In the lead up to our Annual Conference 2015, Stuart Dollow, Chief Executive of Vermilion Life Sciences and one of our conference speakers, explains how our healthcare challenges require a different approach to benefit from new tools and technologies to deliver affordable public health improvements.
With age comes wisdom.
Along with wisdom, comes multi-morbidity and increasing need for care and intervention. It is self-evident that healthcare costs rise as our population ages.
Since infectious diseases, and latterly cardiac disease became less of a threat to longevity, we have new perils. Neurodegenerative diseases and cancers bring not only mortality concerns, but also long-term morbidity and disability that strikes fear into an aging population. Simultaneously, lifestyle changes threaten a resurgence of cardiometabolic diseases, while antibiotic resistance grows.
While this might seem pessimistic, we should not forget the immense health improvements that have been delivered, leading to exponential improvements in cure, care and longevity. Although there are long-term public health concerns, healthcare challenges spur innovators to seek new tools and technologies to intervene.
Technologies such as genomics can personalise disease prediction, providing an unprecedented ability to study people as individuals. They also provide novel targets for increasingly personalised interventions to understand individual responses to medicines or environmental triggers, to explain differences in otherwise homogeneous populations. Such stratification provides us with hope that current phenotypic disease classifications can be genetically or mechanistically classified, to provide insights for personalised therapy, and target healthcare that increases its efficacy, safety and cost-effectiveness.
The wealth of data from these new sciences provides great potential to target and increase cost-effectiveness of care, but only if we can understand its increasing complexity and linkages to outcomes. These data threaten to overwhelm us in complexity, volume and richness, as they emanate from a less structured collection source. This increasing complexity of data, from multiple web enabled ‘smart’ sources, requires a different analytical approach and prompts discussions of ‘big data’ analyses and artificial intelligence. Being able to interpret this volume of data provides hope that new technologies will not only generate data, but that its analysis can make a real difference to implementing innovation to improve healthcare.
Innovation however is risky. Personalised medicine and big data provide great hope, but both are relatively new sciences that will require considerable research to realise the benefits. Much of the experimentation is more likely to fail than succeed. Such failures however, close off blind alleys of research to enable investigation of more successful paths. The path of failure is part of success, but also part of the cost. As such, while personalisation promises greater cost-effectiveness and affordability, high failure rates could negatively impact this hope.
The current medicines development model has served us well, and rests upon well structured sequential steps, with planned hypothesis testing. The increasing disruption of new technologies, the wealth of data produced in real time, with the potential for stratification and personalisation however, makes the current approach seem laborious, and should question its fitness for purpose.
Adopting a standard, population based development plan for a personalised technology will increase time and cost. Equally importantly, it increases the cost of failure. Cumulatively, this results in erosion of patent life and an increased need to quickly recoup investment for further high-risk research. This prompts affordability concerns and is the impetus for price reductions or restrictions of uptake, which in turn impact reward for innovation. Taken to its extreme, medicines development may become the preserve of those few who can afford it, with reduced rewards reducing the diversity of innovators and innovation.
Individually we cannot fail to be excited that new medicines and technologies will revolutionise and personalise healthcare, perhaps in the same way that antibiotics revolutionised medicine. It is increasingly likely however that technology will come from non-pharmaceutical players, given the diversity of approaches. At a societal level however, concerns of cost and affordability, and opportunity cost remain, as tough decisions to maximise health gains, offset reward for innovation that delivers health benefits.
To realise the benefits of personalisation and deep data analyses requires us to think differently about medicines development, to reduce the time and cost of success (and failure) to alter the cost dynamics. Changing development from a stage gate process to a continuum of research with greater alignment of stakeholders, might differently address ongoing risks and their collective management. It could lead to a more flexible, adaptive approach to study design, regulation and pricing that embeds important measures of value for patients and society. Such flexibility may allow more affordable adoption of new technology and its personalisation, while widening the number of innovators.
No one player can solve affordability concerns. Price cannot be considered in isolation, without addressing its causes. To do so risks undesirable consequences that will stifle innovation. Neither can development changes be addressed locally. Development is global, but individual leadership can effect change in our interconnected environment. How a broad collective partnership responds to these challenges will determine the extent of innovation, its cost, affordability and thus its population impact.
Chief Executive Vermilion Life Sciences
Cost, value and affordability are fast becoming the critical issues that are shaping the dialogue and dominating the headlines around our industry. We face an ‘affordability conundrum’.