startups into the realms of drug discovery.

Data driven R&D analytics startups to drive EHR
EHR adoption has always been a tough nut to crack with a myriad of issues, ranging from inaccurate data documentation to lack of standardization of format among data records. Interestingly, a range of startups that use data driven machine learning for diagnostic and care predictions are engaging with healthcare institutions to digitize health records and create useful data driven analytics solutions. Innovations such as voice-based prescription for physicians and algorithm to detect non-compatible drugs in prescriptions are some of the remarkably useful ideas that are adding thrust to the process. In 2016, I foresee more and more healthcare institutions joining hands with startups.

Anubhav is a data scientist who works and writes on new big data decision sciences models and their application in key business areas across industries. Anubhav also tracks the industry developments. He can be reached at anubhav@thinkbigdata.in

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