David Bennett, life sciences advisor, Mind Foundry explains why it's prime time for the pharma industry to embrace data.
Data science
AI and machine learning are rapidly entering the life sciences industry with the promise to boost productivity, speed up innovation and power data-driven decision-making. The next generation of cloud-based solutions are key to the successful adoption of these technologies and moving AI and machine learning beyond the current ivory towers of data science and into the hands of employees.
The pharma conundrum
Life sciences organisations are facing disruption from internal and external sources. On one hand, we are seeing a shifting dynamic of rising payer – or formulary – power in the industry, coupled with a decrease in physician’s prescribing influence and society’s willingness to pay. Deciding where to focus spending sin the face of lagging productivity and R&D returns is compelling more and more biopharma companies to evaluate the possibility of automation.
On the other hand, more agile technology giants such as Amazon and Google have already made their first steps into the sector – bringing extensive financial clout and proven expertise of using emerging technologies. Technology, it seems, will be a key enabler and differentiator to success in the life sciences space – and now even ‘traditional’ players can compete with the giants.
Data, data, data
Pharma, like most industries, is hungry for data. A recent study reveals that C-level executives are especially keen to collect data on brand and reputation, financial forecasts and customer demands. Machine learning technology is now changing this, helping pharma businesses to easily explore data and identify complex patterns from vast data sets including patient health data, clinical trial feedback and research outcomes.
Machine learning – help or hinderance
The need for machine learning in the industry is clear to see. Nine in ten UK SMEs require data science as part of their drug discovery operations and one in two require AI and machine learning. But there are still issues to overcome around time-efficiency and transparency. Average project timeframes expand, not shrink, when machine learning models offer unclear or limited capabilities for data discovery, curation and preparation and are not reproducible across other data sets and business problems. There are also the questions of whether the prediction accuracy is visible, and if output can be understood without further input from specialist data scientists.
Many of these challenges can be resolved by turning to more advanced platforms that automate significant amounts of the data preparation process, provide complete end-to-end visibility in their operations and ensure the human is kept fully in the loop.
Enter humanised machine learning
The demand for data scientists in the pharma sector currently outstrips supply. This means the talents existing data scientists are usually reserved solely for the most business-critical and time-sensitive tasks – particularly in the R&D space –, while other business units such as medical and commercial are shut out. Although these departments generate equally vast amounts of data, they are left unable to harness this expertise to generate insights and refine their operations.
The next generation of applied machine learning solutions is now poised to bring these advanced capabilities into the hands of every department and employee to extract greater business insights and value from their data. A business or science problem owner can quickly harness the full power of advanced machine learning, intuitively augmenting their existing expertise and problem knowledge.
Humanised machine learning platforms such as Mind Foundry help business problem owners become ‘citizen data scientists’ – enabling users of any skill level to prepare, manipulate and visualise data, optimise deployable machine learning models and understand results much faster. They deliver on accessibility and time-effectiveness by eliminating the need for extensive training in data science.
Adding value across all departments
These new developments in machine learning can really make a difference across a pharma organisation. Augmenting the existing workforce with machine learning and moving beyond the realm of the specialist data scientist can inject value to business operations enterprise-wide. It can be leveraged to find and enrol patients in the most suitable trials and facilitate the entire patient journey, enable market access, sales and marketing teams to make better decisions faster and improve productivity.
Mind Foundry is already working with a top-ten pharma company applying ten possible machine learning applications to multiple day-to-day operations, with a view to further refining the transformative applications of AI and machine learning for the industry.
A shot in the arm for life sciences
Machine learning is increasingly being adopted by the life sciences industry to overcome complex daily challenges, but with data science talent in desperately short supply, it’s up to the next generation of accessible machine learning platforms to help organisations succeed.
These novel solutions can be easily deployed to rapidly tackle specific business problems, empowering pharmaceutical companies and other players in the life sciences sector to unlock the full value buried in their data.