Incorporating artificial intelligence (AI) into mental health care holds promise for enhancing early detection, diagnosis, and treatment of mental health disorders. AI tools can enhance access to mental health services and alleviate the strain on healthcare systems. Nevertheless, the integration of AI in mental health care comes with significant challenges and requires a carefully considered approach.

The World Health Organization defines mental health as a state of psychological well-being that enables individuals to cope with daily life stresses, fulfill their capabilities, and contribute to their communities. Mental health includes not only our emotional and psychological health, but also our social well-being, and has a direct effect on how we think, feel, and relate to ourselves and to others. It is an essential component of the individual’s overall health and impacts our ability to make decisions, create relationships and shape the world we live in.  

As basic human rights, mental health and well-being are essential to individual and socioeconomic development and are one of the building blocks of the Sustainable Development Goals, deeply rooted in Target 3.4: reduce premature mortality from non-communicable diseases by one third and promote mental health and well-being by the year 2030. Consequently, mental health is more than merely the absence of illness. It is a complex and multifaceted aspect of the individual’s overall health involving various dimensions of well-being. While the concept of health is a unique and personal experience for each individual, some common factors influence our mental health such as genetics, life experiences, and social or environmental conditions. In essence, mental health extends beyond emotional and psychological elements to include social, behavioral, and individual adaptive aspects.

COVID-19: mental health comes to the spotlight

With the dawn of the COVID-19 pandemic, mental health quickly became an area of major concern for governments and public health authorities in Europe. The social restrictions brought by the pandemic increased overall psychological distress and expanded the onset of mental health illness to new populational segments, while aggravating the status of people already experiencing mental health conditions.

According to the OECD, before the pandemic approximately 84 million people across the EU (or 1 in 6 people) suffered from mental health illness. Anxiety and depression were the most common problems, afflicting an estimated 46 million people combined, followed by alcohol and drug use disorders, bipolar disorder, and schizophrenia. Despite this, mental health in the EU has considerably worsened since the pandemic’s early days. A Eurobarometer survey conducted in 2023 revealed that almost 1 in 2 people (or 46% of the total EU population) had experienced emotional or psychosocial problems, such as depression and anxiety in the previous 12 months.

While many studies have investigated the effects of the pandemic in the general population’s mental health, they mostly focus on comparisons between before and after restrictions were put in place. There is a lack of understanding of the short and long-term impact of the pandemic and its consequences in people’s mental health. Not much has been written about how COVID-19 affected different social groups, which economic and social inequalities were most likely to be linked to a decrease in mental health status, and the impact on vulnerable groups. Therefore, understanding the complexities and the interconnected factors which influenced people’s mental health during and after the pandemic may take years, and we are yet far from viewing the full picture.

Upcoming challenges

Mental health is an important component of overall health, and impacts not only life quality, but also people’s ability to participate in society, be professionally and economically active, and reach their full potential. The increase in healthcare costs and loss of productivity associated with a high prevalence of mental illness can have a substantial impact on economies and societies, posing a particularly worrying challenge.

According to the European Commission the economic burden of mental illness can increase to up to 4% of the EU gross domestic product annually, or approximately € 600 billion per year, primarily due to reduced productivity, healthcare costs, and social welfare expenditures. While significant, these numbers do not include indirect expenses (i.e., informal care from family members) or intangible costs such as the emotional distress and suffering experienced by individuals with mental health issues and their relatives.

Additionally, untreated, and undiagnosed mental health conditions directly contribute to an increase in healthcare costs due to more frequent hospitalizations, emergency care visits, and higher utilization of medical services. In some cases, untreated mental illness can result in higher social and public service costs, being associated with increased rates of homelessness and criminality, and substance abuse. Added to it, matters such as stigma and discrimination surrounding mental health can prevent individuals from seeking help, while limited access to services, influenced by financial restrictions and shortages of mental health professionals, restricts widespread support to individuals seeking help.

Furthermore, increasing public awareness and education, and addressing global inequalities are essential for promoting mental health care and preventing the outcome of illness. Integrating mental health into mainstream healthcare systems, developing effective prevention strategies, and recognizing early signs of mental health issues are complex tasks that will need to be addressed in the (very) near future.

Mental health: AI as a tool for prevention, promotion, and treatment

Undoubtedly, technology is reshaping our lives and the way we seek and receive care. Digital innovations have brought about significant changes across various aspects of healthcare, and artificial intelligence (AI) can have a tremendous impact in the prevention, promotion, and treatment of mental health conditions. AI can transform diagnostics, treatment planning, and personalized medical care. Machine learning algorithms have the capability to analyze vast amounts of data to identify patterns, predict the outcome of disease, and recommend person-centered interventions, significantly impacting the precision and efficiency of healthcare. AI technologies can facilitate early detection of mental health issues by analyzing data from various sources, providing timely interventions to individuals at risk. It can contribute to early detection and diagnosis by analyzing patterns in data, including language, social media activity, and sensor data from wearables, to identify the initial signs of mental health issues. Natural language processing further assists in the analysis of text or speech for feelings and linguistic markers associated with mental health conditions. In promotion, AI tools can enhance public awareness campaigns, disseminate accurate information, and help reduce the stigma surrounding mental health illness, creating a more supportive environment to individuals in need.

In the treatment of mental health illness, AI enables person-centered treatment plans by using machine learning algorithms to analyze vast datasets, tailoring interventions based on individual factors such as genetics, lifestyle, and treatment history. Virtual therapists and AI-powered chatbots can offer scalable solutions for delivering immediate support, direct to available resources, and provide coping strategies, ensuring accessibility to mental health care, and advancing evidence-based practices to enhance overall mental health care. Additionally, the application of AI and machine learning systems in mental health care can result in considerably more efficient and cost-effective health systems and lead to a significant reduction in total healthcare expenditures.

Despite previous assessments of digital health solutions and their benefits for patients and society, the specific economic implications of AI in healthcare have received sporadic attention. Incorporating AI into the EU healthcare have the potential to reshape health systems, have profound economic implications and contribute to overall economic growth. AI can be a valuable tool for promoting a more proactive mental health care and can not only improve an individual’s well-being, but also reduce the economic burden associated with mental illness by reducing healthcare costs and improving workforce productivity. In summary, the integration of AI into mental health care has the potential to improve the early detection, diagnosis, and treatment of mental health conditions. AI tools can increase accessibility to mental health services, while at the same time reducing the burden on healthcare resources, with better workforce productivity, diminished absenteeism, and the prospect of long-term cost savings by addressing mental health concerns at an early stage.  

Considerations for implementation

Implementing AI in mental health care presents promising opportunities but also significant challenges and will require a thoughtful approach. Ethical considerations, such as data privacy, consent, and potential biases, need to be prioritized. Safeguarding sensitive mental health data is imperative, and guaranteeing the accuracy of AI algorithms to avoid errors in diagnosis and treatment is of the utmost importance. Addressing disparities in access, gaining user trust, and establishing regulatory frameworks will require careful and meticulous work and will not be completed overnight. Challenges like cultural sensitivity, algorithm interpretability, and integration of existing information technology systems must be addressed. Data collection and reporting discrepancies among healthcare systems also pose challenges, and harmonizing data practices will be crucial for an efficient implementation of AI systems in mental healthcare.

Despite the challenges associated with implementing AI in mental health care, the medical, economic, and social benefits far outweigh the challenges to be overcome and can significantly improve the quality and accessibility of mental health services. Summarizing, the successful implementation of AI in mental health care requires a multidisciplinary approach, combining technical expertise, understanding of its clinical applications, and ethical proficiency. Such collaborative effort will involve IT security and data governance specialists, public health professionals and user experience designers, working together to bridge the gap between technological capabilities and healthcare requirements. Last but not least, for a successful implementation of AI in mental health care, a deep commitment to continuous learning, adaptability, and a patient-centered approach, will be essential.

The BearingPoint way

As we move forward towards the future, BearingPoint leads the way. We are constantly imagining, designing, creating, and integrating new and sustainable ways of working and finding solutions to both new and old social challenges. We are a team of policy experts, public health professionals, digital transformation specialists, project managers, advisors, and industry leaders. We embody wide-ranging professional expertise, and our competences are almost as diverse as the places we are from and carry our work in. At BearingPoint we take pride in promoting the collaboration between individuals, government, and industry. We bring together insights and perspectives to find solutions from unseen angles and unite cutting-edge technology to people’s well-being. We are a driving force in creating opportunities and in delivering positive change into the lives of millions. At BearingPoint, we are driven by the challenges we face, our commitment to promote social advancement and above all else, as we navigate into the future, we understand that together we are more than business.

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