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Dinanath Kholkar
Vice President & Global Head, Analytics & Insights
24 September 2019

Regulatory burdens in many sectors are increasing dramatically. Accelerating privacy regulations, notably the 2018 General Data Protection Regulation (GDPR), is forcing all global enterprises to improve their data management practices.

GDPR governs the collection, use, and movement of personal data of all individuals within the European Union. It applies especially to artificial intelligence (AI) and machine learning systems that depend upon ever-increasing volumes of data to operate efficiently and improve continuously. Indeed, concerns about privacy regulations and their associated burdens were identified this year as the top emerging risk confronting organizations globally, according to Gartner.

This is especially true in highly regulated industries such as finance, healthcare services, and pharmaceuticals. At the same time, healthcare is the fastest growing sector for AI investment, according to IDC. AI adoption in the pharmaceutical sector promises to speed drug development – getting new treatments to the patients that need them – while lowering the cost of those drugs and devices to consumers (and insurance companies, both private and national). AI also is helping pharmaceutical companies navigate the intense and always-changing regulatory environment.

AI in Pharmacovigilance 

The global pharmaceutical industry is confronted with a host of clinical and commercial regulations. They are intended to ensure that new medicines and medical devices are safe, effective, and provide greater health and economic benefits than currently available treatments.

One area in which pharma companies are increasingly using AI is in pharmacovigilance. This is the process of monitoring the effects of already-approved drugs on patients outside the controlled clinical testing environment, and reporting results to the relevant regulatory bodies.

Traditionally, pharmaceutical firms have employed teams of 250 to 300 people, including physicians, scientists, and other healthcare providers, to do this work. At numerous sites, they interpret data in a variety of formats about patients’ experiences with new drugs, especially adverse reactions. Much of this work is manual: collecting documents supplied by providers about patient reactions and outcomes, and scanning social media posts for self-reported experiences.

Teams of physicians then laboriously review the data, which is eventually organized and compiled in exhaustive reports delivered to the relevant regulatory authorities. Any errors in choosing, collecting, analyzing, or reporting the data (say, a misidentified health facility, or an improperly accessed record) can result in a regulator questioning the validity of the pharmacovigilance process. That, in turn, can result in a new drug or device on which a firm has spent billions of dollars to develop – being denied market access or insurance reimbursement.

AI can streamline this process (shrinking days, weeks, and months of manual labor) and ensure greater accuracy in reporting. An AI pharmacovigilance system can receive data files in both structured and unstructured formats (emails, medical records from doctors’ offices and hospitals), and social media feeds. AI can determine the relevance of reported medical issues from information drawn from these and other sources. Using programmed rules, it will alert the firm to potential errors before the pharmacovigilance report is submitted to a regulatory body. The system’s action saves firms millions in do-overs while providing regulators with a transparent record that shows how the firm achieved its results, increasing confidence is the pharmacovigilance process.

Broad Applicability in Regulated Industries

Of course, the pharmaceutical industry is hardly the only regulated sector deploying AI compliance solutions. In insurance, for instance, AI systems can examine suspicious payouts, looking for fraud. In banking, AI systems can rapidly examine thousands of transactions for anomalies that could signal money laundering activities that could open the institution to ruinous penalties from regulatory bodies.

In all these examples, the connective tissue is the collection of ever larger data sets that enable the machines to continue to improve their accuracy while avoiding the potential for bias generated by missing or incomplete data.

Organizations that can manage their data responsibly and commit to an ongoing search for new data source can improve their operations, lower their costs, and thrive (and gain an advantage over their less digitally advanced competitors) in even the most highly regulated environments.

Dinanath Kholkar is Vice President and Global Head of Analytics at TCS. He is the author of the article “Building the Unbiased and Continually Self-Improving Machine,” in TCS Perspectives Volume 12.

About the author(s)
Dinanath Kholkar
Vice President & Global Head, Analytics & Insights

Dinanath (Dina) Kholkar is Vice President & Global Head, Analytics & Insights at Tata Consultancy Services (TCS). In this role, Dina guides some of the world’s best companies in their journeys to unlock the potential of their data through the power of analytics and artificial intelligence (AI) to uncover new business opportunities, drive business growth and increase revenue streams.

Dina has been recognized as one of the top 100 influential global data leaders and data visionaries. He advocates ‘data centricity’ as a strategic lever for business growth and transformation. His thought leadership in addition to his team’s expertise and collaborative working with customer organizations is empowering them to realize the power of their data in real-time decision making and ensuring success in their Business 4.0 transformation journeys.

Dina holds a bachelor’s degree in electrical engineering. He has been providing industry leadership to the IEEE Pune section for over 15 years; currently Chair, Industry Relations and Membership development. He also provides leadership, guidance and strategic direction in domains including education, sustainability, agriculture, and ‘data for good’ through his volunteering work at IEEE Pune Section, Pune International Center (PIC), and the Tata Group. Dina is a member of the Board of Governors of his alma mater Veermata Jeejabai Technological Institute (VJTI), Mumbai and actively involved in the institute’s alumni association. He is a review committee member, Indian Statistical Institute, Kolkata and is on the advisory committee at Pune Knowledge Cluster (PKC).