In 2018, it was said that “Every 20 minutes, a new ML paper is born”. Two years on from this statement, AI has seen continued significant development in its accessibility and capabilities. Despite this, AI is yet to be utilised affordably and effectively in the workplace to help in an area that concerns all of us: mental health… Until now.
How is Our Mental Health?
Short answer: not great… According to mentalhealth.org.uk, approximately 1 in 6 people in the past week experienced a common mental health problem. There is one reason for this that may be quite obvious; the effects of an intensive working week. It has been found that approximately 50% of employees state they “always” or “often” return from work exhausted . This is going to have a big impact on mental wellbeing, considering a person is likely to spend around 13 years of their life at work.
It is no wonder that work is so stressful, especially today. Due to the economic effects of Covid-19, many companies have felt the need to reduce staff hours, resulting in those who are working having to work harder than usual to make up for the lack of staff and performing tasks that they wouldn’t usually have to perform, which adds extra stress. Some are overworking themselves because of increased competition with other employees, in fear that they may be one of the worse performing staff that could be losing their jobs in these hard times.
The Mental Health Impact on Employees and Employers
Even before the effects of Covid-19, how is it possible to monitor the mental health of employees? Some businesses use Human Resource Managers to take care of workers, but how can one person manage the mental health of so many employees, especially given the range of tasks they must perform? As a Human Resource Manager, you might have to:
- Oversee all aspects of the workforce’s development and management
- Ensure the company’s collaborative culture is appropriate for all
- Serve as the primary contact for all issues related to employee activity
- Balance between what’s good for both the organisation and the employee
- Look for people’s strengths and encourage personal growth
- Make people feel heard
- Be scrutinous in the search for potential emergencies
Human Resource Managers often don’t have the time to take care of all the issues that they come across. Without detecting mental health issues early on, there can be a range of issues that come about as a result:
- Reduced employee motivation
- Chronic pain and physical health issues
- Staff taking time off sick
- Staff quitting their job
There’s also a range of monetary issues that arise for the employer too. For example, in the UK, work-related mental health issues generate an estimated £35 billion in economic losses to the workforce per annum.
What if an employee with poor mental health IS referred to a GP, for example? Past studies have shown that some GPs struggle to identify some mental health issues due to a focus on physical issues or because they have insufficient time with patients [2, 3]. Furthermore, even if someone could detect poor mental health in someone who has been referred due to appearing to have a mental health disorder, wouldn’t it be best if the employee’s mental health disorder was prevented in the first place? This is a real challenge for companies to detect in their employees, so what if the solution were elsewhere? What about AI?
AI is Amazing
It has been said that Artificial Intelligence has the potential for infinite applications. There’re so many amazing things coming our way that 20 years ago sounded like science-fiction, from autonomous vehicles to personal assistants.
Infinite applications are no wonder given the scope of tools that are currently available through AI, which includes Natural Language Processing (NLP). NLP is capable of analysing text data, and in the majority of cases, can provide much more detailed analysis in comparison to a human, and a whole lot faster too! You’ve probably interacted with an AI that uses NLP. For example, most “agents” that appear on websites, which are the typical chat-supports that appear on webpages when you have a question to ask, are AI, not a person. They’re using NLP to identify what it is you’re asking, then find the information you want, and return you the information you’re looking for in a way you’ll easily understand.
Despite AI’s numerous capabilities, AI is yet to be utilised sufficiently to improve mental wellbeing in the workplace. This is where we come in!
How We’re Detecting Oncoming Mental Health Issues Early on at Work
Kaktus.AI is a platform that enables early prediction of mental health-related challenges in the workforce. KaktusBrain is the computing intelligence behind our model which uses AI and trained algorithms to identify early indicators of stress or mental illness and learns mental health triggers. Kaktus.AI will revolutionise in-work mental health support with a prediction diagnosis mechanism and early-stage intervention.
We identify triggers and behaviours through NLP methods, such as through smart segmented surveys, and create customised reports highlighting the mental wellbeing status for each employee/department. Kaktus.AI can provide prediction diagnoses for each individual, this means that we can indicate if an employee will be experiencing mental health issues before they become prominent and an issue.
Kaktus.AI provides a Digital Health (DH) platform that can ease the HR manager’s daily tasks & provide employee support. There are other DH platforms available, but we’d argue that none are as effective or as innovative as what we are planning to provide to corporates. Without us in the future, poor monitoring of employee mental health will continue to lead to a range of issues for employees and employers.
Who Are We?
Kaktus.AI is a start-up company based in the UK that is utilising artificial intelligence on a mission to improve mental wellbeing in workplaces. Our main focus is to innovate smart tools to monitor and predict mental wellbeing issues directly associated with workplace productivity.
- HARPER, A. and W. STRONGE, 2019. The Shorter Working Week: A Radical and Pragmatic Proposal.
- AUBÉ, D. et al., 2012. General practitioners’ management of mental disorders: A rewarding practice with considerable obstacles. BMC Fam Pract. 2012; 13: 19.
- AFANA, A. et al., 2002. The ability of general practitioners to detect mental disorders among primary care patients in a stressful environment: Gaza Strip. Journal of Public Health Medicine Vol. 24, No. 4, pp. 326–331