We delve into Capgemini Research Institute’s groundbreaking report, ‘The Connected Health Revolution’, with Geoff McCleary, VP of Global Connected Health Practice at Capgemini. McCleary sheds light on the report’s findings, discussing the rapid acceleration of connected health technologies, the impact of AI, and the steps life sciences organisations should take to navigate these transformative changes effectively.
The ‘The Connected Health Revolution’ report highlights that life sciences organisations expect over one-fifth of their revenue to come from connected health in the next five years. Can you elaborate on what factors are driving this optimistic revenue projection?
COVID-19 catalysed a rapid shift to digital health, with consumers and patients adopting telemedicine. Since then, recent technological advances have further accelerated the adoption of connected health.
Innovations, such as smart medication adherence apps, connected rehabilitation tools, and clinical decision support tools mean that companies can improve patient outcomes, tap into data and AI-driven patient insights, bring new products to market sooner and improve their existing products. Consumers are also realising its potential and consumer appetite is on the rise, with one in three consumers already owning a health wearable. More product offerings and increased customer appetite translate to significant revenue growth opportunities in the next few years. In fact, our research finds that life sciences organisations anticipate that connected health will contribute an average of 22% to their annual revenue by 2028.
Connected health is an even more exciting prospect because of a growing focus on prevention on both sides of a diagnosis. Before diagnosis, healthcare workers are leveraging digital biomarkers and smarter, predictive programmes. After diagnosis, we have seen the expansion of remote monitoring and digital-driven at-home care. Overall, healthcare organisations are increasingly concentrating on value-based care, a system where healthcare organisations are incentivised to go beyond a specific health issue to focus on overall health outcomes. This model is described as one of the most equitable, sustainable and transparent approaches to healthcare provision by Oxford University’s Centre for Evidence-Based Medicine. Already, a quarter of organisations offer subscription-based data services such as medication reminders and analysis of health data. Value-based care models create recurring revenue and also generate opportunities for upselling and cross-selling, increasing customer lifetime value.
With 63% of life sciences organisations having a connected health product on the market or in development, how have these organisations accelerated their product development cycles since 2021?
As per our latest research, nearly 20% of BioPharma organisations have now rolled out connected health offerings compared to just 3% in 2021. This rapid acceleration is due to substantial improvements in digital and technological capabilities, across a wider range of digital programmes, including clinical research and patient services. Organisations are also leveraging innovative approaches to product development and forging strategic partnerships to speed up this progress. In our work with one global pharmaceutical company recently, we introduced design methodology into the development of an AI-driven clinical trials platform which is just one example of how healthcare organisations are streamlining the product development process.
Organisations have also concentrated on addressing immediate healthcare needs, for example with mobile apps and smart medication adherence monitors. Unlike more complex medical devices which can take years to bring to market, these products can be developed and deployed in months, and can then be updated and customised according to market demands and patient needs. For example, we recently worked with another global pharmaceutical company to gather insights about drug adherence challenges faced by patients and develop an app which could support and motivate patients, illustrating the power of digital solutions to positively influence patient behaviour.
The report mentions that three in five life sciences organisations are developing roadmaps for integrating generative AI, with over half piloting it for patient and HCP interactions. What are some successful examples of these AI use cases, and what challenges do organisations face in implementing them?
AI is already playing a key role in product development and will become even more impactful in years to come. Among the possible use cases, one of the most successful we have seen is a clinical intelligence engine which used Google Cloud large language models (LLMs) to offer proprietary solutions to assist doctors in determining the best course of action for patients, answer medical enquiries and generate summaries of clinical text. In another example that’s highlighted in the report, an OpenAI GPT is deployed to assess the optimal vaccine dose for a patient.
However, organisations face challenges with a lack of AI expertise across their workforce, inadequate data availability and management, and concerns around bias in the implementation stage. Life sciences organisations must think strategically and develop a clear roadmap rather than rushing to implement AI; identifying the right tools to use, upskilling their workforce, ensuring they have sufficient data to create personalised treatment plans and mitigate biases, and building a Generative AI-compatible IT infrastructure.
Despite progress, the report indicates a lack of robust data management capabilities in many organisations. What steps should life sciences companies take to establish a common framework and standards for data handling in connected health?
In the absence of good-quality data, connected health technology will be ineffectual. Patient data is also highly sensitive and must be properly protected. So, there is still work to be done. Less than 50% of organisations surveyed have mature capabilities in data aggregation, design for data interoperability and data analysis. Less than half of those surveyed consider themselves ready for data privacy laws.
In short, organisations know that they must invest in the right foundations to derive the greatest benefits from connected health. In practice, this means outlining the roles and responsibilities of different parties in a data management process and standardising data across the enterprise to enable interoperability. Organisations should also make sure they choose the right operating model for their data management, be it a centralised or a hub-and-spoke model or a mixture of the two, depending on their data maturity.
Finally, collaborating with the right partners in a data-driven ecosystem will be important to promote the sharing of insights to improve patient experience and boost operational efficiency.
The report points out a shortage of technical skills in areas like AR/VR and Generative AI. How can organisations effectively upskill their existing workforce, and what strategies should they employ to attract new talent with these specialised skills?
There’s no one-size-fits-all solution. Every organisation will be different, so they need to take a tailored approach to upskilling employees in new technologies. First, they should define the skills they need to deliver the suite of connected health products that will provide the most value to their customers. Their existing employees will likely already have the potential to meet many of those skill sets, so it is the responsibility of the organisation to provide tools to help those employees assess their strengths and develop those talents that they may not be using in their day-to-day roles. It is important to communicate the benefits of upskilling to employees, showing how they can become more employable and at the same time help the business achieve its objectives to provide better care to patients.
When designing internal training programmes, or choosing a training partner, organisations should consider how to make the learning process as engaging as possible. At Capgemini, one of our business units uses ‘gamified’ learning platforms where employees can win points and certificates upon completing a new training course. Once employees have had a chance to train in new skills, they also need a chance to exercise those skills in a real-world context. Strong training capability also has a role to play when attracting new talent with specialised skills. Alongside the normal considerations like remuneration and opportunities for progression, research has shown that 63% of employees would prefer to join an organisation that is known for its upskilling programmes so they can develop their skills further.
What do you believe are the most critical next steps for life sciences organisations to ensure the successful and sustainable growth of their connected health initiatives over the next five years?
In the report, we set out seven practical recommendations to harness the full potential of connected health and drive the best possible business and patient outcomes.
First, organisations must define and articulate the vision and value proposition for connected health. They should consider the therapeutics area or connected solutions that will provide them and their patients with the most value. They should then concentrate on designing digital product offerings that deliver the proposed value. This means measuring the usability and effectiveness of the solution to ensure that it is meeting healthcare and wellness needs at every step.
To achieve this, it will be necessary to develop scalable, secure and compliant data infrastructure and operations. Organisations should establish robust data and analytics frameworks to facilitate the aggregation, management and processing of health data, ensuring it is accessible and reliable for advanced analytics. They should then turn their attention to enhancing capabilities in digital, engineering and human-centric design, developing a laser focus on innovation and human-centric design, while never losing sight of complex regulations and stringent data security requirements.
Along this journey, they should engage with partners, alliances and early-stage innovators to share expertise and capabilities. This involves seeking out external expertise, technologies and resources when they are not available in-house to accelerate innovation. Alongside this, they should also take measures to bridge the talent gap, upskill existing staff, hire talent, partner with startups, or hire contract workers to address talent shortages.
Finally, they should develop robust quality assurance processes to ensure regulatory compliance. These frameworks should set out standard operating procedures at every step, from design to development, to testing, to operations, to launch and post-launch.
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