The following is a guest article by Nutan B, Pharma consulting partner at Gramener.
Advanced analytics, IoT (internet of things) devices, sensors, and the ability to process large volumes of data have rapidly progressed and can deliver step changes in efficiency, quality, and responsiveness in healthcare business processes.
While innovative startup organizations are experimenting with these technologies, large, established pharma organizations are still resistant to this change. There are various reasons why the healthcare and life sciences industry has embraced digital transformation cautiously.
For starters, organizations usually see digital transformation not as a broader strategy linked to business outcomes but as projects to be implemented. Pharma companies tend to prioritize digital transformation retrospectively and consider them to be execution elements.
The pharma industry is far more traditional than other industries in its organizational structure and design, resulting in segregated business and digital teams. This means that digital teams are less equipped to reach all corners of the business units to support them in their digital transformation.
And what’s more, most prominent technology companies that are driving analytics and digital transformation initiatives have limited focus on the core businesses of a pharmaceutical organization. They are more focused on the outer edge patient-doctor setting.
It’s time for a mindset shift in the healthcare industry—digital transformation needs to be at the core, not at the edge.
Digital Transformation Helps Pharma Companies Meet Regulatory Requirements More Efficiently
Regulations, such as EMA 0070 and Health Canada, have specific recommendations for anonymizing clinical data to safeguard patient privacy and sharing trial data to enable the pharma community to build further upon the research.
The challenge for pharma companies is to find a balance between ensuring patient data privacy and disclosing clinical documents while publishing them in a timely and cost-effective way. If done manually, this is a difficult task that takes weeks or even months to complete as clinical documents are full of unstructured data. Furthermore, the manual process can increase the chances of error, resulting in financially draining consequences—healthcare businesses can face heavy fines due to data breaches or re-identification of patient information.
Let’s take a closer look at these challenges and how AI-based data anonymization techniques can help pharma companies overcome them.
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Complication |
Resolution |
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High accuracy PII Identification with Advanced Analytics |
The process of scrutinizing thousands of pages of clinical summaries for each submission to identify PII information is arduous. It is an intensive manual activity that requires domain expertise. |
High-accuracy domain-specific analytics algorithms can reduce the effort of parsing and PII entity identification process by almost 95% – from weeks to minutes! |
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Regulatory authority-aligned risk optimization approaches |
Risk calculation and data anonymization are required to ensure that CSR documents meet the regulatory mandates while maintaining enough transparency and data utility for meaningful downstream consumption. |
With the right degree of alignment with regulatory authorities on iterative risk optimization algorithms, the risk calculation and justification process can be made seamless. This is especially true since the fine balance between transparency and privacy can be achieved efficiently with the help of optimization algorithm automation approaches. |
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User-centered process redesign enabled by digital elements |
Anonymization is a high-stakes game. This makes the review and approval processes to ensure a highly reliable anonymized clinical summary report a high TAT activity with multiple handoffs. |
Well-thought-through change management processes, early stakeholder involvement, well-designed analytics accuracy reviews, and collaborative development of user interface-driven solutions can break the monotony of the review and handoff processes. |
To Conclude
The assumptions on digital transformation possibilities need to be re-evaluated constantly to keep up with the rapid pace of development in areas like analytics, cloud technologies, etc.
Advances in domain-specific analytics are clearing the way for digital transformation to enter core areas of healthcare industries with greater impact.
Organizations need to be holistic, weave digital initiatives into strategy, and take an egalitarian view of digital transformation across the functions and business units to capture the maximum value and equip themselves to ride the wave.
