Although organisations’ vision for digital technology-enabled transformation is evolving, most R&D groups do not yet fully appreciate all of the possibilities or how to translate these into a practical roadmap which allows for short-term gains as well as longer-term transformation.

A better understanding of the opportunity 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, moves through machines that learn from data to 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 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.

With a clearer idea of what’s possible, R&D organisations then need to draw up a cohesive plan for digitally-enabled change, with tangible milestones and wins along the way. Establishing a benefits framework is invaluable for articulating and quantifying benefits, to ensure that a portfolio of projects can be run which address key R&D challenges and needs in an aligned manner.

This can be mapped to three major strategic priorities, which will be discussed in detail in the sections below.

Improving productivity starts with finding more efficient and repeatable ways of doing things. Digital process redesign should be treated as an overarching initiative, with common tools and approaches employed from the start. Establishing common and consistent assessment criteria can help with prioritising what to automate.

Robotic process automation (RPA) has huge potential for stable processes where accuracy or frequency are high, or where cross-department or system barriers need to be traversed, for instance. Machine learning (ML) has greater potential to drive more flexible and data-driven processes, but the investment in algorithm development is higher. Establishing an automation architecture can enable RPA and ML strategies to co-exist as companies look to use RPA for rapid benefits and ML for more sustained and scalable data processing.

It’s also critical to consider process design and sourcing strategies in direct connection with automation ambitions. The R&D group will need to decide whether it should own and drive the technology for automation internally, or partner with BPO/technology providers to deliver this.

Business process management) tools will help with standardisation, ensuring consistent document management, data quality, master data and reference data to support AI algorithms and data-driven processes.

One practical use case is in improving the control of decentralised data entry involving affiliate regulatory and safety operations, using guided workflows and pre-filled content. This would reduce the effort expended on centralised data correction in regulatory information management systems, and shorten preparation cycles.

Similar efficiency gains can be extended to labelling and submission document assembly.

Expanded data insights offer the potential to drive product innovation - the core mandate of any R&D organisation.

Digital initiatives to date have been concerned largely with generating insight during earlier phases in the development process, through improved access to real-world data (RWD), mined using AI. The opportunity is to be able to profile a disease, candidate molecule or patient population using unprecedented volumes and combinations of data to build predictive models – to identify likely winners and losers in the pipeline much earlier in the development process.

Although many of these investments are highly speculative, the returns are potentially high as they address key scientific challenges - 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.

The choice of which data and where to apply it will be important, and technology must be applied consistently. Between data feeds from social networks, call centres, spontaneous adverse event reports, and patient focus groups, there are plenty of interesting new RWD options. The challenge then becomes how to integrate these diverse sources to enable exploitation through AI.

Developing a real-world evidence ‘playbook’ will help highlight traditional and evolving data sources, questions and case studies, as a means to socialise and optimise new types of real-wold evidence with product teams.

Companies should perform continuous assessments of their sourcing approaches for new data types too. As theiy build AI into their existing data lakes, being open to dual sourcing strategies presents a good way to accelerate access to innovation, and/or make this more viable financially. To access the best AI capabilities, while containing risk, companies are tending towards joint ventures and acquisitions to get up and running.

Customer intimacy relies on creating sustainable relationships during the R&D lifecycle – not just with patients, but also with investigators and other healthcare professionals. Digital initiatives offer the opportunity to make these interactions more informed, reciprocal and transparent, boosting trust and engagement on all sides.

The shift towards digitisation of clinical, medical and regulatory documentation has created the opportunity to increase patient understanding and manage compliance of clinical study protocols. Life sciences companies have started to invest in eConsent tools as they seek to improve the effectiveness and responsiveness of the informed consent process. Digitisation can also make the process more intimate, user-friendly and reassuring to subjects by employing interactive suggestions and tips, or creating more engaging multimedia representations of the study journey.

The rise of patient rights is another critical consideration for life sciences companies. Patient transparency regulations, such as EMA Policy 0070 and the EU General Data Protection Regulation (GDPR), are driving the application of digital technologies to manage data anonymisation and access rights.

Blockchain-based databases have substantial potential here, providing a distributed, decentralised data infrastructure which, in principle, is immutable and insists on trust being established between the data providers (patients, investigators and other healthcare providers) and data collectors (sponsors). R&D companies will need to consider patient access as part of future data management systems and processes, too.

More ambitiously, the rise of new digital engagement models 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.

In the current climate life sciences companies have much to contend with, so 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.