Balcombe Luke, De Leo Diego
Australian Institute for Suicide Research and Prevention, Griffith University, Brisbane, Australia.
JMIR Ment Health. 2021 Mar 29;8(3):e26811. doi: 10.2196/26811.
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).
在新冠疫情期间，心理健康资源供不应求，这为数字技术工具填补这一空白提供了契机，在此过程中，数字技术工具展现出提高其有效性和效率的能力。然而，尽管自21世纪初以来，技术支持的服务在疗效试验中显示出了良好的效果，但在可持续实施方面仍面临挑战。重新概念化的模型和框架以及各种努力已着手解决这些实施过程中持续存在的问题，这些努力包括在不同的开发者和临床研究人员中拓展合作，为他们提供推进评估研究的关键要素。然而，传统研究方法在应对心理健康护理复杂性方面的局限性需要采用多元化的方法。数字心理健康实施面临的挑战的关键在于现有研究的疗效和评估。在疫情期间，基于网络的干预措施越来越多地被使用，使得人们能够以可承受的成本获得心理治疗。然而，滞后的基础设施和技能基础限制了数字解决方案在心理健康护理中的应用。由于数字技术的快速发展超过了对严格的数字心理健康干预措施和预防精神疾病策略的评估，因此需要整合各种方法。为了克服参与障碍，尤其是与预测技术相关的障碍，需要更好地理解人机交互的功能和影响。可解释的人工智能正被纳入数字心理健康实施中，以获得积极且负责任的结果。对用于实时筛查、跟踪和治疗的数字平台及相关应用程序的投资有望在弱势群体中实现成本效益。尽管机器学习受到研究实施和报告方法的限制，但非结构化数据的日益使用增强了其潜力。早期证据表明，增加此类技术的优点大于缺点。基于证据的方法的局限性要求更好地整合决策支持工具，以指导政策制定者实施数字心理健康。在有效性、公平性、可及性和伦理（如隐私、保密性、公平性、透明度、可重复性和问责制）方面存在一系列复杂问题，需要加以解决。需要有基于证据的政策、开发卓越的数字产品和服务以及使用和维护这些解决方案的技能。研究需要专注于开发具有基于可解释人工智能的应用程序的数字平台，以增强恢复力并指导心理健康从业者的治疗决策。对数字心理健康的投资应确保其安全性和可操作性。终端用户应鼓励使用创新方法，以促使开发者有效评估其产品和服务，并使其成为一项值得的投资。在混合护理模式中，技术支持的服务最有可能有效（例如，专家在弱势群体、高危人群中使用这些服务，但不适用于严重精神疾病患者）。
