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).

中文翻译：

---

数字心理健康挑战和解决方案的展望

在COVID-19大流行期间，精神卫生资源的需求超过供应，这为数字技术工具填补这一新空白提供了机会，并在此过程中展示了提高其有效性和效率的能力。然而，自2000年代初以来，尽管在功效试验中显示出可喜的成果，但以技术为基础的服务在可持续实施方面仍面临挑战。这些实现方案的持续失败已在重新概念化的模型和框架中解决，并且进行了各种努力，以在不同的开发人员和临床研究人员之间进行分支，以为他们提供进一步进行评估研究的关键。但是，传统研究方法在应对精神卫生保健的复杂性方面的局限性需要采取多样化的方法。数字心理健康实施挑战的症结在于对现有研究的有效性和评估。在大流行期间，越来越多地使用基于Web的干预措施，从而使人们能够以可承受的价格获得心理疗法。然而，落后的基础设施和技能基础限制了数字解决方案在精神卫生保健中的应用。由于数字技术的飞速发展已经超过了对严格的数字心理健康干预措施和预防精神疾病的策略的评估速度，因此需要对方法进行融合。人机交互的功能和含义需要更好地理解，以克服参与障碍，尤其是在预测技术方面。可解释的人工智能正在被整合到数字心理健康实施中，以获得积极负责的结果。对用于实时筛查，跟踪和治疗的数字平台和相关应用程序的投资为脆弱人群提供了成本效益的保证。尽管机器学习受到研究行为和报告方法的限制，但越来越多地使用非结构化数据增强了其潜力。早期的证据表明，增加这种技术的好处大于弊端。基于证据的方法的局限性要求更好地整合决策支持工具，以指导决策者实施数字心理健康。有效性，公平性，访问权和道德等一系列问题（例如，隐私，机密性，公平性，透明度，可重复性和责任制），这值得解决。需要提供循证政策，开发出色的数字产品和服务以及使用和维护这些解决方案的技能。研究需要集中精力开发具有可解释的基于人工智能的应用程序的数字平台，以增强适应力并指导精神卫生从业者的治疗决策。在数字心理健康方面的投资应确保其安全性和可操作性。最终用户应鼓励使用创新方法，以鼓励开发人员有效地评估他们的产品和服务，并为他们提供有价值的投资。在混合护理模式中，以技术为基础的服务最有可能是有效的（例如，在脆弱人群中使用这些服务的专家，