The Future of Data Analytics in the Life Science Industry: What's Next?

The Future of Data Analytics in the Life Science Industry: What's Next?
Janne Bate

3 minutes

The Future of Data Analytics in the Life Science Industry: What's Next?

The future of data analytics in life sciences is set to transform organisations across the world. Find out how...

If you know the life sciences industry, you’ll know that the application of data and analytics to the sector is nothing new. Even in 2019, over 60% of life science companies were investing in the technology. In fact, D&A in life sciences is so embedded that between 2021 and 2025 the global life sciences analytics market is set to progress at a compound annual growth rate (CAGR) of 11.83%, generating $15.95 billion in profit. 

Today, in an increasingly competitive business landscape, efficiency game-changers like AI and Big Data are fast becoming industry essentials. Research from Deloitte shows life sciences businesses are leveraging the technology for more than one business crucial objective, including: enhancing existing products (28%), creating new products and services (27%), making processes more efficient (22%).

But not all organisations are activating the full potential of AI and Big Data. Industry analyses from McKinsey & Co reveal that most pharma companies are yet to scale from an experimentation model and generate the full return on their investment. 

Read on to discover:

  • How to secure a competitive advantage with data and analytics
  • How data and analytics accelerate drug development and discovery
  • How data and analytics are transforming patient pathways


How to Secure a Competitive Advantage with Data and Analytics

As the pandemic has shown, unprecedented events can rapidly change market trends and disrupt organisational strategy. Data offers the life sciences a way to manage unprecedented disruptions through predictive modelling of market share for specific products.

By forecasting sales volumes based on the unique features of products and the market in real time, AI technology can hugely improve stock management efficiency. Meanwhile, stakeholder trust data can also help leaders understand the best way to position products to improve market share in the current market. 

Data and analytics will also become key for supporting operational decision making and ensuring a consistent approach to care. For example, data could be used to pinpoint bottlenecks in processes, identify stretches in resources, and differences in patient experience. AI also has huge potential in automation, which could reduce administrative workloads and improve the day-to-day running of facilities. 

And for those businesses still on their digital maturity journey, there’s more than one route to harness D&A for market competitivity. When it comes to harnessing data, technology companies have the upper hand with readily available experts and infrastructure, making them ideal partners. 6 of the top 10 tech giants are venturing into health and life sciences. 

Deloitte and AdvaMed’s recent survey reveals that 82% of MedTech R&D leaders are looking for partners outside of the medical industry to help drive innovation. Meanwhile, the biotech sector is readily collaborating with AI start-ups, and larger organisations to fuel their drug development pipelines.

How Data and Analytics accelerate Drug Development and Discovery

Less than 10% of drug candidates that make it to clinical trials progress to market.

A lack of knowledge on the 3D structure of drug compounds and targets, as well as a reliance on animal models that fail to accurately represent human physiology, are just two factors underlying this limited success rate.

By leveraging data and machine learning, AI technology can improve efficiency across every stage of the drug discovery process, including:

  • Initial prediction of the target protein’s role in disease
  • Design of in silico compounds from libraries to improve structural knowledge
  • Novel target identification 
  • Prediction of the structure – activity relationship
  • Prediction of ADMET properties
  • Selection of ideal patient population for clinical trials
  • Observation of adverse effects

When it comes to discovery, machine learning can be used to detect patterns in both structured data (e.g. laboratory results, electronic recordings, demographics, IoT-generated data) and unstructured data, including using natural language processing (NLP) from medical journals and clinical notes to contextualise medical data. The result is a system that not only analyses and interprets vast volumes of data quickly, but is also self-corrective

With huge and varied data sets, as well as increasingly sophisticated artificial intelligence and machine learning systems, we can identify and analyse much broader trends across healthcare. We can use Big Data to identify causation, distribution, determinants, and patterns across a high volume of different and complementary data points to tell us more about current illnesses. In the world of epidemiology and preventative healthcare, this has the potential to change lives.

How Data and Analytics are transforming patient pathways

Various technologies from wearables, apps, systems, and even medical devices combine to create the Internet of Medical Things (IoMT). 

According to research by Deloitte, the IoMT industry is expected to reach $158.1 bn in 2022, while connected technologies will make up 42% of R&D budget by 2023. 

IoMT will improve the speed, accuracy and insight behind diagnosis and treatment. With vast volumes of data collected from multiple difference sources, often at the place of activity and in real-time, we can build up a much bigger picture of both individual health statuses and overall operational performance. 

Utilising AI within these technologies offers the opportunity to introduce personalisation across diagnosis, treatment planning, patient monitoring and even drug discovery – paving the way to personalised medicine. 

Personalised precision drugs have long been conceptualised in medicine but remain too expensive to manufacture without AI intervention. In the future, personalised drugs could provide specific treatment to individual patients, based on their personal genetic background and symptoms to avoid side effects. This would aid early intervention and improve treatment adherence.

The explosion of IoT-enabled medical devices and the drive towards integrated care systems (ICSs) will see also users empowered to make their own decisions around healthcare. 
Accurate, real-time data and trusted advice will be pivotal to help customers take the leap from passive recipients of care to active participants, with options on everything from data sharing to treatment and preventative healthcare. 

For example, individuals at risk of developing a condition due to data, family history and symptoms may choose to opt into additional screening tests. This will result in a more personalised, more accountable approach to healthcare — and one that will pave the way to more customer-centric designs, particularly when it comes to MedTech. 

What’s next?

As our data gathering capabilities become more sophisticated, and the volume of data we collect becomes more widespread, the data-driven business imperative is growing. Many of the other tech challenges facing the life sciences sector – collaboration, interoperability, personalisation, agility – will also come down to how well we know and use our data. Consider this my call to action – it’s time to invest in your life sciences analytics.

Are you interested in learning more the latest data trends, and how they could impact your business this year? 

Download our sister company Lorien’s free whitepaper Data Domination: the data trends transforming your business in 2022 and what HR and talent acquisition need to know about them, for an insider’s view on one of the most critical tech trends today.

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