Data and analytics capabilities have made a leap forward in recent years. The volume of available data has grown exponentially, more sophisticated algorithms have been developed, and computational power and storage have steadily improved. The convergence of these trends intersecting with technology is what is fuelling advances in medical research or healthcare.
The concept of “big data” is not new; however the way it is defined is something that has been a constant confusion over the years. Various attempts at defining the term characterize it as a collection of data elements whose size, speed, type, and/or complexity require one to seek, adopt, and invent new hardware and software mechanisms in order to successfully store, analyze, and visualize the data.
According to Mitali Mukerji, senior principal scientist, CSIR, when a large number of people and institutions digitally record health data either in health apps or in digitised clinics, this information becomes big data about health.
Healthcare is a prime example of how the three Vs of data- velocity (speed of generation of data), variety, and volume, are an innate aspect of the data it produces. This data is spread among multiple healthcare systems, health insurers, researchers, government entities, and so forth
.“Medicine and underlying biology is now becoming a data-driven science where large amounts of structured and unstructured data relating to biological systems and human health are being generated,” says Dr Rohit Gupta of MedGenome, a genomics driven research and diagnostics company based in Bengaluru.
The rapidly expanding field of big data analytics has now started to play a pivotal role in the evolution of healthcare practices and research; it has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Further, big data analytics is also being used to aid the process of care delivery and disease exploration.
Despite the inherent complexities of healthcare data, there is potential and benefit in developing and implementing big data solutions within this realm.
An area that’s witnessing exponential growth is electronic health records. In the US,
electronic health records in office-based physicians in United States have soared from 21 percent in 2004 to 87 percent in 2015. In fact, every hospital with 500 beds (in the US) generates 50 petabytes of health data.
The Meeker study shows that the download of health apps worldwide has increased to nearly 1,200 million in 2016 as opposed to 1,150 million in the previous year.
A report by the McKinsey Global Institute, a renowed global consulting firm suggests that if US healthcare were to use big data creatively and effectively, the sector could create more than $300 billion in value every year. Two-thirds of the value would be in the form of reducing US healthcare expenditure.
After decades of technological laggard, the field of medicine has now finally begun to acclimatize to today’s digital data age. New technologies have made it possible to capture vast amounts of information about each individual patient over a large timescale.
The Ministry of Electronics and Information Technology, Government of India, runs an Aadhar-based Online Registration System, which acts as a platform to help patients book appointments in major government hospitals. The portal has the potential to emerge into a source if big data offering insights on diseases, age groups, shortcomings in hospitals and areas to improve is followed.
An ultimate example of how big data analytics can help modern medicine is the Human Genome Project, which has, in the present day, paved way for personalised medicine, that would have been difficult without the democratisation of data, which is another boon of big data analytics.
In India, a Bangalore-based integrated biotech company- Avestha Gengraine Technologies (Avesthagen), recently launched ‘Avestagenome’, a project to build a complete genetic, genealogical and medical database of the Parsi community.
The biotech, through this project believes that the results from the Parsi genome project could result in disease prediction and accelerate the development of new therapies and diagnostics both within the community as well as outside.
MedGenome has also been working on the same direction. Mukherji says, “We collaborate with leading hospitals and research institutions to collect samples with research consent, generate sequencing data in our labs and analyse it along with clinical data to discover new mutations and disease causing perturbations in genes or functional pathways. The resultant disease models and their predictions will become more accurate as and when more data becomes available.”
“If a researcher has huge sets of data at his disposal, he/she can also find out patterns and simulate it through machine learning tools, which decreases the time required to arrive at a conclusion. Machine learning methods become more robust when they are fed with results analysed from big data,” she adds. “These data simulation models, rely on primary information generated from a study to build predictive models that can help assess how human body would respond to a given perturbation.”
Thus, understanding and predicting diseases require an aggregated approach where structured and unstructured data stemming from a myriad of clinical and nonclinical modalities are utilized for a more comprehensive perspective of the disease states. Researchers are studying the complex nature of healthcare data in terms of both characteristics of the data itself and the taxonomy of analytics that can be meaningfully performed on them.
And it goes without saying that Big data analytics which leverages legions of disparate, structured, and unstructured data sources is going to play a vital role in how healthcare is practiced in the future; one can already see a spectrum of analytics being utilized, aiding in the decision making and performance of healthcare personnel and patients, and thereby facilitating quantum leaps in the field of medical research.
