Abstract
Technology corporations and the emerging digital health market are exerting increasing influence over the public healthcare agendas forming around the application of mobile medical devices (wearables). By promising quick and cost-effective technological solutions to complex healthcare problems, they are attracting the interest of funders, researchers, and policymakers. They are also shaping the public facing discourse, advancing an overwhelmingly positive narrative predicting the benefits of wearable medical devices to include personalised medicine, improved efficiency and quality of care, the empowering of under-resourced communities, and delivery of health services previously unavailable to the citizens of developing countries. Typically techno-optimist in their description, the key barriers to this impending inflection point in healthcare are identified as technical issues such as short battery life and a lack of data protection. However, this tech innovation narrative is consistent with problematic ethical, social, and political assumptions that have practical and normative effects, and risk, one, undermining the real clinical potential of wearable devices and, two, designing social inequality and injustice into our mobile health interventions in global healthcare. I argue that the foundational assumptions dealing with the just distribution of healthcare ‘goods’ (efficiency), the individual as part of society (autonomous and independent), and the political framing (neoliberal) of future healthcare policy devalue equity and despite what they promise cannot meet the distinctive needs of individuals and groups that do not conform to a standardised concept of care-receiver.
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Notes
This vision requires even healthy people to accept extensive surveillance and can demand persons trade their privacy for public services and well-being (Prainsack, 2019).
See Crisp’s (2003) outline of distributional justice and explanation of why ethically it is vital that we consider how welfare is shared within and across communities and not simply to focus on aggregate well-being.
Some market forecasts predict that by 2026 wearable health will be worth $139 US billion and in 2021, up to 1 billion devices could be in use (ANDHealth, 2020).
Lupton (2013) notes that often this is facilitated through ‘lay people’ self-monitoring and seeking information on illness, treatment, and health, and by enabling remote consultation.
Public-private partnerships add another layer of complexity that I do not have scope to address in this article. Ballantyne and Stewart (2019) offer an interesting case study of big data, the NHS and public-private partnerships.
See the review conducted by Rowland et al. (2020).
On the poor performance of scaling-up mHealth interventions, see Tomlinson et al. (2013)
Cited in Morozov (2013), p. 1
Sadowski provides a broader and more detailed discussion of this issue throughout his book, Too Smart.
See, for example Dunn et al. (2018)
A search conducted by Morley and Floridi (2019) of the social sciences citation index in 2018 pairing ‘empower’ and ‘health’ returned 6651 articles. It is also noted that the narrative of empowerment coincides with the rise of neoliberal health policy.
mHealth may have a legitimate role in achieving these outcomes and shows promise in improving access to health services in low- and middle-income countries; however, success appears less likely if healthcare programs are shaped by solutionist rhetoric, are technocentric, and focus on efficiency at the expense of egalitarian forms of distributive justice, such as giving priority to the worst-off. Appropriately designed mHealth interventions that involve socially embedded research and are subject to rigorous health technology assessments show potential to improve quality of life (although there is presently little clinical evidence), especially where traditional healthcare support and infrastructure is unavailable (see Agarwal & Labrique, 2014; Mechael, 2009). I discuss this further in Section 5.
Patient care in clinical settings is, at times, portrayed as a ‘burden’ by those promoting eHealth alternatives. See, for example Baxendale (2016).
On disruptive technology, see Bower and Christensen (1995). This, in part, is why it is important to rebalance the discourse on mHealth, promoting alternative approaches (including some lead by the private sector) that are socially embedded and are more closely aligned with a moral repertoire of civic responsibility than the industrial worldview. An example of the latter is the GSMA (2015) Connected Women project.
As Sharon notes (Sharon, 2017, p. 94) the narrative of disruption and revolution ‘…saturates popular media and policy reports’, and has become popular with ‘…public health officials, and funding agencies’.
Gilbert and Ovadia (2011) offer the historical example of lobotomy, acceptance of which benefitted from the media portrayal as a ‘miracle cure’. Such enthusiasm created an environment where careful assessment of ethical and social impacts was typically absent.
Gardner and Warren (2019) provide the example of deep brain stimulation (DBS), where the media reporting is highly optimistic, while clinicians involved in the application of this technology present a more conservative assessment, aware of the limitations and complexities associated with the use of DBS to treat neurological conditions. See also Racine et al. (2007, 2010).
See also Lupton (2013)
This article is staged as a response to the overwhelmingly positive account of mHealth commonly encountered in market reports and public health policy; however, it is appropriate to highlight that those users who meet this description and have an appropriate level of health literacy may benefit from the narrow conception of mHealth (see Wagner, 2019).
The literature on empowerment is diverse and expansive, and consists of many competing discourses. For an excellent review in relation to healthcare, see Morley and Floridi (2019).
Industrial: valorising technology and productivity (see Boltanski & Thévenot, 2006).
See also Hutchison et al. (2016).
Hutchison et al. (2016) and her colleagues provide a useful distinction between efficiency and equity in healthcare, with the former concerned with ‘the aggregate number of health gains achievable under a given resource constraint’, and the latter with the ‘distribution of those gains across the population or, in other words, the characteristics of the people to whom the health gains accrue’.
This approach also presents a compromised notion of ‘empowerment’ that requires individuals accept a normalised identity determined by public and private institutions. It has been argued that empowerment, at a minimum, should enable self-determination and support substantive forms of autonomy (see Morley & Floridi, 2019).
Ahuja and Kumar (2021) have recently highlighted the larger problem of the trivialization of ethics by business stakeholders involved in technology industries.
This tension is discussed by Barsdorf and Millum (2017)
On an ethics of care and its relationship to western thought, see Held (2006).
Sullivan and Reiner (2019) have previously inquired as to how ‘well-being’ should be established when developing technologies with the intent to alter behaviour, and conclude that at a minimum individuals must be enabled to derive and pursue their own idea of living well.
Look-alike modelling arguably exacerbates this problem (see Montgomery et al., 2018), shaping the market in the image of the best commercial client.
The interaction of mHealth and personal autonomy is complex and cannot be addressed within the constraints of this manuscript; however, the normalized notion of health and patient that typically founds mHealth programs is suggestive of what Morley and Floridi (2019) refer to as ‘static autonomy’, where we exchange the paternalism of the physician-patient relationship for ‘freedom’ within the confines of normative identities.
This speaks to a ‘second’ problem for an efficiency ethic. Beyond potentially discriminating against sub-populations, the failure to socially embed research may undermine efficiency simply because the intervention is not as effective as hoped.
See Paldan et al. (2018) who elaborate upon the notion of ‘intervention-generated-inequalities’.
While I am sceptical of the claim that markets can allocate resources justly, modern forms of capitalism are premised on this idea. See, for example Nozick (1974). Regarding patient-centred care, I am referring to the six domains of care outlined by the Institute of Medicine in the USA (Institute of Medicine, 2001).
For a detailed discussion of mHealth and how an individual’s social situation may impact the outcome of self-monitoring applications, see Paldan et al. (2018) who offer a helpful account of how ‘intervention-generated-inequalities’ might be redressed.
On the larger issue of the relation of wearable devices, social determinants of health, and overall well-being, see Owens and Cribb (2019).
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Howard, M. Wearables, the Marketplace and Efficiency in Healthcare: How Will I Know That You’re Thinking of Me?. Philos. Technol. 34, 1545–1568 (2021). https://doi.org/10.1007/s13347-021-00473-4
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DOI: https://doi.org/10.1007/s13347-021-00473-4