Digital technology’s growing appeal to life sciences is linked to its potential to address well-documented R&D productivity challenges. This explains the recent raft of acquisitions, spin-off ventures and collaborations, and the recent finding from The Pistoia Alliance that 94% of life science professionals expect to increase their use of machine learning within two years.

Yet a lack of clarity about what digitisation is and how best to apply it is holding back progress. Another issue is poor coordination: having several different digital initiatives underway across the organisation can dilute the focus, leading to overspend and duplication of effort.

Companies often also lack measurable business cases needed to drive timely decisions, due to the absence of a focused roadmap. This could be because R&D leaders and teams haven’t been involved sufficiently in setting the aims. Success relies on approaching digital initiatives more as business-technology partnerships than traditional IT delivery projects – an approach that must be extended externally too, with an emphasis on ‘innovation’ when choosing service and/or technology partners. Additionally, R&D organisations may underestimate the need for effective business change management to support the design of new, modern processes that can harness new types of data from a plethora of real-world sources.

A better understanding of what’s possible can be arrived at by considering three converging developments that are driving digital advancement. The first is the ‘Automation to Artificial Intelligence’ continuum – progression from using robotic process tools to accelerate human tasks, moving through machines that learn from data towards the ability to solve more strategic problems intelligently using neural networks.

These opportunities are being fed by a proliferation of new sources of data – in particular non-traditional, real-world data (RWD) from patient communities, electronic health records, and connected devices or sensors. Combined, these possibilities are giving rise to new user experiences, such as increased personalisation of products and services.

Establishing a benefits framework is key to articulating and quantifying benefits, to ensure projects are aligned with each other and to specific outcomes. Such a framework can be developed across three classes of benefit:

Operational excellence

Improving productivity starts with finding more efficient and repeatable ways of doing things, so digital process redesign should be treated as an overarching initiative, with common tools and approaches employed from the start. Establishing an Automation Architecture would enable Robotic Process Automation (RPA) and Machine Learning strategies to co-exist, for example, as companies look to use the former for rapid benefits and the latter for more sustained and scalable data processing – each playing to its strengths as part of a coordinated plan.

An integral decision as part of all this will be who will own and drive the technology for automation – will this be best handled internally, or via partnership with a BPO/technology provider, for instance?

Using business process management (BPM) tools, meanwhile, will help ensure standardisation, to get the most from AI algorithms and data-driven processes – for example so that automation leads to more efficient and reliable regulatory submissions or labelling creation across regions/affiliates.

Product innovation

Expanded data insights offer the potential to drive product innovation, from improving trial feasibility and recruitment, to improving drug safety and efficacy, to arriving at a better understanding of the value of the medication in the real world. Between data feeds from social networks, call centres, spontaneous adverse event reports, and patient focus groups, there are plenty of interesting new real-world data options. The challenge then becomes how to integrate these diverse sources so that they can be analysed using AI to enable maximum, decision-support insight.

Developing a real-world evidence ‘playbook’ can help with this. Companies should also perform continuous assessments of their sourcing approaches for new data types too. Being open to dual sourcing strategies presents a good way to accelerate access to innovation, and/or make this more viable financially.

Customer intimacy

Customer intimacy relies on creating sustainable relationships with external stakeholders including patients and healthcare providers across the R&D lifecycle. Digital initiatives offer the opportunity to make these interactions more informed, reciprocal and transparent, boosting trust and engagement on all sides.

Use of eConsent tools has been a useful starting point, and digitisation can also make the process even more user-friendly by employing interactive suggestions and tips, or creating more engaging multimedia representations of the study journey. New regulations governing patient rights is driving the application of digital technologies too, as a means of managing data anonymisation and access rights.

More ambitiously, the rise of digitally-enhanced patient engagement raises the possibility of companies moving towards new business models, for instance based on sensor-enabled medications which enable improvements in dosing regimen and patient adherence.

Certainly the impetus for change is considerable. With a clear focus of where they want to be and a cross-functional approach to change that is well mapped out, R&D strategists will be better able to convert digitisation to competitive advantage, and maximise ROI.

Dr Nicholas Lakin is a VP in Kinapse’s advisory practice in London