Most of various IT industries with financial services have had a long track record of updating information and applying analytics to development consumer relationships and building new services, life sciences companies have only in current years start to fully embrace and grip upon the opportunities to manage and apply their data in a methodical way to a range of drug development and patient care problems.
As real science companies’ start to strongly mature their use of data, remarkable progress is starting made in the efficiency of software development and the standard of insights produced at the research stage. However, given the increasing size of learning about human resource and another processes, the opening to deploy data for even larger boost also continue to increase.
Here are 5 major ways we see Big Data and AI impacting the Life Sciences in 2018:
1. As both President Trump and Alex Azar, his applicant to execute the Department of Health and Human Services, have made clear, we can await the environment in the US to be progressively hostile to high drug prices. It will, therefore, be require for life science companies to defend their research forecasts and their benefits margins by utilizing robust data that clearly demonstrate the value of their products.
The opportunity to adequately coordinate data from the real world (e.g. medical insurance claims), genomic research and clinical behinds will offer real sciences companies to unlock answers to a host of standard questions such as the true success of treatments which can then be used to defend pricing location in this increasingly tough market environment.
2. Improving the speed and quality of bi-directional learning between the patient and the cure discovery procedure has been a basic strategy for life sciences companies in the last few years. However, their ability to do this definitely has been hobble by poor data access and data quality issues. As best practice in data strategy (including governance and architecture) advance to move through the industry we can expect the value unlocked by such translational medicine to accelerate.
3. As risks and inability continue to dog many real science supply chains world, the employment of new technologies such as blockchain allows the likely to thoroughly advance levels of control and quality amount whilst at the same time reducing overall costs for infrastructure.
4. As new branches of science deepen our knowledge of genomics and the broader implications of epigenomics, opportunities for utilizing AI to gain previously impenetrable insights are emerging over the horizon. Although still very much at the research stage, indications are that these techniques will increasingly impact fields such as oncology.
5. With all the different fields of study opening up beyond genomics and epigenetics (proteomics, metabolomics, transcriptomics, et al), it’s important to remember the wise words of Prof John Quackenbush of the Dana-Farber Cancer Institute, “At the end of the day, the most important ‘omics of them all is econ-omics.” Accessing and analyzing the right data to deliver sustainable business value remains the central purpose for life sciences firms.
We have known for a long time how a single drug can impact parts of the population in various ways. With hospital Electronic Medical Records (EMRs) offering an progressively completing view of each patient, the ways in which, quiet issues notwithstanding, researchers can boost insights from this data into how their therapies are operating at a more granular level will be increasingly important to the conduct of life sciences companies as they fine tune the delivery and costing of medication to where it is most efficient for patient require and the corporate bottom line. Opportunities for beneficial learning will accelerate further as EMRs and clinical trial technologies become increasingly integrated as we move beyond 2018.
Whatever the coming year holds, one thing is beyond doubt: Exciting new ways to create value and improve patient care await those firms willing to exploit the data tools and techniques that are now emerging.