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In the pharmaceutical industry, lifespan of research and development determines the fate of the drug. The drug patent life is only extendable to 20 years and this gives the enterprises a short amount of time to earn the ROI. Confronted with a matrix of data (varying in volume, complexity and variety) which serves as the raw material for research, pharmaceutical companies today are in need of technologies which can manage the data and turn it into information for analysis and insight.
Data is received through a multitude of systems and has been deemed “Big Data”. How an enterprise handles and processes this Big Data will decide the development and marketing of the drug. Decline in growth and development rate of the new drugs has brought about an urgency for Big Data technology that would be able to resolve organizational challenges like extensive R&D expenditures, live global data collection, integration of patient and research insights and others.
Nature of Data in Pharmaceutical Industry
Pharmaceutical firms receive data which include research records, patient information, supply chain reports and utilization information. About 3/4th of data project involves handling of the data: compounding, filtering and transitioning the data into insightful information which is when analysis of the data can even begin. Every movement in life cycle of the product has to be charged with data analysis that would show the potential for innovation. Clinical trials are latent with invention and can fertilize into successes only when the participants’ data and results have been recorded valuably. The pre-existent therapies can be judged for their efficiency using data from reimbursement structures.
With the rise of Big Data technologies, patients also look forward to having access to the operations and results of trials holistically, in order to judge which treatment would be ideal. In order to decide and determine which treatment to choose, inferences from typical clinical trials is inadequate and needs to be fuelled by real-time evidence.
The spread of communicable diseases at great speed across the world and rise of chronic illnesses hands over the pharmaceutical companies the need for a pharma-tech revolution that would equip them with a plethora of reliable and relevant data to innovate upon. This data includes: economic information, weather and climate, political track record, local and global commercial trends. These would impact the conclusions on supply and demand of the product.
The Big Data technology that a pharmaceutical company picks up will need to encompass the volume, variety, velocity and veracity of the data received. The amount of data which will be received runs into petabytes and gigabytes which requires the technology to deal with continuous flow of information. The conventional methods of relational database management system (RDBMS) do not have the capacity to deal with the humongous amounts of data. Big Data pharma-tech would have to produce models through which real-time data can be entered and decisions be made. The system through which medical data is received are diverse: CTMS, LIMS, RDC, CDW, CDR among others. Data have to be received through different centers like clinics, hospitals and labs. Also, the formats on which such data is collected are varied: X-Rays, images, Scans, text and audio files.
Pharma-evolution into Revolutionary Big Data Technology
The prime motive for development of a revolutionary pharma-tech would be to nurture a model where research and patient care needs would intersect and give rise to an inter-disciplinary system of data analysis/insight. The shift into Big Data technology is also a response to the changing attitude about relation of patient to healthcare institutions and vice versa. Previously, the information was divided according to its source: external or internal and then, authorized through independent analyses. There was also asymmetry between the different players in the system: between biopharma, patients, payers and providers. Now, the Research and Development of drugs and clinical trials require involvement of other disciplines as well and patients also demand transparency in access to their data. Coupled with the nature of data received, such an attitude shift gives rise to the need for Big Data Technology.
In pharmaceutical research and development, Big Data would provide an improvised model of products, treatments and trials which could result in personalized development of drugs using real-time patient information. Apart from the recognition of undiagnosed patients, one can also get sight of the adverse events which might occur, and thus also an estimate on re-admissions. Researchers can have access to chronological record of patient treatment allowing for a holistic view. We can also forecast trends of diseases and also the potential locations of their rise. With data about a patient on hand, better communication is stimulated and institutions can get feedback from patients. The overarching impact would be decreased cost and amplified quality of drugs.
Big Data technology is also freckled with the use of tools like Databases, Enhanced Cloud Computing, Storage and MapReduce to quicken the development of data into insights.
The Holistic Big Data Matrix
Big Data technology would, through entrance into pharmaceutical industry, cater to the needs of three primary players: the information galaxies, researchers and patients. The information galaxies include intra-national and international centers for data collection and research with distinct data-capturing technologies. Needs of the patient would be served through fitter platforms that combine feedback with experience records and provide holistic solutions. The IT departments would also have access to expansive APIs where the multi-layered infrastructure would allow customizations. The researcher can utilize the fitter forms of search options and also present data in varying formats (depending on the target audience).
Thus, Big Data technology would be the breeding ground to masterpieces in research and development (pharmaceutical industry) as well as patient care (healthcare industry). The gradual reign of Big Data on the pharma world is visible through prediction of McKinsey Global Institute that through Big Data strategies, US healthcare system could produce over $100 billion dollars in revenue.