Top Five Reasons Big Data Is Advancing Personalized Medicine

Big Data as it pertains to health care has emerged at the center of the revolution in personalized medicine. Simply put, the proliferation of data offers great possibilities for more precise diagnosis, as researchers are able to drill down to see what’s happening and create more targeted therapies, specifically at the molecular and tissue levels.

In this slideshow, Thomas Heydler, CEO of Definiens, a leading provider of image analysis and data mining solutions for quantitative digital pathology in the life sciences, diagnostic biomarkers and health care industries, explores the top five reasons Big Data is advancing personalized medicine as we know it.

Click through for five reasons Big Data is advancing personalized medicine as we know it, as identified by Thomas Heydler, CEO of Definiens.

Exposing the unknown

Technologies that can now extract large amounts of data out of samples or biopsies are allowing for previously unknown factors involved in disease to be discovered and utilized as drug targets or disease biomarkers. Data is also able to expose the complexity of a disease, especially cancer, and that there will never be one drug or treatment option that works for every patient.  

Correlating multiple sources for diagnosis and therapy decisions

Big Data analysis of clinical outcomes, genetic profiles and tissue morphology will be a big driver of personalized medicine. As we are able to align and compare multiple data points from various sources, tailoring individualized treatment plans for each patient will be possible.

Decisions based on hard facts, and less on subjective interpretation

Historically, much diagnosis has been based on subjective visual analysis of a biopsy sample viewed through a microscope. Depending on the clinician’s experience or background, the diagnosis could be different from clinician to clinician. Hence, the oft requested “second opinion.” The datafication of patient samples, where discrete data points are extracted from qualitative samples, yields a vast quantity of knowledge that can be statistically analyzed and quickly reviewed by multiple clinicians for solid diagnosis and therapy recommendations.

Systematic analysis of tissue and genomic information

Through the datafication of patient tissue samples and genomic fingerprints, clinicians can systematically extract more information from each patient without requiring multiple rounds of testing. By having all available information at the same time while determining diagnosis and the patient prognosis, the best treatment decisions can be made on an individual basis at a faster rate.

Reproducibility within and between clinicians

Reproducing testing results in the clinic is very important. Each clinician should be able to produce the same diagnosis from day to day and diagnoses should be the same between clinicians. By using Big Data generated from clinical samples and testing, consistently reproducible test results are possible between clinicians and doctors for more accurate diagnosis and appropriate spending on therapy options.