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    Data and technology are etching a new DNA of each touchpoint of our lives today. As we live and breathe, the fourth industrial revolution and its fusion of technologies are dissolving the boundaries of physical, digital, and biological spheres1. As humans, we are more connected than ever before, using extremely advanced technologies, creating unbelievable amounts of extremely granular data. This data explosion is feeding into AI-based quantum computing engines for instant and highly-personalized insights, which help us make better decisions. To operationalize our informed decisions, Internet of Things (IoT) and its connected experience step in to deliver instantaneous desired results. As customers, we are embracing these advancements towards elite customer experiences. As professionals, we need to follow suit, by leveraging data and technology to create innovative products, operational efficiencies, stronger customer equity, and higher profit margins.

    With its storm of big data and digital technology, the fourth industrial revolution is paving the way for significant transformations across all industries, and more specifically in healthcare and life sciences.

    With its storm of big data and digital technology, the fourth industrial revolution is paving the way for significant transformations across all industries, and more specifically in healthcare and life sciences. So much so, that life sciences companies are finding themselves placed right at the core of this revolution, as healthcare data grows faster than that of other industries at 36 percent through 20252. Patient-related real-world evidence (RWE) data forms a significant chunk of this pie, with 74 percent of global consumers agreeing to share their most sensitive healthcare data3. This data has limitless potential towards improving patient lives, with higher patient-engagement, at lower treatment costs, covering more therapy areas than before. Gathering all this data is just a start, but analyzing it for meaningful insights, and operationalizing the insights with assisted technology is imperative for continued success. Meeting the expectations of the entire healthcare ecosystem is impossible without diving headfirst into the disruptive tides of the ongoing revolution.

    This blog lays down the four fundamental pillars that will help life sciences companies garner the necessary disruptive advancements towards patient centricity, commercial efficiencies, and overcome regulatory barriers during the fourth industrial revolution.

    • Data Integration
    • Analytics Industrialization
    • Augmented Intelligence
    • Cross-functional Collaboration


    Data is to healthcare what carbon is to the universe. It is the underlying force driving life sciences companies towards disruptive transformation and bringing them closer to the patients, especially patient-level, payments, and managed care data. Pharma companies are stepping up their data management game to harness the patient-level information – such as Protected Health Information (PHI), to track the complete patient treatment journey and make informed clinical and commercial decisions.

    Data management is incomplete without data integration. Data integration helps maximize the value of data by bringing together siloed pockets of data across the enterprise. One of the biggest problems for transformational innovation is the inability to access data-sets being used by different functional teams, in varying terminology and definitions. Pharma companies need to work towards building an infrastructure to value data integration, encouraging complete, consistent, and accurate data across its lifecycle. Having all the standardized data centrally available can enable insights for clinical trials for product development, efficient commercial operations (including value-based pricing and market access4) and identifying therapy trends with patients’ treatment journeys. These insights can then be used for creating effective medical and commercial operational strategies. Soon, healthcare regulatory bodies are likely to encourage data integration with the adoption of intelligent and automated technologies, including storage of data in the cloud5. Advanced analytics deployed on such structured data-sets can bring companies closer to the patient and gain competitive advantage.


    Traditionally, multiple analytics teams work with siloed data pockets, deriving limited results against the parameters relevant to them. As the results are reported up the hierarchy towards decision-makers, insufficient insights from various disjointed systems, and overwhelming reporting views lead to delayed actions and loss of business opportunities.

    Integrated data must be analyzed and operationalized across the enterprise, at scale. A recent survey confirms that more than 60 percent of life sciences leaders said that having a scalable environment was “most important.”4 Analytics industrialization simply means to automate the entire analytics project lifecycle on a single platform, which can be operated at a beginner skill level, with speed and accuracy. Such self-serve AI-ML-based data management and analytics platforms can directly serve the decision-makers as they pick-and-choose from the interlinked on-demand data, assemble (not create) analytics models of choice, derive instantaneous insights towards their business questions, and create an operational strategy, in minutes. Such automated platforms can help pharma executives analyze patient-level information to make swift decisions for drug development6, clinical trials6, product launch strategies, commercial operations, and market access. Without the pre-requisites of advanced technical skill sets, these analytics platforms can be convenient for enterprise-level efficiency, agility, accuracy, and scale, especially when they are augmented with healthcare and life sciences expertise-based business rules, modules and workflows.


    Augmented Intelligence reflects the enhanced capabilities of human clinical decision making, coupled with computation methods and systems. It is the critical difference between systems that enhance and scale human expertise rather than those that attempt to replicate all human intelligence.7

    Healthcare data is extremely sensitive. It consists of de-identified patient-level information, revealing their demographics, health parameters, treatment journey, prescribing healthcare physicians (HCPs), and much more. The analysis performed on such data-sets helps pharma companies to discover and create new drugs, HCPs to identify disease and therapy trends, and patients to benefit from better treatment outcomes at reduced overall costs. To say that AI-ML-based advanced technology stacks are entirely capable of utilizing this data to improve patient lives without human expertise, would be a bit too ambitious.

    According to the first policy recommendations by the American Medical Association (AMA)8, the development of health care Augmented Intelligence “benefits patients, physicians, and the health care community.” It also lists “clinical decision support, patient monitoring and coaching, automated devices to assist in surgery or patient care, and management of health care systems” as examples of high-quality Augmented Intelligence systems. By augmenting Artificial Intelligence (AI) with human instinct, expertise, and domain knowledge of the healthcare ecosystem, pharma companies can connect with the patients at a more personal level. With patient data from various sources (including wearables and IoT), computational technology to harness it, AI-ML-based analytics platforms to derive real-time insights and operational strategies, and human knowledge about medicine and patient behavior, HCPs will be able to access the unhinged potential of the products created by life sciences companies to improve patient lives.


    Pharma companies often struggle with fragmented analytics teams, dispersed thought leadership, and disconnected operating models. With data integration, synergistic collaboration amongst the various functional teams across the enterprise is crucial. As integrated, centrally available data lakes open doors for cross-functional analyses, collective decision-making towards common goals can never be emphasized enough. For instance, while designing the overall commercial approach for pharma companies, several teams become parts of an interconnected commercial value-chain – starting from the product launch and go-to-market strategy, to structuring the sales force sizes to be deployed across target territories, to designing individual call plans to achieve maximum customer conversions, to crafting attractive incentive plans to keep the sales reps motivated. When constructive inputs from one team flow into the decision-making process of the next team, winning synergies are born, which lead companies on the path of ground-breaking success and sustainability.

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    The healthcare ecosystem is faced with overwhelming transformative pressure, arising from the fourth industrial revolution, coupled with patient-centric expectations and regulatory demands. By leveraging, and carefully institutionalizing all the data and technology at their disposal, pharma companies can rise to this occasion and leap forward for commercial and sustainable success. With the help of next-generation cloud-based AI-ML platforms, pharma companies can benefit from an all-in-one suite. Such platforms are equipped with the necessary technology stacks, industry-relevant business rules, and user-friendly interfaces, making advanced analytics self-servable across the enterprise. By marrying intelligently automated technology with expert human knowledge and consultation, pharma companies can very much embrace the fourth industrial revolution towards sustainable competitive advantage and patient welfare.


    1. The Fourth Industrial Revolution: what it means, how to respond - https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-respond/
    2. Big Data to See Explosive Growth, Challenging Healthcare Organizations - https://healthitanalytics.com/news/big-data-to-see-explosive-growth-challenging-healthcare-organizations
    3. PWC’s Global Consumer Insights Survey 2019 - https://www.pwc.com/gx/en/consumer-markets/consumer-insights-survey/2019/report.pdf
    4. Deloitte’s Big Data to See Explosive Growth, Challenging Healthcare Organizations - https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Life-Sciences-Health-Care/gx-lshc-2017-pharmaceutical-rd-leader-survey.pdf
    5. Deloitte’s 2018 Global life sciences outlook - https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Life-Sciences-Health-Care/gx-lshc-ls-outlook-2018.pdf
    6. Biotech Companies Investing in Key Technologies for the Fourth Industrial Revolution - https://www.biospace.com/article/biotech-companies-investing-in-key-technologies-for-the-fourth-industrial-revolution/
    7. Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan RFI Responses - https://www.nitrd.gov/rfi/ai/2018/AI-RFI-Response-2018-James-Madara-AMA.pdf
    8. Augmented Intelligence in Health Care (White Paper) - https://scipol.org/track/augmented-intelligence-health-care-white-paper/augmented-intelligence-health-care-white-paper