Big data in healthcare is about more than just improving hospital workflows and creating electronic medical records (EMR). It has the power to reshape all aspects of medicine, from basic research to diagnostics. Promising new treatments are being developed by analyzing patient data and tracking specific patient populations over time. Combined with the potential for EMR data to aide in personalized diagnoses, treatments, and care, big data is creating a new era of breakthroughs that benefit more conditions and segments of patients than ever before.
Big Data, Smaller Targets
Lung cancer is a devastating disease, and while its contributing risk factors are often environmental and behavioral, a certain population of lung cancer patients don't carry any of these typical risk factors. Instead, what they share is a mutation in their ALK gene.
Pfizer, using predictive analytics from data generated by genomics, clinical trials, and anonymous EMR data was able to develop a drug that worked for lung cancer patients with ALK gene mutations. Without the analytics, the effectiveness of the drug for ALK mutation patients might have been obscured by its ineffectiveness for the general population of lung cancer patients. The drug's effect on ALK mutation patients might have been lost, its overall effectiveness for treating lung cancer dismissed, and its approval by the FDA denied.
The implication for treatments is huge. Big data can aide basic research by finding patterns among trials, identifying non-obvious connections between patients, illnesses, and treatments, and by targeting specific populations and diseases for significance out of apparent noise. In fields like medicine where statistics play a critical role in guiding action, big data analytics can add new dimensionality and quantification to ambiguous situations.
Making It Personal
Targeted medical research through analytics is part of a larger ongoing trend in medicine - personalized healthcare. Personalized healthcare incorporates genetics and genomics into treating individuals for illnesses and maintaining overall healthiness. Because of the sheer number of variables involved in medicine, including individual differences that affect not only how a disease progresses but how a person responds to specific treatments, the more doctors can tailor treatment for individuals, the better the results.
Prior to the wide availability of big data analytics, personalized medicine was far more difficult, since analyzing genetics and genomics across populations involved unwieldy amounts of data. Now, analytics can reveal unknown disease factors by incorporating clinical data with genetic profiles and tissue morphology. Correlations between multiple sources can weigh in on the effectiveness of drugs and foster individual treatments and health plans for patients. Diagnostic medicine also benefits from not only having more information available during a diagnosis, but also having a way to ascribe relevance and importance to each piece of information given a specific situation. More hard facts means reduced ambiguity, fewer chances for misinterpretation, and fewer repeated tests to undergo for patients.
With intelligent MRI machines and cloud-based health platforms alone, big data has shown transformative potential for the healthcare industry. Now, with basic research and personalized treatment benefitting from mining patient data, all of medicine stands to benefit in ways that were previously impossible. If widely embraced, big data stands to become one of the greatest breakthroughs in medicine since penicillin.