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Emotional AI: Driving deeper connections for patients in digital healthcare

By Sarita Wilkinson – Principal – Design Insight at PDD

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Emotional AI in healthcare

In recent years, there has been a significant growth in investment in healthcare-driven AI, in response to the accelerated demands for remote healthcare systems.

We are seeing an increase in development of monitoring apps, digital triage systems and home-care devices aimed at providing healthcare support to patients and shifting some aspects of monitoring and treatment away from clinical settings.

Software, in the form of mobile health (mHealth) apps, is more often being prescribed by clinicians as a supportive counterpart, and in a few cases, as an alternative to drugs.

Yet, while these apps pose many opportunities to enhance the patient experience, significant challenges around high patient dropout rates still remain.

Addressing this challenge of retention, mHealth apps have gone some way to increase patient engagement with push notifications, elements of gamification and connection to support groups or healthcare professionals.

However, with even more emphasis now on digital healthcare systems to aid self-care and self-management, we ask: How might we facilitate a deeper level of connection for patients with digital healthcare systems to encourage better adherence and lasting, positive behavioural change?

Emotion and Function

A starting point perhaps would be to consider the patient in a holistic manner, placing their needs at the centre of solutions to create adapting approaches to healthcare that take into account not just the physiological aspects of a condition, but also the psychological effects that a condition can bring… fear, uncertainty, doubt, anxiety, apprehension, desperation, relief, hope.

While undoubtedly digital health solutions need to focus on functionality, be straightforward to use, and instil confidence in the patient; they can often lack a ‘human’ quality, which is where real connections happen.

The emotional burden of health conditions heightens the need for empathy, understanding, patience, pace, contextual awareness, and demands a responsiveness that is personal to an individual’s situation.

Driving more natural and authentic Human-Machine Interactions (HMI), Emotional AI is a branch of Artificial Intelligence (AI) that can recognise, interpret, and respond to more emotive channels of human communication.

It uses AI to detect both verbal and non-verbal signals, picking up on anything from voice inflections to facial expressions, essentially creating a series of behavioural biomarkers.

The use of Emotional AI is starting to emerge across a wide range of industry sectors, from smart home systems to automotive, digital marketing to retail, and even in the financial sector.

The aim of all these applications is to create a deeper connection with users through systems that can anticipate needs, and automatically adapt their outputs based on even the most subtle of human behavioural cues, without the need for excessive data input by the user.

The evolving personality of AI

With AI’s application in the NHS so far relatively limited, there are some inspiring examples that could be a positive sign for the future.

An example is HeartFlow’s AI technology, which is a system that analyses CT scans of patients who are suspected of having coronary heart disease and then creates a personalised 3D model of the heart that shows how blood is flowing around it.

This helps doctors spot where blood flow is disrupted by blockages.

Whilst this early adoption is promising, when we look at how mHealth apps and AI could further impact the healthcare industry, there’s a lot further we can go, especially when we prioritise emotion.

Emotional AI in healthcare

Sarita Wilkinson

An interesting scenario to consider is what if mHealth apps had the ability to become more like the patient by taking an empathetic approach; proving encouragement, support and advice that is personal to the individual’s behavioural and emotional patterns over the course of a treatment regimen?

For instance, could a system understand the level of discomfort of a patient self-injecting from facial expressions, and suggest an alternative technique for next time?

Or could a system detect if a patient wasn’t able to keep up with the pace of video instructions for a medical device through their real-time actions, and automatically slow down or pause the playback?

Responding to user context and condition

As technologies continue to evolve, we are seeing the importance of context awareness in AI, through proactive systems that can detect not only the physical conditioner of the user, but also the environmental conditions and respond accordingly.

An industry leading in this area is automotive, which takes a holistic approach to monitoring, by combining biological markers, behavioural biomarkers, and context awareness.

The healthcare industry could learn a lot from this sector, with the potential for digital healthcare applications and devices to respond to or anticipate the needs of the patient based on a combination of real-time context and state of their condition, creating more tailored and immediate responses.

Additionally, could a system automatically switch between audio and haptic reminders, or alerts based on the ambient noise of the patient’s environment?

Or could a system offer tailored supportive and motivational advice for patients who suffer from anxiety disorders when out in public spaces?

Capturing and casting human qualities and attributes

Drawing back to the all-important area of empathy for this last example, it’s worth taking a moment to consider the future of chatbots within the healthcare context.

For many, chatbots can be a challenge at the best of times, often creating annoyance and frustration; and for a patient, it’s hard to get any sense of empathy through computer-generated text based conversations even with a photo of a person hovering in view.

Empathy requires a far deeper sensory approach that relies on subtle verbal and non-verbal cues from both parties, to create a connection and a sense of shared feeling.

Perhaps the notion AI with ‘true empathy’ is still a thing best left for the realms of science fiction, for many it encroaches uncomfortably on what defines us as humans.

Over the last few years, companies in other sectors have continued to explore the potential of avatars to expand their service offering, creating ‘digital-twins’ of real personnel in an attempt to create deeper connections with their customers.

With this, could mHealth apps feature hyper-realistic Healthcare Professional (HCP) avatars based on real healthcare providers, to help guide patients through their treatment regimens and bridge the gap between physical and digital healthcare experiences?

Could we assign to patients a HCP-avatar of someone from their real-life healthcare team, to create a more personalised and joined-up experience?

Or could we use HCP-avatars to enhance digital mental health services and apps to make them less intimidating and more ‘human’?

Greater than the sum of its parts

By taking a wider view of technologies and applications of Emotional AI across sectors, we can learn and leverage the opportunities around behaviour change.

By pairing those insights with a deeper understanding of behavioural science, we can also explore numerous opportunities to improve patient adherence in the future of digital healthcare.

But ultimately, if the development of healthcare AI is to facilitate a deeper level of connection for patients through digital healthcare systems, a far more holistic approach is needed.

Using Human-Centred Design (HCD) methodology within the development of digital healthcare, allows patient to be put front and centre of innovation.

HCD helps to uncovering the unmet needs, desires, and challenges of patients; fuelling the development of solutions that resonate deeply with people and ensuring that digital applications address the wider needs of patients on their treatment journey.

Sarita Wilkinson is Principal of Design Insight at PDD

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