The marriage of Big Data to Analytics is showing its impact in various domains. It is particularly important in Healthcare , Life Sciences , Insurance and Pharmaceutical domains. The revolution of Information Technology and arrival of Big Data – Analytics to relatively conservative vertical of Healthcare has brought about a paradigm shift in the way we would be delivering healthcare in coming 5-10 years and beyond.
Applying the principles of data science, Pharmaco-Genetic-Epidemiology, Big data analytics, Genetics and Statistics to Medicine has brought about a revolution. Making data driven decisions in Clinical settings using Systematic Research based evidence is the norm now, that has delivered effective personalized “tailor-made” healthcare as opposed to old “one size fits all” kind of therapy ! Integrating the Basic Sciences , translational , clinical and population health data has made this possible.
Healthcare is one good example of how data and information is being converted into knowledge to gain wisdom !
The use of big data analytics tools , computational methods and statistics does help in recognizing the patterns in this data for Medical Research as well as designing Clinical therapy. The Human Genetic data and the availability of Electronic Health Records (EHR) for large population helps in formulating personalized therapies for better Clinical outcomes.
The use is two fold :
1) For effective clinical research / medical research / clinical trials
2) For providing optimal,cost effective, personalized therapies and Healthcare
The success of translational medicine bringing therapies from bench to bedside is being achieved at a very fast pace. Creating Wisdom and Knowledge from data and Information is being effectively achieved by the UCSF based on the framework proposed by the Institute of Medicine.
Clinical-healthcare data in digital form generated on daily basis , correlating with the available outcomes data from databases and registries , associating with the genetic makeup , makes it easier to decode the patterns in diseases , outcomes of treatments , predilections of pathologies to certain cohorts , effects with con-concomitant medications or pathologies etc. This is particularly helpful in the disease management and effective healthcare provision related to various cancers and chronic diseases like Diabetes Mellitus , Asthama, Hypertension , immunological diseases , allergies etc.
Henceforth , genetic profile would become a vital requirement in :
1) A Clinical Trial Protocol :Inclusion and exclusion criteria of a Clinical Trial
2) An essential stratified variable for Adaptive Clinical Trials and during Risk Based Monitoring
3) Phase IV or Post-Marketing reporting of Adverse Events or SAEs ( Pharmacovigilance)
4) Labeling of an IND or a new drug / device
This is a win-win for the patient (subject) advocates and the sponsors in delivering effective pharmacovigilance ,de-risking and saving costs over a long run while providing effective disease management and healthcare !
Clinical Scientist 