Compared to other industries, Healthcare is perhaps most affected by the rapid proliferation of data. With HITECH and HIPAA regulations and high-visibility data breaches driving more focus on security, retention, and privacy of personal health information (PHI), the latent demand for a scalable data strategy has become a primary driver of industry decision-making. Rapid innovation, spearheaded by new technology and data-driven leadership, is upending the business of health. This brings Big Data into the spotlight for a movement dedicated to efficiency, accessibility, and, most importantly, outcomes
To any medical researcher, the most important aspect and ironically the most difficult challenge is ‘Data’ For medical researchers, data can help surface subtle relationships between symptom and ailment, between patient and predisposition, between actual predictive indicators, and reactive symptoms, If they could only implement a big data solution..
In fact, medical researchers are always attempting to obtain as much data as possible to reduce the signal-to-noise ratio—since in the case of medicine, even a small degree of noise can lead to a significantly different diagnosis.
How can the researcher obtain as much data as possible, yet have the tools to sift through that data, understand hidden relationships and insights—all within a reasonable amount of time? This has been a constant challenge for a medical researcher. Moreover the struggle with medicine research today is the understanding of the biology of disease. Big data comes into play around aggregating more and more information around multiple scales for what constitutes a disease—from the DNA, proteins, and metabolites to cells, tissues, organs, organisms, and ecosystems. Those are the scales of the biology that is needed to be modeled by integrating big data. If we do that, the models will evolve, the models will build, and they will be more predictive for given individuals.
It’s not going to be a discrete event but rather a continuum and an evolution. The first step would be of building models, aggregating big data, testing and applying the models on individuals, assessing the outcomes, refining the models and so on. Questions will become easier to answer and the big data insights will be more valuable.
The Medical research can benefit greatly from these big data insights. Imagine researchers being able to leverage the voluminous amounts of data generated each day at it’s teaching hospitals. Apply that data to research currently being conducted by the medical school or apply it to courses medical students are enrolled in. Imagine being able to use this information not only for advanced diagnostic purposes, but to aid researchers develop new treatments and/or techniques—and being able to observe not only the efficacy of those new advances, but being able to understand the factors impacting that efficacy.
The role of big data in medicine is the one where better health profiles and better predictive models can be built around individual patients so that diseases can be better diagnosed and treated.