Big Data, Big Difference: Building Smarter Devices with Data Analytics

You’ve probably seen the term “big data“. This refers to gigantic amounts of digital information and the analytics tools used to extract it. Data analytics plays a vital role in virtually every field from criminology and government to finance and environmental research. Big data is used to identify patterns, make predictions, and otherwise dissect and examine useful info.

Data analytics plays an essential role in healthcare. Data analytics with medical devices can change the logistics of healthcare delivery.

In the not so distant future, smart medical devices will be able to deliver more effective, comprehensive, personalized, and automated solutions across the board to both patients and health care providers. This may sound like a line from a science fiction novel, but the reality is that medical device engineering and advanced analytics are already beginning to merge.

Look at DAQifi, NeuroLife, EluciData, and Battelle as examples of brands merging big data and medical engineering. EluciData, for example, is an analytical engine using the same pattern recognition and machine learning methods used in national security to collect and analyze medical data from records, body sensors, billing, and diagnostics.

Here are just six of the broad innovations on the horizon with data analytics on medicine’s side:

Automated drug delivery, daily health guidance, and chronic disease management could all be possible with mHealth apps and smart medical devices capable of capturing and analyzing live data. These devices would allow physicians and patients alike to better understand the factors impacting health day-to-day and better apply thusly needed treatments.

Drug dosages are perfect examples. Dosage for most medications vary based on environmental factors, activity, diet, major weight changes, and secondary health issues. It’s impossible for doctors to account for all these factors on a daily basis. Many primary care physicians have trouble broadly factoring mere pieces of such info in during a quarterly or annual appointment for medication renewals.

Automated smart devices can provide real time analysis after bringing all the relevant data together from an array of sources – weather reports, sensors and implants, lab and diagnostic reports, diet tracking app, medical records, and such.

Image diagnosticians have to be highly skilled and have copious amounts of data for interpretation. Analyzing data is still a slow process that’s subject to human interpretation errors.

Data analytics and machine learning could help the devices themselves recognize abnormal scans, thereby increasing accuracy, reducing human error, reducing delay, and allowing for medical personnel to instantaneously identify any abnormalities or changes from previous testing.