Advancing Mental Health Research and Care Using Real World Data

Mental health disorders are a leading cause of global disability, yet significant gaps remain in understanding the real-world effectiveness of treatments, alone or combined, such as pharmacotherapy, pharmacogenomic testing, psychotherapy (e.g., cognitive behavioral therapy), and community-level support. Traditional clinical trials, while essential, often operate under controlled conditions that may not fully capture the complexities of everyday clinical practice and patient navigation in the pursuit of care or support. Real-world data (RWD), including electronic health records, insurance claims, consumer data, local public survey data, and patient registries, offer an opportunity to study treatment effectiveness amid these complexities. Advances in data analytics and large language models have further enhanced the ability to harness RWD for robust, large-scale analyses. This special research topic aims to explore innovative applications of RWD assessment to evaluate treatment effectiveness, optimize care delivery, and inform evidence-based decision-making, ultimately improving mental health services research and patient outcomes.

Despite the growing availability of RWD, its potential to generate real-world evidence (RWE) for mental health services remains underutilized. Mental health care often involves individualized treatment pathways, making it challenging to systematically study treatment adherence, symptom severity, functional outcomes, and long-term impacts. Inconsistent tracking of these outcomes in RWD sources presents challenges for evaluating treatment effectiveness. This research topic seeks to address these gaps by promoting the use of advanced methodologies, such as target trial emulation and causal inference techniques, to derive robust insights and strengthen causal conclusions from RWD. Additionally, advanced analytics, including the application of large language models, offers new opportunities to extract meaningful patterns and enhance the granularity of RWD analyses. By integrating these innovative approaches, researchers can bridge the gap between controlled clinical trials and real-world practice, enabling evidence-based decision-making that optimizes patient outcomes, informs policy, and drives the evolution of mental health services. Contributions are invited to advance methodological frameworks, explore novel applications, and address ethical considerations, such as privacy, data security, and algorithmic bias, to ensure the responsible use of RWD in mental health research.

This Research Topic focuses on leveraging RWD to advance mental health services research, with an emphasis on applied and methodological studies that utilize robust analytical techniques. Of particular interest are studies employing advanced methodologies, such as target trial emulation and causal inference techniques, to derive reliable and actionable insights into treatment adherence, symptom severity, functional outcomes, long-term impacts, and relapse prevention. Manuscripts integrating advanced data analytics, machine learning, and large language models to enhance the utility of RWD in evaluating the delivery of treatments and services are highly encouraged. Additionally, studies addressing challenges in systematically tracking mental health outcomes within RWD sources are welcome. Only studies using rigorous and validated methodologies will be considered. Ethical considerations, including privacy, data security, and algorithmic bias, are also central to this topic. We invite original research articles, systematic reviews, meta-analyses, methodological papers, and perspective pieces that contribute to the advancement of RWD-based mental health services research.

Topic Editor Alex Vance is employed by Holmusk USA, Inc. and also has equity ownership in Holmusk. The other Topic Editors report no competing interests related to this Research Topic.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: real world data, real world evidence, mental health services, treatment effectiveness, innovative methods

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.