How Critical is Data Analytics to the Life Sciences Industry?

The life sciences and healthcare industries are currently facing an unprecedented data deluge. A recent article by Forbes magazine observed that a single patient generates over 80 megabytes of data through their electronic medical records and medical imaging data. This number balloons into several terabytes when one accounts for the volume of genome sequence data and the information that traverses across the connected medical and life sciences device ecosystem. As a result, data analytics in life sciences has become the principal focus in this ultra-competitive industry.

The rise of big data in Life Sciences

There is a lot of data to be harnessed, and top life sciences companies have noticed. With rapid reductions in costs of genome sequencing, the amount of genomic data has skyrocketed to over 40 exabytes over the past decade. The rise in genomic data has seen a similar — yet inequivalent — rise in clinical data from various data sources, including population health records, electronic health records (EHR), and laboratory data, which have moved from structured to unstructured formats.

The interaction of EHR data with core genomic data has seen an increase in integrated databases demonstrating all the classic characteristics of big data: volume, veracity, and value. This increase has translated into a range of life sciences data management applications, including the design of randomized clinical trials, digital therapeutics, population health plans, and disease risk modeling across population health registries.

Greater emphasis on precision and personalized medicine

Over the past few decades, there has been a gradual shift from the traditional blockbuster drug development model to personalized and value-based care. This change in mindset has led life sciences companies to invest in a spate of therapeutic advancement initiatives ranging from precision and personalized medicine to mRNA and digital therapeutics. The emphasis on statistical models and derived artificial intelligence algorithms in both these areas has contributed profoundly towards shifting the focus of the healthcare and life sciences ecosystem towards a predictive and prescriptive model of patient-centric innovation and care.

The fundamental difference between the erstwhile blockbuster drug development model and the molecular therapeutic design pursued by life sciences companies worldwide is the increased dependence on a staggering volume of data captured from a diverse range of clinical and biological databases, devices, and patients. As a result, the troika of life sciences companies, healthcare providers, and healthcare payers have shifted the focus toward designing a data-driven value ecosystem and insights enabled.

Unlike previous linear models, the precision plus personalized medicine approach emphasizes patient ownership of data and the participation of patients in the design of care management and therapeutic procedures. Consistent with Leroy Hood’s definition of medicine being predictive, prescriptive, participative, and personalized, the practice of precision and personalized medicine differentiates itself from the traditional models in the use of predictive analytics and data insights as the foundational principles of innovation in the healthcare and life sciences ecosystem today.

More investment in connected devices and IoT ecosystems

The shift toward patient-centricity has amplified the idea of the connected patient from being a fictional premise to a realistic metric that has become the cornerstone of effectiveness across the modern life sciences and healthcare ecosystem. Data flows in the healthcare and life sciences ecosystem are driven by two cardinal sources:

• Patient wearables and connected remote monitoring devices, and
• Ancillary process flows that include inputs from PHRs, EHRs, R&D workflows, clinical trial management tools, and post-marketing surveillance records.

While quantifying the immense amounts of data streaming from these data sources remains an arduous task, an increasing body of evidence suggests that life sciences companies and collaborative healthcare partners view investment in the connected ecosystem of the Internet of Things (IoT) to drive overall enterprise effectiveness.

For instance, an article by the European Pharmaceutical Review suggests a 30-40% improvement in life sciences productivity through IoT solutions. This observation is supported by the fact that life sciences companies consider allocating significant investments into technologies such as IoT, natural language processing (NLP), and computer vision as an imperative to becoming data-driven and digitally transformed (IDC).

Unlike a decade ago, pharmaceutical engagement with patients now has significant ramifications around ensuring patient medication adherence, which tends to be an important factor in influencing the revenues of pharmaceutical and life sciences companies. This increasing contribution by data insights towards revenue and patient engagement is why top life sciences companies are turning to data to fuel innovation and success in today’s competitive industry.