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Guest Commentary: How humanized machine learning is giving the life-sciences industry a shot in the arm
The life-sciences industry currently finds itself facing a perfect storm of challenges, from society’s rising concern over health costs to changes in the physician’s role. Artificial intelligence (AI) and machine learning are being rapidly adopted to transform existing business processes and unlock additional value and insights, but the required data science talent is in desperately short supply. The next generation of accessible machine learning platforms will be crucial in helping departments from R&D to commercial to unlock the full value of their data..
Research from Deloitte (in a report titled “Unlocking R&D productivity: Measuring the return from pharmaceutical innovation”) shows that productivity and R&D returns in biopharma companies have dropped to their lowest in nine years. The conundrum for these companies is where the R&D burden should fall, and they are continually evaluating whether to do it in-house, outsource to smaller companies or involve academia in the process with a view to pursuing automation.
All this is at a time of an emerging and shifting dynamic of rising payer—or formulary—power while physicians' prescribing influence decreases, and the cost of cutting-edge healthcare begins to exceed society’s willingness to pay.
Meanwhile, larger and more agile and tech-focused companies such as Google and Amazon are sizing up the life-sciences space with an eye to discontinuous disruption of the established order. These disruptors bring extensive financial clout and proven expertise with emerging technologies as a key enabler and differentiator—but technology also holds the key to “traditional” life-sciences companies fighting back.
Machine learning to the rescue
How can the life-sciences sector as a whole boost productivity, reduce the time to market and unlock the full value of their data? The answer lies with pharma’s ability to successfully internalize and operationalize the promise of AI and machine learning and move it beyond the current ivory towers of data science.
The latest “PwC CEO Survey” on 2019 healthcare and pharmaceutical trends revealed the stark contrast between data abundance and quality. C-level executives are hungry for data on brand and reputation, financial forecasts and customer demands, but they simply do not have access to data that is fit for purpose or tools that are capable of deriving comprehensive business insights from the data that they do have. This is at a moment when the industry generates more data than ever before.
New developments in applied machine learning offer the opportunity to quickly explore data and identify complex patterns from vast data sets including patient health data, clinical trial feedback and research outcomes.
Solving AI pain points for the industry
Pharma businesses are already seeing return on investment from initial projects. Over in the United Kingdom, the Medicines Catapult report “State of the Discovery Nation 2019” revealed that 90 percent of small and medium-sized enterprises (SMEs) in the pharma industry required data science as part of their drug discovery operations, with half of these SMEs requiring AI and machine learning. But there are still issues associated with AI in the life-sciences industry.
Capabilities for data discovery are not clear, and curation and preparation are still limited, all significantly lengthening the average project timeframe. There are also transparency considerations. Is the selected machine learning model reproducible across other data sets and business problems? Is the prediction accuracy visible and can output easily be understood without ongoing reference to specialist data scientists?
Many of these pain points will be resolved by turning to platforms that automate significant amounts of the data preparation process, are truly end-to-end and transparent in their operations, and ensure the user is kept fully in the loop.
Humanized machine learning empowers the citizen data scientist
With talented data scientists in scarce supply, the skills gap is continuing to pose challenges to life-sciences organizations. Existing data science departments do not have a wealth of data scientists, so their talents—and workloads—are reserved solely for the most business-critical and time-sensitive tasks, particularly in the R&D space. This means that other business units such as medical and commercial that enjoy an equally vast although different wealth of data are unable to harness this expertise to generate insights and refine their operations with any velocity.
New applied machine-learning technologies enable these life-sciences organizations to bring machine learning and other advanced technology within the remit of employees of all skill levels, helping these problem-owners become “citizen data scientists” in their own right. They bring the ability to prepare, manipulate and visualize data—creating, managing and optimizing deployable machine-learning models within minutes into the hands of every employee, effectively coaching the user from data preparations right through to model deployment and management.
The bottlenecks of a limited data scientist talent pool are avoided and projects can be completed quickly, without having weeks or even months added to their timeframe while waiting to be resourced.
Platforms like Mind Foundry are designed with accessibility in mind, eliminating the need for extensive training or a background in data science. A business or science problem owner can quickly harness the full power of advanced machine learning, intuitively augmenting their existing expertise and problem knowledge.
Far reaching applications: Unlocking business value across the enterprise
Mind Foundry is already working with a top 10 pharma company applying 10 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.
Beyond all the promises that have been made for AI in drug discovery, the real transformation in productivity in life-sciences companies value chain will be wrought by augmenting the existing workforce with AI and moving beyond the realm of the specialist data scientist. Machine learning can be harnessed to find and enroll patients in the most suitable trials and facilitate the entire patient journey. Market access, sales and marketing teams can make better decisions faster; the productivity of other scarce resources, such as MSLs, can be transformed; and patient-centric real-world evidence can be made truly useful.
Transforming every step of the life-sciences value chain
While we already talk about the applications of AI and machine learning in life sciences, the next generation of cloud-based solutions is now poised to bring these advanced capabilities into the hands of every department and employee that has a data set and a desire to extract greater business insights and value.
These 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 of their data.
David Bennet brings 30 years of international software industry leadership experience (predominantly focused in life sciences and healthcare) to Mind Foundry as its life sciences advisor.