One of the most notable aspects of the digital age is that the average person is now producing so much data that some of it can be analyzed for healthcare purposes. As many patients, doctors and researchers have come to discover over the past few years, big data analytics and machine learning (ML) technology has become an essential part of studying public health trends and preventing health crises.
What few people understand, however, is how pharma companies are applying ML to patient data. Here is how modern pharma companies are combing through your data in an effort to put new medicine on the shelves.
Pharma companies are joining the data race
It is only natural that pharma companies are joining the ongoing 'data race', or the rabid pursuit of insightful data that can be leveraged for commercial purposes. After all, nearly every sector of the modern economy produces data that successful companies leverage in their pursuit of a profit. Many people are taken off guard by the idea of pharma companies maintaining huge troves of patient data, however, as they justifiably fear such things as data breaches or the exploitation of patients. To some, the idea of pharma companies applying ML to patient data is a chilling prospect rather than something to be warmly embraced.
With healthcare data volumes expanding by a whopping 48% annually, however, the trend of pharma companies tapping into the power of patient data is only likely to grow regardless of its critics. Most of the pharma companies that are interested in patient data are using ML to detect notable patterns in a hue data set. Whereas human experts can only manage so much information at once, these complicated algorithms can conquer an entire data set in seconds to churn out useful insights that may save lives or point researchers in the direction of a new cure.
The increased focus on ML in the field of healthcare analytics will become more notable as more and more pharma companies pour funding into it. Whether they're investing in predictive, descriptive or prescriptive applications, pharma companies will always find a use for ML algorithms in their race to market new drugs to the public.
Pharma companies need more access to data
If pharma companies want to keep churning ahead with ML techniques, they will need greater access to patient data. Greater collaboration is needed across the healthcare industry as researchers, medical companies and healthcare providers alike can all benefit by swapping information with one another in the pursuit of saving lives. Of course, privacy considerations will always make it essential that personal information is not needlessly bandied about, but modern encryption technology will enable companies and healthcare providers to safely swap patient data back and forth.
Merging patient data for commercialization will become a central facet of the pharma industry sooner rather than later. Widespread initiatives to collect data on public health trends can lead to the elimination of certain diseases and the betterment of public hygiene, so the trend of merging data should be welcomed with open arms by those interested in more powerful drugs without any design or manufacturing defects that can lead to recalls later on.
Finally, patient data will be digest by intelligent algorithms and leveraged to produce crucial new insights with the help of AI assistants. Big pharma is already tapping into AI to boost drug discovery efforts, especially as it's proving to be a scalable way to fund general research. Those who doubt the power of ML when it comes to saving lives should consider how these algorithms can be used to classify diseases and search for new cures.
Big data is so important that even big pharma companies are getting in on it. Nobody is entirely sure what is in store for the future of healthcare, but it is clear that pharma companies applying ML to patient data are helping to pioneer new medical treatments.