Why the future of AI in Healthcare is Salutogenesis

For many years, private healthcare and technology companies have lauded Artificial Intelligence as the future of medicine.

With some even suggesting machines will lead the future of care. But with the risk of error still too high, and covid19 highlighting how much we need a human touch, is the future of Artificial Intelligence salutogenic rather than pathogenic?

In 1979 the medical sociologist Aron Antonovsky proposed an approach termed ‘salutogenesis,’ a theory of how and why certain people stay healthy. The word ‘salutogenesis’ comes from the Latin word salus, meaning health, and the Greek word genesis meaning origin. As a medical approach, it focuses on factors that support human health and well-being, rather than on factors that cause disease (pathogenesis).

More specifically, the salutogenic model is concerned with the relationship between health, stress, and coping. Now we find ourselves in a world where people are generating so much data about themselves, it’s time healthcare companies stopped focusing on the treatment of conditions and started looking at our data to help us make functional adjustments to our lifestyle to stop illness, rather than treat it.

One interesting observation from salutogenesis is that stress is ubiquitous, but not all individuals have adverse health outcomes in response to stress. Instead, some people achieve positive health despite their exposure to potentially disabling stress factors. What is it then that separates those that fall victim to the stress, and those that are able to control it?

Making sense of the factors that affect us, by creating a ‘Sense of Coherence (SOC)’ is a huge opportunity for primary healthcare. Coherence to any situation is central to successfully coping with challenges. Coherence means that the world is understandable, manageable, and meaningful. Giving all people that level of control over their data and outcomes using algorithms will be a huge win for humanity. If we direct the technology we create at identifying pathways and mechanisms leading to ways of coping with stress, we might really be able to reduce a person’s need for pathogenic treatment.

Salutogenic technology, therefore, implies a focus on health maintenance processes rather than disease processes; manage the cause, not the condition.

The Healthcare Continuum

With a newly identified sense of coherence, humans can make sense of the world, use the required resources to respond to it and feel that these responses are meaningful and make sense emotionally. A sense of coherence has three elements; comprehensibility, which is the cognitive element and relates to how the person sees the world, manageability, the instrumental element, and meaningfulness which refers to how the person is motivated to think and act.

If our healthcare sits at two ends of a continuum; health-ease and dis-ease, AI-powered Salutogenic technology can establish the processes that move people towards, or keep people at, the health-ease pole. Creating comprehensibility, manageability, and meaningfulness.

3 Use Cases for AI in salutogenesis

Using our data and AI to analyse, organise, sort, and provide insights is a good place to start. If we make data visible, individuals can use their data to understand negative behaviours and the effect it has on their future health. This approach could help with forecasting, predicting, and offering people help before they know they need it.

Productivity and Performance Enhancement

We all live busy lives, and we often sacrifice our long-term health for short-term gains. We are working long hours, eating sporadically, and exercising at irregular intervals. Taking control over those bad behaviours usually involve a lot of manual and repetitive interventions that distract you from what you believe are higher-value tasks such as paying the bills and delivering for your clients. It is now entirely plausible that AI systems can analyse your historical decision-making data to automate the tips and tricks that might nudge you to help prioritise cause over an eventual condition.

For example, if from wearables and phones we know your sleep patterns, we can start to help you get more restful sleep, or recommend products to improve or encourage better sleep.

Personalised Recommendations

Looking at marketing for inspiration, most consumers say they are willing to share personal information in exchange for personalised offers or discounts or to get product recommendations that meet their needs.

An efficient AI algorithm that creates personalised life-style adjustments can be beneficial for any one-to-one long-term health strategy. Salutogenic AI as a strategy focuses on individualised experience and makes recommendations based on patterns that could be deemed damaging to our longer-term health.

As a strategy, it could help people find what they need, even if they were not actively looking for it. In retail, for example, almost 35% of Amazon sales come from product recommendations. AI will only become better at recommending things because these algorithms learn from feedback data. So making sure people can collaborate with salutogenic algorithms by perhaps putting an interface such as chat over them, might be the key to highly successful, targeted health and life-stress adjustments.

Mental Health and Wellbeing

We choose our paths based on a series of decisions that often occur at certain life moments. Life itself can be mapped out as a series of Goals, Standards, and Preferences (GSP) much in the same way that some algorithms use multi-attribute utility theory and Bayesian probability mathematics to make decision-trees on our behalf.

Our mental health is often affected by the satisfaction one derives from various outcomes or situations; the things that make us happy or depressed. Life is multi-attribute, meaning that there are several attributes or values (hence, the use of a tree) that are considered when evaluating the utility of a specific outcome. The use of Bayesian mathematics allows a frequency distribution of the past choices of a person to define the relative importance of each branch of the value system to the future.

If we help people choose and set goals, or focus on near-term events that they would like to achieve, and use intelligent-decision-nudging in order to drive a behaviour, we have a genuine possibility of utilising AI to evaluate GSP and assess the utility of any potential action a person could take. For instance, if we learn from early decisions that a person is apathetic and short-term gratification-oriented, we could target language and nudges specifically for that personality type. In the future, with a wide enough range of patient behaviours, based ethically on actual patient histories, AI could try to derive important archetype values to each node in a GSP Tree for that personality type.

This type of technology has the potential to guide people through the outcomes in their decision making that could create complicated long-term mental health challenges.

Conclusion

The use of AI technology in healthcare has been so focused on analysing data, images and historical cases to support doctors’ treatment of conditions, we may have missed the most significant opportunity; helping future patients themselves with a sense of coherence, and understanding of the cause of many negative health outcomes.

By providIng personalised experiences that increase our understanding of what makes us stressed and sick, we empower people further upstream to prevent potential sickness  from occurring. Another positive outcome is that doctors and public health consultants might also get more useful information about the public, compared to traditional healthcare approaches. Which in turn creates a more collaborative healthcare experience.


About the Author

Pete Trainor is the bestselling author of “Human-Focused Digital”, and CEO of international healthcare provider Vala Health. Vala focuses on delivering primary care, using ethical applications of data-driven technology. He is on a mission to bring design, data, AI and technology together to empower humans to be happier and do more of what they are good at. He has a straightforward philosophy: Don’t do things better, do better things.

Featured image: ©Maksim Kabakou

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