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Analytic Health Apps

Big Data App MediDash Wins the Philips' HealthSuite Digital Hackathon

— March 12, 2015 — Tech
The winner of the first ever Philips HealthSuite Hackathon was named MediDash, a big data app that helps to close the gap between professional and personal healthcare.

Over 100 people participated in the event, with the intention of developing a prototype for a new Philips HealthSuite Digital Platform. The digital platform is set to be a cloud-based infrastructure that's capable of collecting all sorts of data and making analyses based on its findings. Using this information as a base, one team imagined MediDash to be a platform that would ease the decision making process. For instance, when looking at an X-ray of a chest, a radiologist would be able to pull up other relevant data, revealing respiratory issues or other conditions that may provide insights on a current issue.

MediDash's creators will be recognized at a SXSW Interactive event at the Philips Motivate Lounge, where the big data app will also be demoed.
Trend Themes
1. Big Data Analytics - Opportunity for developing apps that can collect and analyze vast amounts of data to improve decision-making processes in healthcare.
2. Healthcare Technology Integration - Opportunity to create platforms that integrate various digital tools and data sources to bridge the gap between professional and personal healthcare.
3. Cloud-based Infrastructure - Opportunity to leverage cloud-based infrastructures for efficient data collection, storage, and analysis in the healthcare industry.
Industry Implications
1. Healthcare Technology - Disruptive innovation possibilities include developing digital platforms and apps that improve healthcare data integration and decision-making.
2. Big Data Analytics - Disruptive innovation opportunities lie in the development of analytics tools specifically designed for the healthcare industry.
3. Cloud Computing - Cloud computing can disrupt the healthcare industry by providing scalable and secure infrastructure for storing and analyzing large amounts of healthcare data.
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