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Analytics’ Role In Mental Health and Wellbeing Programs

By Steve Cavolick

How’s everyone doing today?

If you’re feeling stressed, anxious, or depressed, you’re not alone.  A recent study of global workers showed that 42% of those surveyed experienced a decline in mental health since the pandemic started. It’s no wonder: the pandemic brought us radical change to business norms and rules for socializing, separation from loved ones, endless virtual meetings, and the feeling that connectivity to work was demanded 24/7, all of which contributed mightily to a decline in mental health.

Mental health issues manifest themselves in the workplace through absenteeism, presenteeism (not being fully functional at work due to injury, illness, or other condition), and lower productivity.  In fact, the World Health Organization (WHO) estimates that anxiety and depression cost the global economy more than $1 trillion per year in lost productivity. 

Eliminating harassment and bullying in the workplace is a good start to improving mental health of workers, but also having structured wellbeing programs in place is beneficial.  The same WHO study mentioned above also reports that for every $1 spent on programs to prevent and treat common mental disorders, there is a return of $4 in improved health and productivity.

Knowing which programs to implement and which employee populations to target can be enhanced through analytics.  Let’s examine some of the ways digital solutions and data are being used bring assistance to those who require it: 

  • Data Collection: There are many indicators of mental disorders which can be recorded through a variety of wearable devices and apps. Smart watches, phones, and other wearables can track physiological changes to heart rate, temperature, and electrodermal conditions due to excessive sweating.  The same devices could record eye movements and facial expressions, both of which are known indicators of mental conditions.  Apps could even ask employees to share their emotional state and use voice recognition algorithms to detect changes in frequency or patterns of speech. 
  • Analysis: AI can make decisions about next best steps for a given employee based on many types of data. For example, if an employee consistently reports feeling “down” or angry, the application can recommend time off or direct them to a therapist.  The more data that a person opts in to share means faster detection and action occurs.  It’s easy to imagine an AI-driven app that uses physiological symptoms of a person, but also takes physical activity and social media involvement (or lack thereof) into assessing the mental wellness of a person and recommending appropriate action.
  • Wellness Programs: They could begin with chatbots and evolve to in-person sessions with therapists depending on the severity of the diagnosis. Using data from a plethora of sources, programs could also make recommendations on sleep, nutrition, physical training, and even teach meditation techniques.

The benefits of positive mental health to individuals and economic productivity are clear.  There may be fear from individuals that privacy will not be protected, but these apps would be deployed so that employers would only see anonymous and aggregated data.  In this way, individuals’ privacy is protected and employers can look for patterns of stress in particular departments or category of worker in the workplace.  This approach benefits both individuals and companies at an enterprise-level.  

If you are ready to build AI applications to help with mental wellbeing and human capital management, our data science team can help. If you’re not quite there yet, the LRS Big Data and Analytics group has over 20 years of experience implementing applications in advanced analytics, information management, and data warehousing.  Not sure how to get started?  Our strategic offerings can help you align business and technology teams, discover the right use case, and determine an ROI.  If you are interested in understanding how we can help you find value in your data, please fill out the form below to request a meeting.

About the author

Steve Cavolick is a Senior Solution Architect with LRS IT Solutions. With over 20 years of experience in enterprise business analytics and information management, Steve is 100% focused on helping customers find value in their data to drive better business outcomes. Using technologies from best-of-breed vendors, he has created solutions for the retail, telco, manufacturing, distribution, financial services, gaming, and insurance industries.