By Dr. Rafael Bengoa:
After the Covid-19 crisis, healthcare in Europe is being reconfigured. That momentum has already started. These policy initiatives should not be strategic policy level exercises separate from the strategic changes related to the technology agenda, writes Rafael Bengoa, an expert in management and public health, who kindly accepted our invitation as a guest columnist.
It is well known that all European countries follow a model focused on rescue in the management of acute episodes. This model addresses care for acute diseases with quality, but it must be complemented with a care model focused on chronicity, which is responsible for 90 percent of deaths in Europe and for 70 percent of the demand in healthcare. This occurs both in Bismark-type insurance assistance models (Germany and France, Belgium) and in tax-based NHS-type models (Spain, UK and Italy).
Simultaneously to this strategic redefinition of healthcare, artificial intelligence (AI) emerges showing great potential. So it is convenient to understand the complementary potential of the technology to achieve these changes, as well as to understand how the process of decision-making and investment across the EU can weave AI into the changes that will happen in these next few years.
In Europe, best practice examples for the use of AI in healthcare can be identified, but in general, the services are not yet oriented to use AI to proactively detect health or social problems. In general, health systems are more passive than proactive. They are frequently faced with the need to react to medical crises in patients whose pathologies may have been effectively managed initially, but whose therapeutic regimes no longer function optimally.
Data management powered by AI would allow for a proactive approach in the early detection of pathologies when patients are home, before they arrive to the emergency room and occupy a hospital bed. All European systems share the challenge of transitioning towards another, more integrated and proactive model, providing clinical services in an innovative way and, in many cases, turning the home into a delivery hub.
AI can be a tremendous ally in this effort to detect problems early. AI tools are already being used to monitor patients in home care models, and within healthcare infrastructures, they enrich diagnostic imaging tests in screening for the most common cancers, such as breast or lung cancer. In this decade, all countries will tend to shift their system from reactive to proactive, by identifying situations before they become crises.
The transformative effect of AI and the productivity paradox
It is necessary to use the potential of AI to transform care models and not to root the current models. This approach will be particularly important given the arrival of post-pandemic recovery funds in Europe. It is a strategic moment that will not happen again.
Many of these funds will have to be directed to AI projects in the healthcare sector and many decision-makers will demand more evidence for such investments in AI. In this context, it is worth noting the “productivity paradox”.
The expert panel – The Watcher Review, 2016. UK – indicated that, although it is natural to ask for a short-term return on investment for investments in information technology, experience shows that this short-term return comes more in the form of quality improvements and clinical safety than in terms of raw financial returns.
In fact, investment savings can only be seen ten years later because of what is called the “productivity paradox” – the observation that, as more investment is made in information technology, worker productivity may go down instead of up.
This has become evident in other sectors of the economy, in which the digital push has been key to rise to the challenges of the 21st century. This strategic effort does not imply that all the investment needs to come from the public sector, but rather via the identification of public and private partnerships that could add value to these policies.
