Summary of BMS and Data Analytics
BMS has been at the forefront on drug development for years but the company has recently leaned into innovative uses of data to drive pricing and inform which new drugs to develop.
Driving Meaningful Drug Development
The impact of analytics is particularly promising in the biopharmaceutical space. It allows researchers and drug developers to accelerate efforts to discover, develop and manufacture medications that will help improve the lives of patients.
Data analytics is allowing BMS to transition from mass therapies to precision therapies, with the goal of eventually offering personalized medicine. With the ability for scientists to quickly access information there is opportunity to drive improved treatments.
Teams of scientists are currently leveraging digital capabilities and artificial intelligence to support digital health initiatives. These offer the ability to use real-world data and electronic medical records to drive innovative approaches to drug development. One example is the use of biomarkers to monitor responses to diseases and tracking mechanisms of resistance.
Challenges to Innovation and Use of Data In Drug Development
BMS has struggled to move from legacy computing capabilities to innovative tools and platforms. Many employees of the business have been working in the space for years and are resistant to change and as a result, efforts to drive innovation have faced major head winds. In addition, there is a lack of compatibility between new tools and the previous systems so the transition is slow and often cumbersome.
The volume of data is also extremely challenging to work with. BMS aggregates information from a range of sources including clinical trials and anonymized medical data that protect patient information. The confidentiality requirements of patient data add an additional layer of complexity.
One solution that BMS has investigated is to leverage partners’ cloud capabilities. For example, they use AWS data lakes to store data from a number of sources in a central repository that is easily accessed. Data scientists can then use this data for different analytics and machine learning which leads to opportunities for improved understanding of diseases and treatment effectiveness.
Data analytics has allowed BMS to advance treatments in oncology, cardiovascular, and immunological diseases. It helps researchers make more informed decisions in terms of which drug classes to invest in and how to develop clinical trials so that they are most valuable to the company in the long-term and most beneficial to patients. The future applications of data analytics in the bio-pharmaceutical space are endless and time will tell how the industry will evolve due to these emerging capabilities.