Industry must use technology to unlock productivity
ROI is at a low point in the pharmaceuticals industry, according the Deloitte 2018 report on the industry:
- R&D returns have declined to 1.9 percent, down from 10.1 percent in 2010—the lowest level in nine years;
- Returns have been impacted by the growing cost of bringing a drug to market which now stands at $2,168 million—almost double the $1,188 million recorded in 2010;
- Forecast peak sales have declined from last year to $407 million—less than half the 2010 value of $816 million R&D returns have declined to 3.2 percent, down from 10.1 percent in 2010.
To improve this picture, traditional ways of working must be shifted, Deloitte comments. “Technology can provide the catalyst for much of this change by either replacing or augmenting work that was previously done by humans.”
Deloitte points to technologies like artificial intelligence and machine learning to speed the way back to profit. “However, implementing these technologies requires shifting skill sets, new sources of talent, and a strategy for when and where implementation should start.”
Deloitte recommends that pharmaceuticals manufacturers find partners with value-adding skill sets to help them make this transition. “We believe the time for this transition is now. Digital transformation is a continuous, fast-moving and multi-year process that even fast followers may struggle to keep up with. Companies should start adopting new approaches to work now. Those that wait will struggle to compete with those who are already engaging with the talent that is needed.”
According to McKinsey: “Big data and machine learning in pharma and medicine could generate a value of up to $100 billion annually, based on better decision-making, optimized innovation, improved efficiency of research/clinical trials, and new tool creation for physicians, consumers, insurers and regulators.“
Pharmaceuticals Firms must embrace data analytics
Deloitte emphasises the need for productivity improvement at pharmaceuticals manufacturers. “This means linking to workflow, cognitive and analytics solutions, to manage the ‘oceans’ of data,” the report adds.
“Operational imperatives are evolving, from drug production and delivery that are limited in scope towards a versatile supply chain addressing the needs of multiple stakeholders. The traditional supply chain approach based on materials requirement planning has progressed to its limits,” writes an executive with Arthur D. Little in a recent note.
Using pharmaceutical analytics, data can be contextualised, and patterns and trends in the data can be established. Researchers can then use this information to assess the data and decide how to present it to pharma companies in the future.
As new treatments become available, new operational models need to be established, with new actors, as well as new technological and logistical systems. Managing these new treatments with traditional operational methods is costly and complex, with high risk of non-compliance. Instead, using new technology, including data analytics and machine learning, also provides new opportunities in terms of improved and more efficient processes.
Transition to Data Analytics
“Just as pharma companies once had to develop marketing as a core competency to remain competitive and profitable, they now have to take advantage of data analytics to maintain a competitive edge. A competitive advantage is now given to whoever can analyze the most data and quickly derive insights by making intelligent, meaningful connections,” explains one industry expert. “It must become a key part of their operations.”
There are vast amounts of data to be used, the expert points out. “Pharma firms need to realize that the problem is not a lack of data. There is an abundance of data everywhere. Pharma is constantly inundated with a massive inpouring of data that goes beyond information obtained from clinical trials. Firms receive both structured and unstructured data from a variety of sources, ranging from doctors notes and data captured through mobile devices to structured clinical research reports and patient questionnaires.”
But it needs to be gathered and structured.
“The first step is to correctly aggregate this data all in one place. Data that is distributed across multiple servers and locations ends up getting siloed wherever it’s stored — whether in doctor’s offices, on researcher’s computers, or elsewhere. If analytics teams aren’t able to access the full breadth of data that’s available on a given subject, they are slower to arrive at novel insights and are more likely to generate faulty hypotheses,” he continues. This is the aggregation process.
Then, the data scientists must normalize the context of these various data points — to structure the raw, unstructured data — before it can be effectively analyzed.
What is required are technologies that map the right unstructured datasets together and translate these datasets into structured formats. Trying to manually structure unstructured data is an expensive and time-consuming process that today’s pharmaceutical companies cannot afford if they want to be competitive. Instead, they must work with experts in AI and machine learning technologies that can automate the data normalization process.
PointData: How to Normalize the Membership Roster Data Processes
Managing data from membership rosters demands the kind of new technology partner that Deloitte describes above. With its years of experience in managing unstructured data using AI and machine learning, PointData is the kind of partner for pharmaceuticals firms to help bring back positive ROI increases.
The amount of roster data is vast, and its unstructured form makes it difficult to work with.
PointData, an outsourcing provider with years of experience, manages aggregation and structuring of roster data for pharmaceuticals manufacturers. PointData updates data in real-time, and ensures that roster data is accessible and easy to use for pharmaceuticals manufacturers while guaranteeing its safety.
The service is provided at a cost that is far below what pharmaceuticals manufacturers would have to pay to gain the hardware, software and know-how to process roster data.
At the same time, PointData helps pharmaceuticals firms adapt to the efficient use of such data, providing services to optimize contracting, rebates, strategic objectives and process assessment.