S P E C I A L I S T I N S I G H T

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 AIcompatible IT infrastructure .

www . intelligenthealth . tech 49