In recent years, mental health crises and suicide rates have surged across America. This concerning trend spans demographics, with 11.5% of youth (over 2.7 million people) reporting severe major depression and 20.78% of adults (over 50 million people) facing some form of mental illness. The aftermath of COVID-19 continues to take its toll, with lasting impacts on mental health and well-being. The Stress in America™ 2023 survey conducted by The Harris Poll on behalf of the American Psychological Association (APA) found that chronic health conditions among adults aged 35 to 44 increased from 48% in 2019 to 58% in 2023–following a rise in mental health diagnoses in this group, from 31% in 2019 to 45% in 2023.
Given that suicide is a leading cause of death globally, claiming over 700,000 lives annually, the urgency for advanced healthcare solutions to predict and prevent mental health crises has never been greater.
The Role of Big Data in Mental Health Crisis Prevention
Big data analytics offers a transformative solution for predicting and preventing mental health crises at scale. While commonly applied in physical health, the transformative impact of big data within behavioral health has only recently started to gain momentum. By implementing the tools, processes, and infrastructure to collect and analyze rich demographic and behavioral health outcome data sets, we can better support our population and reduce the number, impact, and cost of mental health crises across the US.
The Foundation: Measurement-Based Care (MBC)
While physical healthcare is driven by data–including diagnoses, clinical progress evaluation, patient engagement, and treatment adjustments–behavioral health has historically lagged behind. However, with the accelerating adoption of Measurement-Based Care (MBC), there is now a solid and growing foundation for a data-driven behavioral health system. MBC uses patient-reported outcome measures (PROMs) to monitor progress and inform care decisions throughout treatment. Research shows that MBC leads to improved clinical outcomes and deeper patient engagement and enables providers to proactively identify and respond to off-track results.
At the individual level, MBC ensures effective and high-quality care. At the health system level, it generates data that can be used to predict and prevent crises on a larger scale. Scaling MBC across health systems unlocks powerful datasets to reveal population trends, address service gaps, and respond to patient deterioration or crises before they happen.
Scaling MBC: Data-Driven Insights for System-Wide Impact
Scaling MBC across health systems enables key data-informed insights that are critical for the continual enhancement of behavioral health services:
- Benchmarking: With large-scale data, we can set and assess standards for care effectiveness across any demographic, program, or treatment modality. For individuals, this empowers an improved quality of care and the early identification of those at risk of crisis. Benchmarks also serve as an effective avenue for value-based care contracting, where providers are incentivized to engage in care processes that are known to facilitate high-quality care and enhanced clinical outcomes. Benchmarks are also useful beyond clinical outcomes, as they can be leveraged to fine-tune the process of care and ensure that mechanisms of change that will enhance clinical effectiveness are being leveraged in practice.
- Treatment Planning: Big data allows for more personalized care. By automatically analyzing demographics, acuity of challenges, and individual experiences, providers are empowered by predictive models that show which treatments are most likely to be effective for any individual. This minimizes reliance on trial-and-error approaches, helps direct people to the most effective care option for their needs, and offers an early indication as to when treatment may need to be adjusted.
- Understanding Demographic Needs: Big data enables a deeper understanding of how treatment disparities affect various populations, allowing for the development of policies and services that better address gaps in care quality and ensure equitable care is prioritized throughout behavioral health services.
Proven in Practice: The NHS Talking Therapies Program
The NHS Talking Therapies Program (formerly IAPT) is a leading example of large-scale data collection leveraged throughout behavioral health treatment. Since its 2008 launch, it has used session-by-session outcome monitoring to improve access and quality of care, raising clinical recovery from 38% to 52%.
The program now collects self-report measures from 98% of its service users across all of the UK, generating a rich dataset that drives continual improvements in care quality. It has also demonstrated the financial ROI of leveraging big data to improve care quality; for every £1 invested, the NHS gains £4 through reduced healthcare costs, reduced work-related benefits, and increased tax revenues. The program’s commitment to data transparency has enabled international replication of its approach, where MBC serves as the foundation for service delivery. In Norway, for example, their IAPT-style program had a benefit-to-cost ratio of 3.6 and resulted in recovery rates twice as high as standard care.
The Future: Big Data, AI, and Predictive Analytics in Behavioral Health
The future of behavioral health care lies at the intersection of big data, AI, and predictive analytics. By harnessing these tools, clinicians can access actionable insights that help them identify subtle or significant symptom changes in their patients, allowing for timely treatment adjustments, optimized care pathways, and improved outcomes.
Expanding Data Sets for Deeper Insights
In addition to patient-reported outcomes (PROMs), behavioral health systems can integrate other metrics—such as wait times, number of sessions, demographics, insurance coverage data, and natural language processing analysis—to build a more comprehensive picture of effectiveness and areas of improvement in behavioral health care.
As an example, the NHS Talking Therapies program was able to examine their outcome data and uncover that assigning additional measures specific to a patient’s unique challenges would contribute to an increase in the number of sessions within a client’s treatment plan and higher recovery rates among these individuals. Even when controlling for the number of sessions, each hour of therapy was shown to be more effective at improving outcomes when additional, more precise measures were leveraged.
By leveraging these data elements, we can substantially enhance access to high-quality care and ensure patients receive the most appropriate and effective services for their needs.
Population Health: Proactive Crisis Prevention
Population-level data provides a powerful tool for understanding overall mental wellness and developing preventive, upstream care strategies. Population health management systems with self-guided resources and mental health tracking empower case management teams to leverage predictive models to identify individuals at risk. With proactive intervention, they can avoid further deterioration of those individuals’ mental health symptoms as well as the associated high costs of care.
Given that in the US, there are 350 individuals for every one behavioral health provider, and millions of people are living in regions with behavioral health workforce shortages, self-guided population health care models are imperative to ensuring resources are allocated based on need and that everyone has timely access to the most appropriate levels of care.
A Call to Action: The System-Wide Benefits of Big Data Analytics
Behavioral health systems powered by big data analytics can function more efficiently and deliver vast enhancements to the overall quality of care available to any population. Improved clinical outcomes, early identification of at-risk individuals, and the ability to understand which treatments work best based on symptom acuity and demographics will lead to a healthier population.
The financial benefits are equally significant, with reduced healthcare costs, fewer hospitalizations, and increased productivity resulting from improved behavioral health outcomes. The enhancements fuelled by big data analytics enable us to build health systems that invest strategically and can better reduce mental health crises and disparities across the US.
The behavioral health industry is at a turning point; we need to continue driving MBC education and implementation, equipping behavioral health providers and leaders with the tools to better understand their patients and meaningfully improve care. Only then can health systems harness the full potential of big data, MBC, and AI in order to reshape the future of behavioral health and build a system where we deliver proactive, personalized care that not only prevents crises but also builds a healthier, more resilient population.
Jeremy Weisz is the CEO and Co-Founder of Greenspace Health, North America’s leading Measurement-Based Care technology provider that supports more than 500 clinics, hospitals, and health systems. To learn more about Measurement-Based Care and its impact on behavioral health systems, visit greenspacehealth.com or schedule a call with an implementation expert.
