Successful Big Data applications in Healthcare
Data in Healthcare spans areas from disease prevention to diagnoses to efficient operations and even fraud prevention. Big Data is also perceived as the key to revealing the long-sought cures to complex diseases like cancer. Recent explorations into medical Big Data are already producing unexpected positive results.
For years, most medical research and discovery have been based on the collection and analysis of data: who gets sick, how they get sick, and why. Scientists hope that by consolidating all the medical records ever accumulated on the planet, the process of finding medical cures will become faster. Yet, one of the challenges is to find a middle ground among private and public research institutions keen on filing for patents and thereby slowing down the process of new discoveries.
Consolidating all medical data is easier said than done. Data containing clinical records amount to about 170 exabytes for 2019 alone, with a yearly increase of about 1.2 to 2.4 exabytes. Getting around all that vast sea of disjointed data is no mean feat, but the rewards are more than worth the trouble. So let’s take a look at how data management has been successfully applied in the Healthcare sector.
You can track the perceived quality of your lifestyle with smartphone apps, and similarly, also chronic ailments like Parkinson's disease or diabetes can be logged and tracked personally by an app.
Researchers are beginning to compile this information into incredibly useful databases that could be game-changers in understanding the intersection of lifestyle and disease with vast population sample sizes.
Smart devices can record and transmit the actual data — steps walked, heart rate — anonymously to a central data store, giving the researchers more detailed near real-time information than ever before.
Similarly you can share your activity data with your own physician, and based on this data, valid diagnosting can be provided, with preventive and corrective treatments and right follow up on care.
Vast libraries of DNA records, patient records, research studies, and other related fields, often with fully anonymized patient data, can be used as the material for AI. This may open the door for formerly unseen associations and connections, as well as even come out with new medications altogether.
Information gathered from Big Data gives medicare providers more insights than they would have otherwise at their disposal. Accurate preventive care can reduce the number of required expensive treatments or hospital visits, providing direct savings in the healthcare costs.
Automated processing of mountains of electronic health records can be used to generate real-time alerts of patient status. With such continuous and real-time tracking of patients' progress, healthcare professionals can respond faster to out of the ordinary situations and keep treatment processes on track.
Reducing costs in hospitals
According to McKinsey, Big Data analytics may enable more than $300 billion in savings per year in U.S. Healthcare alone. The two major areas are clinical operations and R&D, with $165 million and $108 million potential for savings.
In clinical operations this covers comparative effectiveness research, determining more clinically appropriate and cost-effective ways to diagnose and treat patients.
In research and development, we can apply predictive modeling to produce a leaner, faster, more targeted R&D pipeline for drugs and devices. You can use statistical tools and algorithms to improve clinical trial design and patient recruitment to match treatments to individual patients, thus reducing trial failures and speeding new treatments to market. Similarly, analyzing clinical trials and patient records can identify follow-on indications and discover adverse effects before products reach the market.