Where development of vaccines usually takes years, the U.K. could roll out 30 million doses of a COVID-19 vaccine as early as September, according to the British government, reports CNBC. Data science and artificial intelligence enables scientists such as the ones at Oxford Vaccine Group and Astra Zeneca to deliver life-changing medicines.
Software Country works with Fortune 500 pharmaceutical companies designing and building high-load web solutions for digitisation, analytics, big data, reporting and AI, integrated with core business software, emphasises data load time, the quality of data and automated data integration process as the benchmarks for a successful project as medical giants tackle increasingly large amounts of data.
Some of the challenges that pharmaceuticals face are in optimising machine learning pipeline to deliver very fast and highly scalable calculation pipeline that uses different machine learning algorithms to learn and predict chemical compound activity to reduce the number of real experiments. As well as big data capabilities, it is crucial to have it possible to handle large amounts of data provided in different types and formats that are common in the scientific community.
As an industry leader in global medical safety custom software, Business Development Director responsible for Healthcare practice at Software Country, Maxim Draschinsky explains: ‘Often times the customer has multiple teams with hundreds of people in total, which are involved in pharmacovigilance (PV) process across the globe.
‘Since each team is responsible for its own part of the process, historically most of the process data capture was done using a simple set of tools like Excel spreadsheets, Access databases, Word documents, each team having its own set of files. This toolset does not meet present requirements as it slows down employee performance and does not prevent human errors; besides, there is a lot of duplication due to a lack of data integration.’
Software Country's goal is to increase employee productivity and concentrate on real business process tasks instead of spending time on data quality issues.