The Pharma and Life Science industries are at the early stages of a fundamental shift as so-called enterprise analytics and advanced data sciences are embedded across the value chain to influence business decision making at all levels.
Companies now need to use new methods for rapid acquisition, curation, analysis, and visualization of large, diverse data sets in today’s disruptive market.
Adopting new technologies for data analytics that can manage and assess the results of personalized medicine delivery and determine the direction of product development is one of the 2017 Pharmaceuticals and Life Sciences Trends.
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The revolution in personalised medicine is increasingly being driven by streamlined access to multiple data points by all key personnel who need to interact, interpret and make actionable decisions.
Clinicians have constantly worked to personalise patient treatments and care according to individual needs. Techniques developed in data extraction and analysis means it is now possible to predict specific responses and interventions, or identify future potential risk.
Unprecedented data offers pharmaceutical CRM the capability for more precise diagnosis as knowledge is advanced through intensive, micro analysis. A key dynamic outcome would be the ability to provide for more molecular and tissue-specific, targeted therapies.
Cloud-based CRM can correlate and merge data from a variety of diﬀerent IT systems to dynamically improve processes of on-demand interaction and effective solution feedback.
Diagnosis has traditionally has been based upon subjective visual microscope analysis of a biopsy sample. However, a diagnosis was reliant on a clinician’s experience or background, which could lead to a request for a “second opinion.”
Technologies are now capable of extracting large amounts of data from samples or biopsies, which can be statistically analysed and quickly reviewed by multiple clinicians to supply a diagnostic consensus and recommendations for therapy. Data uncovered by patient tissue samples and genomic fingerprints provides clinicians with more information from each patient without the need for multiple rounds of testing.
At the same time data processing allows for the discovery of previously unknown factors, which can be used as disease biomarkers and drug targeting, and also reveal the complexity of a disease, such as cancer. Access to use data generated from clinical samples and testing also enables consistently reproducible test results between clinicians and doctors for more accurate diagnosis and appropriate therapy options.
Alignment and comparison of multiple data points from strategic input sources makes possible the precise preparation of individualised, patient treatment plans. The emergence of personalised medicine is a function of big data analysis supplying clinical outcomes from genetic profiling and tissue morphology.
Current practices, in which patients may receive the same drug treatments with variable results, could soon be consigned to pharmaceutical history. By making available all necessary information at the same time as determining diagnosis and patient prognosis, the best treatment decisions can be given on an individual basis.
Pharmaceutical CRM is a seamlessly integrated and easily managed ‘one-platform’ and user interface (UI). Through a powerful combination of knowledge management, self-service and multi-channel engagement, an entire structured integrated service instantly transforms a previous fragmented input into dynamic performance deliverability.