Real-World Data Delivers Dose of Hope for Mental Health in the U.S.

Brand Insights - Thought Leadership | Paid Program

If you were asked which health care conditions rank as the highest in both unmet need and in cost, which would you choose? Would it be cancer or heart disease?

While those conditions certainly place in both categories, mental health outranks them in unmet need and annual health spending.Issues related to mental health also outrank both cancer and heart disease with respect to poor access to effective treatments, social stigma, and limited insurance coverage of higher priced treatments.And as a comorbid condition, depression and other mental health problems significantly drive-up costs and complicate the treatment of other chronic physical health ailments.1

Exacerbated by the pandemic, mental health disorders now constitute a national crisis. For example, prior to the pandemic, most estimates suggested that one out of every five Americans reported symptoms of depression. Those estimates now are closer to one in two.2 And the costs keep rising.In 2019, mental health care spending for prescription treatments and therapy reached $225 billion, and that doesn’t account for losses related to reduced workplace productivity and the lack of participation that comes from a lower quality of life for those affected.3

Hope for people suffering from mental health problems comes from the growing focus on new research and development programs to advance treatments for major depression, schizophrenia, bipolar disorders, and substance use. Also, there is genuine enthusiasm for novel compounds in the battle against treatment resistant depression, such as ketamine and psilocybin. Yet, despite increased early-stage investment for the clinical development of new treatments in mental health, high quality insights from research to enable market adoption, broaden payer coverage, and personalize care are lacking or have been difficult to attain. At the same time, awareness is growing related to the high costs, slow speed, and limited utility of randomized controlled trials as the sole -basis for developing treatment guidelines.4

High quality real-world data (RWD) for mental health can help. As defined by the U.S. Food and Drug Administration (FDA), RWD “are the data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources.”5  Examples of RWD include electronic health records, insurance claims, laboratory results, and patient-reported outcomes. Social determinants of health data are also RWD and are increasingly growing in importance for our understanding of racial and other disparities in health utilization and outcomes. When linked together in a standardized and cloud-based architecture, RWD can be accessed to provide insights into gaps in care and offer a powerful aid to help tackle the many challenges in research, treatment, and care delivery for the millions of Americans that suffer from mental illness.

The Outcome Measures Framework, led by the U.S. Agency for Healthcare Research and Quality (AHRQ), is one initiative focused on standardizing outcomes measurement in both clinical and research settings, including patient registries that are an important source of RWD. In many condition areas, including mental health, there is significant variation in outcome measures making it challenging to compare, aggregate and use data. Major depressive disorder is one of the first condition areas addressed in this initiative.The primary outcomes addressed by expert consensus resulted in recommendations for how to measure clinical response, adverse events, resource use, and survival for depressed patients.As more large integrated health systems, like the Harvard Medical School teaching affiliate Mass General Brigham in Boston, adopt these patient-related outcomes in the Department of Psychiatry and beyond, even more high-quality data for mental health will be available over time.7

But access to high quality data is just the first step.Being able to analyze and apply data science and artificial intelligence tools to both structured and unstructured data enables the transition from real-world data to real-world evidence. Automated extractions, natural medical language processing, and machine learning tools when applied thoughtfully to structured and unstructured data can fill the gaps in RWD and provide insights not found elsewhere – including identifying treatment resistance, reasons for stopping or switching medications, and how quality of life factors impact care. RWD with clinical depth and nuance and the evidence they lead to are critically needed in mental health to help personalize care, understand disease progression, leverage patient characteristics, identify condition subtypes, and analyze the costs associated with clinical outcomes of new and existing treatments.

At OM1, we are proud to announce the release of our new Mental Health Network with the addition of more than 3 million patients with mental health disorders followed longitudinally by more than 9,000 specialists in 2,000 clinics across all 50 states. The OM1 Mental Health Network includes deep clinical data with specialist notes, medication lists, social determinants, claims data, and patient reported outcomes.

This type of real-world data network can provide greater accessibility to these important variables for mental health and are available to partners to leverage for:

  • Informing key stakeholders about how a treatment impacts patients’ lives outside the narrow confines of controlled clinical trials;
  • Accelerating the drug approval process with decision support, strategic insights, and data that can shorten the time to bring new innovations and advances to market;
  • Exploring the effectiveness and safety of medications on a more diverse patient population over longer time periods that cannot be assessed in randomized controlled trials;
  • Providing valuable information to payers to understand the effectiveness of medications and whether they improve outcomes in specific patient populations.

High quality RWD when combined with the tools of data science and artificial intelligence generate insights that are already having a huge impact in multiple chronic condition areas, like cancer and heart disease. Mental health is finally getting its moment to shine with accessible, high-quality data to advance the next generation of research, development, and ultimately personalized care for a range of psychiatric conditions that affect far too many Americans.

References

  1. Sporinova B, et. al., “Association of Mental Health Disorders With Health Care Utilization and Costs Among Adults With Chronic Disease,” JAMA Network Open, 2, vol. 8 (2019):e199910, https://doi.org/10.1001/jamanetworkopen.2019.9910
  2. Ettman CK, et. al., “Prevalence of Depression Symptoms in US Adults Before and During the COVID-19 Pandemic,” JAMA Network Open, 3, vol. 9 (2020):e2019686, https://doi.org/10.1001/jamanetworkopen.2020.19686
  3. Leonhardt M, “What You Need to Know About the Cost and Accessibility of Mental Health in America,” CNBC Health and Wellness, May 10, 2021, https://www.cnbc.com/2021/05/10/cost-and-accessibility-of-mental-health-care-in-america.html
  4. Mulder R, et. al., “The Limitations of Using Randomised Controlled Trials as a Basis for Developing Treatment Guidelines,” Evidence-Based Mental Health, 21 (2018):4-6, http://dx.doi.org/10.1136/eb-2017-102701
  5. US Food & Drug Administration, “Real-World Evidence,” Science and Research Special Topics, 2022, https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence
  6. Gliklich RE, et. al., “Harmonized Outcome Measures for Use in Depression Patient Registries and Clinical Practice,” Annals of Internal Medicine, 172, vol. 12 (2020):803-809, https://doi.org/10.7326/M19-3818
  7. Advisory Board, “How Mass General Brigham Uses ePROs to Improve the Patient Encounter,” Advisory Board Case Study, February 17, 2022, https://www.advisory.com/sponsored/mass-general-brigham-epros