Is There an App for That?: Ethical Issues in the Digital Mental Health Response to COVID-19

Abstract

Well before COVID-19, there was growing excitement about the potential of various digital technologies such as tele-health, smartphone apps, or AI chatbots to revolutionize mental healthcare. As the SARS-CoV-2 virus spread across the globe, clinicians warned of the mental illness epidemic within the coronavirus pandemic. Now, funding for digital mental health technologies is surging and many researchers are calling for widespread adoption to address the mental health sequelae of COVID-19. Reckoning with the ethical implications of these technologies is urgent because decisions made today will shape the future of mental health research and care for the foreseeable future. We contend that the most pressing ethical issues concern (1) the extent to which these technologies demonstrably improve mental health outcomes and (2) the likelihood that wide-scale adoption will exacerbate the existing health inequalities laid bare by the pandemic. We argue that the evidence for efficacy is weak and that the likelihood of increasing inequalities is high. First, we review recent trends in digital mental health. Next, we turn to the clinical literature to show that many technologies proposed as a response to COVID-19 are unlikely to improve outcomes. Then, we argue that even evidence-based technologies run the risk of increasing health disparities. We conclude by suggesting that policymakers should not allocate limited resources to the development of many digital mental health tools and should focus instead on evidence-based solutions to address mental health inequalities.

Towards a Taxonomy of AI Risks in the Health Domain

“…For one, there is the risk of increasing already existing inequalities in health and public health since access and opportunities to gain the benefits resulting from this technology are not distributed equally across society and are largely dependent on the user's digital competency as well as their access to the internet [3]. This circumstance may result in biased training data for AI systems [5], [19], which is particularly problematic because populations that are most in need and likely to benefit most from the systems are often excluded from contributing to the data sets [20]. Such data biases might persist in the developing processes of drugs and treatment methods.…”

You Can’t Have AI Both Ways: Balancing Health Data Privacy and Access Fairly

“…Moreover, resources allocated to health AI may come at the expense of non-AI solutions. Since the value of AI remains uncertain and many health interventions in the field of AI are—thus far—of limited real-world effectiveness ( D’Amour et al, 2020 ; Skorburg et al, 2021 ), it has been argued that policy-makers should not allocate resources to AI tools exclusively, especially when these resources could strengthen existing evidence-based solutions and help to overcome structural barriers to care ( Skorburg et al, 2021 ). This dilemma is well-known in the field of public health.…”

Mental health mobile application self‐help for adolescents exhibiting psychological distress: A single case experimental design

“…This limited evidence is surprising given the national drivers promoting digital interventions, including ‘quality assured apps’, including National Institute for Health and Care Excellence (NICE, 2019 ) guidelines and the Department of Health ( 2012 ) Future in Mind initiative. Nonetheless, while the COVID‐19 pandemic has highlighted the role for digital interventions (Moreno et al, 2020 ), the use and recommendation of mobile apps remain under scrutiny (Skorburg & Yam, 2022 ; Torous, Myrick et al, 2020 ; Whelan et al, 2020 ).…”