Foreword
Written in early 2023, at the first crest of the current AI wave. The argument, that retrospective outcome measurement cannot carry personalisation, which needs a forward model, has aged better than the hype around it.
The world of healthcare stands at a precipice, caught between the stalled promise of Value-Based Health Care (VBHC) and the transformative potential of AI-driven digital twins. Malcolm Gladwell's concept of the tipping point suggests that a series of seemingly insignificant events can accumulate, eventually reaching a critical mass that sparks significant change. In healthcare, AI-driven digital twins are poised to be that catalyst, revolutionising the way we approach prevention, diagnosis, and treatment.
VBHC has been hampered by numerous challenges, including poor data availability, misaligned incentives, limited education on value, and the retrospective nature of outcome measures. Furthermore, the lack of standardisation in outcome measurement has hindered its adoption and economic viability on a global scale. As VBHC struggles to gain widespread acceptance, the advent of digital twins and personalised care signals a tipping point in healthcare innovation.
AI-driven digital twins, which provide rich, individualised data sets, can address the shortcomings of VBHC by delivering hyper-personalised health benefit packages and care interventions. This approach leverages AI to optimise resource allocation, ensuring that individuals receive the care they need, leading to better outcomes for both patients and healthcare systems.
It is important to note that VBHC has not been widely adopted, and healthcare systems are not necessarily losing out by considering alternative approaches. The introduction of hyper-personalised care packages doesn't need to be 100% adopted or successful, just better than the current status quo. The potential of AI-driven digital twins to enhance healthcare is undeniable, and their adoption could be phased to minimise disruption and maximise benefits.
At present, health benefit packages are typically designed at a national level, offering a uniform set of services to all citizens. With the advent of AI-driven digital twins, these packages could be personalised to cater to each individual's unique needs and circumstances. For example, a pilot might receive a higher allowance for optical care, while a person at risk of obesity and diabetes might be provided with a smartwatch, gym membership, and discounted access to healthier food.
The integration of AI-driven digital twins into healthcare goes beyond merely providing personalised care; it has the potential to create powerful networks that can transform the entire system. By connecting digital twins, healthcare providers can identify trends, patterns, and correlations that would otherwise remain hidden. This newfound knowledge will empower healthcare professionals to make more informed decisions, leading to better patient outcomes and more efficient use of resources.
Phasing the introduction of AI-driven digital twins could begin with volunteer groups from high-cost, high-risk populations, such as the frail elderly. As the technology proves its efficacy, regulations and safeguards could be established, and adoption could gradually expand, eventually becoming compulsory. This gradual approach, reminiscent of public health initiatives like seat belt laws and smoking bans, would ensure a smooth transition and widespread acceptance.
As we stand on the cusp of a new era in healthcare, the potential of AI-driven digital twins is undeniable. The shift away from VBHC towards a system rooted in personalised medicine holds the promise of a healthier, more equitable world. By embracing this innovative approach, we can tip the balance in favour of a brighter, healthier future for all, capturing the attention of policymakers and thought leaders alike.