Investing in digital competencies, infrastructure and interoperability
While AI models rely on data to train and learn, improvements in the infrastructure that algorithms run on, including applications, hardware and software, are key factors enabling the utility of AI.56 In the future, data collected, processed and used in real-time by innovative medical devices will be biopharma’s new ‘currency.’ As such, a key differentiator for companies will be how they can spend and earn this currency by generating insights and evidence from multiple, reliable data sources.
However, this will depend on investment in a robust digital infrastructure as AI cannot be imprinted onto an existing siloed digital system. Biopharma companies should prioritise developing the right technology assets to establish a cohesive and interconnected infrastructure with a high level of connectivity and interoperability that supports secure and transparent data exchange. Digital transformation will also impact business models, the development of new products and services, and transform how companies engage with HCPs, patients and other customers.
AI-powered post-market PV and PSPs have the potential to increase the connectivity, effectiveness, efficiency and resilience of the system.57 The combination of predictive data analytics, improved infrastructure (such as 5G connectivity) and novel diagnostics increasingly become more embedded and support a more preventative model with a focus on value (quality of care and outcomes), not the volume of care delivered. These changes will enable HCPs to offer more patient-centred care and spend more time communicating and providing compassionate care.58
The deployment of AI will also require significant changes to roles and responsibilities across the post-market PV value chain. Biopharma employees will need to develop new skills and talent and take into account the increasing volume of remote work. Many biopharma companies are consequently re-evaluating their workforce model. This trend could cause talent redistribution and create a need for up-skilling and capacity-building while helping employees stay responsive to changes. The next generation of biopharma PV and PSP talent will need to be agile, digitally literate and open to continuous learning as part of their career development.59
Moreover, to realise the benefits of AI, the skills and talent required by biopharma companies will need to include advanced analytical cognitive and digital skills, data scientists and software engineers who understand how to design a digital product and solution that meets patient needs. Therefore, there is a need for skilled interdisciplinary leaders to share learnings and support new business and operating models. The talent shortage needed to support digital transformation initiatives is one of the most significant barriers, so expanding and upskilling talent should be a top priority for the C-suite.
Improving transparency and trust in technology and data
PV and truly personalised PSPs are dependent on trust. As patients become increasingly comfortable with virtual care and RPM through wearables and apps, their service expectations will increase. The ability to connect with their HCP on their terms and continuously share the information they choose will be increasingly important in empowering patients to become more active participants in their own care and support the shift to preventative approaches.
Biopharma’s increasing access to personal data generated through PSPs comes with a greater responsibility to strike a balance between protecting individual privacy and utilising the data to improve patients’ health and quality of life.60 Companies who demonstrate active steps to protect patient data will gain a competitive advantage while empowering individuals to understand their own data. They are also likely to continue to build greater trust with patients. While a PSP alone cannot drive differentiation and increase trust and engagement, programmes that are well-designed, interactive and tailored to personal preferences, can.61
As biopharma embraces AI-powered digital transformation across the post-market ecosystem, the amount of patient data in the hands of these companies will exponentially increase. This data needs to be responsibly and securely handled across an integrated data network. This requires biopharma to include ethical considerations into the design, build and deployment of AI-powered systems. This also includes testing and remediating systems that unintentionally introduce bias and treat users unfairly.
An increasingly personalised patient experience
Over the next few years, patient-centric, co-created experiences will evolve to make patients more equal partners in decision-making throughout their care pathway, helping biopharma deliver better, more personalised outcomes. However, genuine patient centricity means understanding the patient’s lived experience of their condition – what the individual patient values and needs, and what is most likely to result in a positive health care outcome.62 Implementing AI in PSP design provides an opportunity to view patients’ health holistically. As biopharma companies capture each patient’s unique clinical history, socioeconomic factors and previous experiences, they can create increasingly personalised solutions, providing a seamless delivery of ‘what customers want, where they want it, and when they want it.’
AI provides the opportunity to predict, at a personal level, a patient’s disease trajectory and recommend treatment or highlight the need for potential intervention while improving patient engagement. Research shows there is an expectation from society for this kind of approach, with 83 per cent of patients saying it is important that providers know them personally, beyond their health record.63 This increased level of personalisation has been shown to improve patient engagement and outcomes and increase the level of trust between providers and patients.64 Examples include Orion Health’s AI Engage platform which provides personalised, relevant educational information for patients, encouraging them to actively contribute to their health management by sharing their health information and improving ease of access to their HCPs.65
Many aspects of drug delivery can be personalised in biopharma, from the tailoring of dose to its administration, including frequency. This can lead to better therapeutic outcomes and decreased AEs.66 When combined with AI, a personalised drug delivery regimen can create an interactive open feedback loop between the patients’ needs and the treatments. Twin Health has created a Whole-Body Digital Twin, a dynamic, digital, AI-enhanced representation of metabolic function derived from personal health data points. It monitors daily activities and personal adaptation.67 Their Twin Service provides each user with personalised and precise activity, medication and sleep management guidance.68
The development of digital devices to support patient participation in a PSP should include input from end-users to assure ease of operability and their engagement in the shift from data entry to data quality assessment. Selecting outcomes that provide meaningful business values and achieving them in a specific timeframe will drive PSP professionals, HCPs and patients to adopt these AI capabilities. AI-enabled platforms allow HCPs to interact physically and digitally concurrently. Importantly empowering patients includes explaining how AI is used in jargon-free language and providing use-cases.
Co-creation of patient support programmes
Engaging with patients from the beginning of the PSP development process can significantly improve outcomes. The National Institute for Health Research (NIHR) aims to bring patients and life science companies together earlier in the research and development processes to instil a culture of partnership between the biopharma industry organisations and patients, which can be expanded to the development of PSPs.69 With greater transparency, biopharma has scope to go beyond co-design to co-creation, where patients are involved throughout development, testing and roll-out. Creating solutions that patients are invested in helps to build advocates for the PSP and ultimately scales a biopharma company’s ability to make an impact through patient partnerships.70
A more collaborative relationship with regulators
By collecting information on AEs and acting in response, regulators aim to protect the public from emerging safety issues throughout a treatment's life cycle. In January 2021, the FDA released the AI-based SAMD Action Plan.71 The FDA acknowledges that one of the most significant benefits of AI/ML is its ability to learn from real-world use and experience to improve its performance. The FDA has declared its commitment to support a patient-centred approach and emphasised the need to be transparent about the functioning of AI-based devices to ensure users understand the device’s benefits, limitations and risks.
Similarly, in April 2021, the European Commission published its proposal on AI Regulation, introducing a comprehensive, harmonised regulatory framework for AI with significant turnover-based financial sanctions. For life sciences, the AI Regulation is designed to complement and work alongside several existing legal frameworks, particularly the product safety / CE regime and data protection under GDPR. More specifically, the provider is expected to design the system to ensure:
- the individual is aware they are interacting with an AI system
- that if the AI system involves emotion recognition or biometric categorisation of individuals, the user must be informed that this is happening
- if the AI systems generated content has been artificially created or manipulated, it must be disclosed.72
The application of RWD to create adaptive labels brings new opportunities and challenges to the regulatory relationship. Intelligent automation of pharmaceutical labelling can increase the speed and accuracy of labelling compliance as the complexity and expectations of labelling regulations continue to evolve. Between January 2017 and September 2019, 14.9 per cent of drug recalls by the FDA were due to labelling issues.73
In the future, AI will compare any number of countries’ regulations simultaneously and adjust before non-compliance occurs, helping to ensure resilience against the ever-changing regulatory compliance landscape. This faster updating of new requirements and other label adaptations will enable critical information to be processed and relayed to patients quickly, increasing transparency between the industry and patients.
Transparent and collaborative communications between regulators and biopharma companies about the deployment of AI-enabled technologies in post-market surveillance and PSPs will be essential in maintaining regulatory compliances. Companies will need to report serious incidents and malfunctioning, taking appropriate measures or even withdrawing the AI system when it presents a risk to health, safety, human rights or a public interest. In addition, transparency is essential to ensure that the individual is aware of being exposed to an AI application. Nevertheless, new data-driven approaches will enable biopharma to work more collaboratively with regulators to balance risk and create new evidence frameworks for PV with blockchain-like technology used to verify the origin of data submissions.
The future of post-launch patient support
Post-launch, biopharma companies will need interoperable health data to track and engage with patients remotely and intervene with personalised treatment options at the right time. This requires integrating and analysing patient data in real-time to determine when an intervention is needed. Applying AI to monitor the safety of patients will be critical, particularly in detecting potential AEs proactively and in real-time. Insights into RPM beyond safety will also be important, and compliance monitoring is an area many biopharma companies and start-ups are targeting. AI-enabled PSPs will transform biopharma’s relationship with customers improving enrolment, adherence and retention, delivering improved patient outcomes.