Know How Big Data and Product Development Transforming Healthcare
                     Big Data and Product Development 
Transforming Healthcare
Advanced product design and big data are changing our approach to healthcare. Take the 
human genome project for example. Using a genome sequencer, the whole human genome 
(about 3 billion base pairs) was successfully mapped in 2003. This breakthrough opened the 
door for personalized medicine where an individual’s genetic makeup influences the prescribed 
treatment plan. Since then, medical device innovations and big data have transformed how we 
approach health monitoring and analysis of health.
Remote health monitoring
Medical professionals traditionally evaluate health based on data collected from patients while 
at a medical clinic. Although these tests and evaluations are informative, office visits are 
infrequent with weeks or months between visits. In addition, medical tests can create burdens 
of time and means for individuals as well as clinics. As a result, the development and production
of remote monitoring devices are increasing, allowing for data to be collected at a higher 
frequency and without the need to visit a clinic. For example, Reveal Biosensors has created a 
device worn on the arm which monitors tissue oxygen levels. The device has been used as a 
monitor for sleep apnea rather than requiring a patient to spend the night at a sleep lab. Oasis 
Diagnostics is another example. They are marketing a device that can be used anywhere to 
analyze saliva samples. The device is under development to analyze cortisol, testosterone, and 
hormone biomarkers with future tests under also underway to identify Alzheimer’s disease, 
Parkinson’s disease, and sleep disorders. Devices like these mentioned here have the potential 
to save time as patient monitoring can be done more frequently in the comfort of their own 
home and reduce the workload of medical facilities.
Predictive analytics
Predictive analytics in healthcare is the use of machine learning algorithms to build predictive 
models to identify states of health or recommend patient treatment. For example, one research
group has developed a system to analyze the risk of a miscarriage for expecting mothers. The 
mothers wear sensors to collect data which is analyzed using machine learning algorithms, and 
the resulting information is sent to the patient and doctor by an app to indicate the mother’s 
state of health. This information can be used not only to identify risks, but to treat conditions 
before they become problematic.
Another predictive analytic system has been used and tested in clinics to identify cases of sepsis
or heart failure. The study used algorithms to correctly identify the onset of sepsis shock up to 
28 hours earlier than traditional identification methods allowing for quicker treatment and 
recovery. In addition, the algorithm was able to more accurately identify patients at risk for 
heart failure than traditional methods. As a result, patients were educated on their conditions 
and readmission rates dropped.
The development of medical devices in concert with big data will undoubtedly continue to 
improve and change the way we approach healthcare. So far, these advances are shifting to 
more personalized medicine and changing from a reactionary to a preemptive approach.