Digital tools and platforms are remaking every aspect of the life sciences (LS) value chain, from co-prescribed apps to implanted sensors to data queries for drug discovery, and digital is overturning how companies conduct clinical trials and manage production/distribution. The changes extend to how individual patients assimilate and internalize health and healthcare technology directly, through augmentation, implantables, wearables, digital pills and other cutting-edge technologies.
Despite significant progress, however, challenges remain. From our experience, we see life sciences companies investing more in front-end, customer-facing processes and systems than on the back-end processes that would effectively integrate the enterprise into a cohesive, collaborative organization. In fact, LS organizations that fuse cross-enterprise functions into a collective whole dramatically outperform their peers and realize greater productivity, customer satisfaction, regulatory compliance and health outcomes.
Advancements throughout the healthcare industry only serve to reinforce the need for enterprise integration among biopharma and medical device companies. Healthcare organizations are enabling cross-industry connections that support distributed and virtual care, while requiring standardized and reproducible data elements. Even at the consumer level, advanced technologies are changing the individual’s view of health. 23andme and other genetic and biometric solutions and apps have precipitated a societal shift toward greater comfort and familiarity with precise genetic identities and disease profiles.
The Drive Toward Data Modernization
At the core of these shifts is data – torrents of it. In a world rapidly careening toward data levels of 175 zettabytes, LS and healthcare are responsible for much of the data growth and complexity. The first onslaught of standardized data in electronic medical records (EMR) and electronic health records (EHR) is merging with even greater streams of genomic and epigenomic data, phenotypic data and microbiome data, each one exponentially greater than the genome. All these -omics contain vital information for personalized treatments and preventive care. Data will allow for “systems of you” (your genome, biome, etc.) to be placed within the data systems of healthcare (i.e., systems within systems), allowing for commonalities, patterns and, ultimately, cures to be found.
The extremely complex healthcare and life sciences data lake, where data volumes increase exponentially every year, requires a fused-enterprise approach to holistically manage data access and rights. Organizations will need to turn to the elastic scaling of hybrid cloud architectures to support the cognitive computing capabilities that enable accelerated NME identification or newly modernized commercial models. They’ll need to integrate disparate data sources and apply advanced analytics to deliver on new capabilities like value-based contracts for chronic condition management. Applying analytics to integrated data systems could yield insights that enable biopharma and medical device organizations to develop timely, high-quality and personalized treatments.
Keeping Up With Digital Natives
Already, organizations are using “digital twins” to model human body systems to inform clinical decision-making and intervention strategies. While the current approach involves abstract twins, the ultimate goal is to create your particular digital twin, which should enhance the ability to capture, store and interpret individuals’ systems data, or –omics. Indeed, it’s this ability to agglomerate individual data points into data sets that enables digitally-native companies to build predictive and personalized consumer solutions, such as Amazon’s ability to recommend items for purchase that you didn’t know you needed.
In addition to monetizing systems-within-systems of consumer markets, such companies are eagerly eyeing the massive amounts of health and healthcare data (its $3.65 trillion market size is one indication of its potency), with the goal of delivering on the industry’s increasing emphasis on value and outcomes. All this adds up to a ripe new market for conquering.
To effectively compete against – and at times collaborate with – these new entrants, biopharma and medical device companies need to build out their big data management and analytical capabilities. (See how we helped a global pharma company with this endeavor.)
This is just an overview of the issues and factors that are integral to the evolution of the life sciences and healthcare ecosystem. Future posts will assess the digital strategies and tactics needed to compete in a new health and healthcare economy in which care is disaggregated and value-based contracts become the norm.
The application of machine learning to drug discovery, the growing number of approved software medical devices and the integration of behavioral modification solutions to change health outcomes all reflect the challenges biopharma and medical device companies must address in establishing digital governance models, honing DevOps capabilities and modernizing data to uncover hidden meaning and unleash new capabilities.
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