Using Emotional AI to Improve Healthcare Outcomes

Stress is a normal human experience; it can fuel your brain, boost your alertness and memory, even motivate you to perform better. But, what happens when there is too much of it?

The stressors of the outside world are limitless. Today, our brains are overwhelmed with stress from work, school, family, and undeniably COVID-19. Stress is even normalized as the baseline for successful individuals, constantly pushing themselves to grow. So much so, the American Psychology Association reported in 2021 that 84% of Americans experienced strong emotions correlating to prolonged stress over the 2 weeks post-inauguration. Of which, at least 33% were categorized as extreme stress. Estimates show that employers in America spend over $300 billion annually on stress-related health care and lost workdays, a figure which has likely skyrocketed in the times of COVID-19.

Prolonged Stress throws the Body and the Brain off-balance.

It causes permanent structural and functional changes in the brain, which can start a domino effect impairing other functionally connected brain regions. Leading to the symptomatic development of depression, anxiety, and burnout.

Chronic stress is also implicated in the accelerated development of many physiological diseases, including cardiovascular dysfunctions, diabetes, cancer, and autoimmune syndromes.

Chronic stress, a double-edged sword for health care outcomes.

As it stands, chronic stress is one of the largest contributors to disease development and progression, leading directly to increases in illness rates and hospital admissions. With hospitals at or approaching capacity, due to COVID-19, the second pain point of the stress cycle is strained - healthcare provider (HCP) stress.

HCP stress harms more than their well-being, it is consistent with increased rates of medical errors and compassion fatigue, significantly reducing the patients’ quality of care (QOC). Inherently, reduced QOC leads to even greater stress for the patient and higher rates of hospital readmissions. In doing so, further increasing the stress burden on HCPs and contributing to long-term effects on the healthcare system.

Thankfully, stress is treatable without medical intervention if recognized in time. The severity of the psychological component of stress is directly dependent on an individual’s perception of its predictability and controllability. However, chronically stressed individuals struggle with this due to avoidance mechanisms. Avoiding your negative emotional state can be helpful in the short run, however, in the long run, avoidance behaviours lead to decreased self-awareness of stress level, further perpetuating chronic stress.

Using Heartbeat Emotional AI to Break the Stress Cycle

With the assistance of stress monitoring AI technology, we can break this cycle by increasing people’s self-awareness of their stress level by measuring the physiological and emotional correlates of stress.

Heartbeat AI can measure the deep emotional signature of stress through the integration of Heartbeat Touchpoints into the routine of patients and HCPs. Between speech to text and tone of voice analysis, these touchpoints will acquire rich human experience data, correlating to stress, from regularly scheduled voice notes. Heartbeat AI can use this qualitative data collected over time to develop a quantifiable and personal stress signature.

For patients, Heartbeat Touchpoints would enable real-time stress-monitoring based on their experience with recovery challenges. Identification of critical patient stress levels can be used by HCPs, to suggest personalized behavioral and treatment-related interventions preventing further progression.

HCP touchpoints would facilitate a better awareness of critical stress levels and suggest appropriate behavioral or operational interventions to mitigate them. With the long-term goal of reducing HCP stress levels and providing more insight towards addressing issues related to the reduction of patient QOC.

Coupled with advancements in biometric analysis and physiological stress-monitoring AI, stress can be predicted and controlled reliably in breaking the cycle. Not only is this applicable to hospitals, but highly accurate stress detection has the potential to significantly improve psychotherapy outcomes and revolutionize our understanding of work and school-related stress.