How Big Data Analytics is set to Power Precision Medicine
Visualize a scenario where you are (god forbid) gravely ill, and yet you (miraculously) receive an accurate diagnosis with a recommended treatment plan within 10 minutes of reaching the hospital. Sound like the future?
This has, in actuality, been happening for a few years now withthe help of Artificial Intelligence (AI). The University of Tokyo, in 2016 reported that Watson, IBM’s cognitive supercomputer, correctly diagnosed a rare form of leukemia in a 60-year-old woman which doctors originally thought was acute myeloid leukemia. After examining 20 million cancer research papers in 10 minutes, Watson was able to correctly determine the disease and recommend a personalized treatment plan. AI and its related applications are changing healthcare as we know it. The advancements made in AI will revolutionize research and, ultimately, personalized medicine.
In the past, medication involved healthcare providers giving generalized medicine, collecting empirical evidence to know the effectiveness of the cure, and if it worked, the approach continued. Data and machine learning are set to revolutionize the way patients are treated by bringing a personalized approach to medication. With these techniques, the ‘trial-and-error’ or experience-based approach is being replaced by evidence-based medicine, which can be personalized for each patient. This is known as precision medicine. As a part of the Master’s in Business Analytics course at the University of California, Davis coursework, my practicum project for CPMC Research Institute’s Cancer Avatar project involves testing drug efficiency for precision medicine in cancer treatment. The concept of precision medicine is an evolving approach that considers individual variability in lifestyle and genes for the treatment and prevention of diseases. It provides an ability to predict more accurately which medicines will work best for different groups of people.
What is Big Data In Healthcare?
Healthcare providers have been looking to harness the power of big data to come up with distinctive treatments. Big data in healthcare refers to the vast health data amassed from numerous sources including electronic health records (EHRs), medical imaging, genomic sequencing, payor records, pharmaceutical research, wearables, and medical devices, to name a few. Three characteristics distinguish it from traditional electronic medical and human health data used for decision-making: It is available in extraordinarily high volume, it moves at high velocity and spans the health industry’s massive digital universe; and, because it derives from many sources, it is highly variable in structure and nature.
Existing and potential applications of Data Science in Healthcare
Despite data challenges due to diversity in format, type, and context, several new technological improvements are allowing healthcare big data to be converted to useful, actionable information.
In 2015, then-President Barack Obama announced the United States’ government-funded precision medicine initiative that is more commonly known as “All of US.” Under this project, over 1 million enrolled individuals will share their personal data that has been acquired from electronic medical records, DNA sequencing, personal reported information, and other digital health technologies. The in-depth analysis of these data hopes to increase the understanding of the origin and ultimate pathogenesis of a wide range of diseases. The United States Precision Medicine Initiative is aimed towards enhancing current diagnostic and treatment technologies to create more sophisticated and precise screening, detection, and clinical management methods.
There are two key trends that encourage the healthcare industry to embrace big data: First, the move from a pay-for-service model, which financially rewards caregivers for performing procedures, to a value-based care model, which rewards them based on the health of their patient populations.
