Virtual and augmented reality in life sciences

New data dimensions: virtual and augmented reality in life sciences

Just as engineers in complex physical environments might use enhanced visualisation to gain a clearer understanding of a technical problem, or shoppers might use QR codes in a retail environment to scan products and read more about them on their phones, it’s likely that in next waves of data transformation in life sciences, Regulatory and Quality teams will explore and interact with regulated product information and market intelligence using virtual or augmented reality (VR/AR). It could enable them to uncover new correlations in the data, accelerating discovery and enabling more powerful predictions about the future. Here, Amplexor’s Romuald Braun sizes up the opportunity.


All businesses are swamped by data. If only they could get to the real insights more speedily, they could truly transform the way they operate. Colourful, graphical dashboards with drill-down detail have done much to bring data alive for decision-makers, drawing their attention to what’s important and giving them the chance to slice and dice the data in a range of different ways to see what’s really going on. The logical next development is to be able to ‘walk through’ the data, uncovering new correlations and insights using VR or AR.

It might sound like something from science fiction, but technically much of this is already possible. In fact, in our own organisation, we’ve already started to walk through our own object data models wearing VR headsets to visualise the possibilities.

So how might it apply to life sciences and its management of regulated information and associated operational processes?

The limits of 2D data representations

First, it’s worth delving deeper into why we might need new ways of visualising and navigating complex data sets.

With the evolution of IT, companies are collecting more and more data and making decisions based on it. But to make meaningful decisions, they need to turn this big data into information and experiences that the business can learn from and use as the basis for new action.

Up to now, companies have relied on two-dimensional ways of looking at this, limiting the correlations that can be made or the conclusions drawn. Insights inevitably depend on the angle at which the data is being looked at – when there may be more interesting and so far undiscovered insights within the data layers.

The ability to represent different data objects or assets in 3D models, and turn them in different ways to reveal different perspectives, could be transformational in delivering a richer understanding of a situation, and in projecting how this will play out under different parameters. The opportunity is to uncover previously unseen patterns and trends, and combine and see the correlations between diverse data sources. The extra dimension suddenly allows teams to ‘look around the corner’ of data and what it’s telling them.

As humans, we are visual beings – so it follows that we will derive greater value from the ability to more intuitively ‘see’ what we’re looking at, as something more than sets of numbers in databases, tables or bar charts.

New ways of visualising and navigating data also present new scope for collaborating on data discovery, applying the principle that several brains are better than one. The more people who are looking at the data, from different angles, the more teams will be inspired to dig deeper or to bring further data sets into the analysis.

Potential use cases

So what, specifically, will virtual and augmented (a blend of virtual and physical worlds) reality bring to life sciences?

It’s probably true that 90% of the available VR apps are gaming-oriented today, but even these involve strategy building, and use experiences and data interdependencies to model and forecast future outcomes. In educational and scientific fields, there is a growing range of both free and paid-for VR tools available for investigating and exploring data. Add to this the immense computing capacity now readily available via the cloud, and these capabilities are becoming well within each of non-IT people who want to expand their knowledge by looking deeper into data. And the technology and its application are evolving all the time.

In life sciences, once you apply an object data model across Regulatory and Quality Management processes – so that different data fields such as country, drug type, dossier and document are represented and can be viewed in different combinations, by their different inter-dependencies -there are numerous practical ways companies could apply VR and AR-based data visualisation and navigation for useful effect.

Here are 7 examples:

  1. Pharmacovigilance and Safety, for signal detection

Take the current situation with the Covid-19 vaccines, for which Phase III clinical trials are being conducted with people out in the real world, because of the urgent need to roll out the protection.

Although the vaccines have been authorised as being safe to use, mass monitoring for potential adverse effects is paramount, which means collecting huge volumes of data and analysing it in a comprehensive way. With 5 billion people being targeted, and each individual potentially generating 1Mb of data, that’s an unthinkably challenging prospect – overwhelming not just scientific brain capacity, but the scope of AI (assuming this hasn’t yet been sufficiently trained in what to watch out for). So those responsible need to be able to represent and configure the data in different ways to spot and compare potential adverse effects.

  1. Impact assessment, forecasting and simulation

If there is a change in regulatory requirements, VR or AR visualisation offers a chance to ‘pull on that string’ and visually see how the impact of that change cascades through its operations and current assets.

Although it’s already possible to conduct fairly extensive impact assessments using software, the addition of a third dimension would allow teams to factor in the current availability of resources and of network infrastructure as part of the calculations, and weigh up all of the correlations simultaneously.

Once companies can visualise the fuller impact of a change, across all affected products, they can more accurately determine how realistic it will be to achieve that within the given timeframe.

  1. Quality consistency checks

Today, data sources take multiple different forms, from structured data and numerical values to free-form text, images, video and audio files. Making sense of all of this, and being able to rely on what all of this diverse data is saying, means having confidence in the quality of all of these contributing sources – and being able to spot any overlap.

Introducing the VR/AR element could help ensure consistency across all the data and metadata, highlighting anything that needs to be corrected, completed or removed due to duplication.

  1. Including emotion in PV reporting

If headaches are emerging as a common adverse event, whether linked to a COVID treatment or some other medical intervention, the ability to include the dimension of emotion in analyses could help determine whether stress and anxiety might be significant contributors.

  1. Clinical trials planning and management

Clinical studies can be harder to plan and recruit for as pharmaceutical companies’ focus turns away from blockbuster drugs towards more specialised medicine such as therapies for rare diseases. Adding a VR capability to clinical study planning and management, including dimensions for patient recruitment and availability, could make it easier to factor in all the variables and make more realistic calculations.

  1. Manufacturing & distribution

Getting the BioNTech-Pfizer Covid-19 vaccine to market requires a complex logistics chain and infrastructure, because of its particular temperature requirements. Being able to navigate the complex considerations visually across multi-dimensional data sets would enable accurate planning including any contingencies required.

Roads and trucks could represent supply and demand, and colours signal time or quality issues. In the context of COVID, a model of the earth and spike lengths could signal where peaks of the virus are currently or where demand is building/least fulfilled.

  1. Planning and managing marketing authorisation

Assessing the progress of eCTD submissions by being able to visualise and navigate these as 3D pyramids, and see at a glance which parts are incomplete or waiting for documents or data, and which submissions have deadlines approaching, aided by colour coding, could make it much easier for regulatory teams to keep things moving.

As IDMP submissions become obligatory, advanced data visualisation could provide an invaluable overview across all the different data dimensions, helping companies cope with their increasingly complex data gathering and maintenance burden.

Seeing with fresh eyes, through new lenses

It won’t be long before more intuitive, 3D data modelling and visualisation will become the norm, bringing data alive in all kinds of important new ways for companies – ways that work with the human senses – supported by technology that is becoming an ever more seamless part of how people work.

Already, once-cumbersome VR headsets are giving way to slimmer glasses – without wires – which in turn could be exchanged for content lenses. And all of the heavy lifting – the intense data processing – can happen in the cloud today, connected via continuously-improving network bandwidth.

The AR angle

Take the scenario of a pharmacist asked to perform an inventory check, to identify any non-compliant products in its stock room following a regulatory change. It’s possible to imagine scenarios in the future where they would simply put on a headset which automatically scans for affected products, by scanning and visually comparing the labels of products on the shelves with the correct latest information logged on back-office systems, triggering next actions if these don’t match.

What draws all of this together and makes it so exciting is the prospect of being able to engage with data more intently and naturally; and to simultaneously include others in these explorations – rather than each individual viewing numerical values or graphs sequentially, one dimension at a time.