Biobeats

The impact of mental health disorders is felt globally, with 1 billion people suffering from conditions such as anxiety, depression, schizophrenia, bipolar disorder, post-traumatic stress disorder, eating disorders, attention deficit hyperactivity disorder, obsessive-compulsive disorder, borderline personality disorder, addictions, phobias, and self-harm conditions.

These conditions are the most significant cause of years of life lost due to ill health, disability or early death — higher than both cancer and cardiovascular disease.

As well as clearly being a bad thing in and of itself, the cost of mental health disorders on our economy is enormous and growing fast — projected to surge to $6 trillion by 2030, up from $2.6 trillion in 2010 — making mental disorders the most crucial cause of productivity loss among non-communicable diseases.

Beyond direct reimbursement costs, mental health challenges lead to increased absenteeism, reduced presenteeism, and in general huge effects on productivity. Given this rising burden, corporations are now seeking solutions to address this challenge and improve worker well-being.

But solving these challenges — a critical step to enabling the deployment of mental health interventions — requires gaining an unprecedented understanding of the interconnectedness of biology, psychology, neurology, and behavior that goes beyond the understanding that we have today – a significant technical and scientific challenge.

We were introduced to David and his team by the team at Oxford Sciences Innovation.

While Biobeats was only confronting their approach to the market, we were struck by David’s personal connection to burnout and mental health issues and his dedication to using the bleeding edge of science to improve outcomes.

Biobeats operated at the intersection of experimental psychology, biomedical engineering, neuroscience and machine learning. Its core value proposition was its focus on detection. They built a suite of proprietary machine learning algorithms that used neurological, physiological, contextual and psychometric data to understand what triggers stress at a deeply biological level. The system then imputed those stress profiles through an “Intervention Engine” to determine recommended interventions to support a user’s specific strategies to manage their stress.

The company’s focus on measurement and insight gave them the ability to help corporations and HR departments a) see the relationships between poor mental health in a workers' population and the impact on a company’s business (through indicators like churn or increases in productivity), b) extract insight into the complex web of potential causes and consequences of mental health disorders and identify potential issues, and c) measure the impact of any particular intervention in real-time, thereby providing a good proxy for return on investment that can guide future investment decisions.