Digital Mental Health Challenges and the Horizon Ahead for Solutions

Abstract

The demand outstripping supply of mental health resources during the COVID-19 pandemic presents opportunities for digital technology tools to fill this new gap and, in the process, demonstrate capabilities to increase their effectiveness and efficiency. However, technology-enabled services have faced challenges in being sustainably implemented despite showing promising outcomes in efficacy trials since the early 2000s. The ongoing failure of these implementations has been addressed in reconceptualized models and frameworks, along with various efforts to branch out among disparate developers and clinical researchers to provide them with a key for furthering evaluative research. However, the limitations of traditional research methods in dealing with the complexities of mental health care warrant a diversified approach. The crux of the challenges of digital mental health implementation is the efficacy and evaluation of existing studies. Web-based interventions are increasingly used during the pandemic, allowing for affordable access to psychological therapies. However, a lagging infrastructure and skill base has limited the application of digital solutions in mental health care. Methodologies need to be converged owing to the rapid development of digital technologies that have outpaced the evaluation of rigorous digital mental health interventions and strategies to prevent mental illness. The functions and implications of human-computer interaction require a better understanding to overcome engagement barriers, especially with predictive technologies. Explainable artificial intelligence is being incorporated into digital mental health implementation to obtain positive and responsible outcomes. Investment in digital platforms and associated apps for real-time screening, tracking, and treatment offer the promise of cost-effectiveness in vulnerable populations. Although machine learning has been limited by study conduct and reporting methods, the increasing use of unstructured data has strengthened its potential. Early evidence suggests that the advantages outweigh the disadvantages of incrementing such technology. The limitations of an evidence-based approach require better integration of decision support tools to guide policymakers with digital mental health implementation. There is a complex range of issues with effectiveness, equity, access, and ethics (eg, privacy, confidentiality, fairness, transparency, reproducibility, and accountability), which warrant resolution. Evidence-informed policies, development of eminent digital products and services, and skills to use and maintain these solutions are required. Studies need to focus on developing digital platforms with explainable artificial intelligence–based apps to enhance resilience and guide the treatment decisions of mental health practitioners. Investments in digital mental health should ensure their safety and workability. End users should encourage the use of innovative methods to encourage developers to effectively evaluate their products and services and to render them a worthwhile investment. Technology-enabled services in a hybrid model of care are most likely to be effective (eg, specialists using these services among vulnerable, at-risk populations but not severe cases of mental ill health).

Digital Mental Health Amid COVID-19

“…There are a range of digital mental health interventions available for various mental health needs and conditions, from substance abuse recovery to suicide prevention [23]. Stronger evidence and new models of care will be necessary for higher accessibility, equity, and successful clinical implementation [5,20,24,25]. Predictive technologies (e.g., real-time machine learning algorithms) are useful for monitoring but should be deployed carefully as decision support tools [20,25,26].…”

“…Stronger evidence and new models of care will be necessary for higher accessibility, equity, and successful clinical implementation [5,20,24,25]. Predictive technologies (e.g., real-time machine learning algorithms) are useful for monitoring but should be deployed carefully as decision support tools [20,25,26]. On an operations level, there is the issue of integrating technology into mental health care delivery for diverse populations.…”

“…However, there is a lack of studies demonstrating sustainable improvements in symptoms and global functioning from specialized early intervention services [31]. Considerations other than equity include engagement, ethics, security, adaptability, and technicality [25,32]. There were mostly contextual issues noted in recent studies.…”

“…There were safety, comfort, and flexibility noted for telehealth experiences during COVID-19, although face-to-face service delivery was still preferred [47]. There is a great opportunity for digital and data-driven tools to become prominent (e.g., machine learning algorithms and artificial intelligence (AI) platforms, the Internet of Things (IoT), digital phenotyping, and immersive solutions such as virtual reality (VR)) [25]. The resources required to guide the selection, implementation, and evaluation of machine learning algorithms and artificial intelligence (AI) platforms are available online for public use [48].…”

“…Digital mental health implementation in clinical settings requires thoughtfulness and preparedness [55], along with a higher understanding of technical and medical standards, in order to increase effectiveness, validation, and user-centered design as well as to counter ethical concerns [20]. Stakeholders need an adequate infrastructure and skills-base [25,56] as well as ethical guidance in the development, deployment, and management of digital mental health solutions [20,56]. The paradoxical efforts to advance evidence and implementation of digital mental health led to the suggestion for simulation-based research [57].…”