Category: Health and Healthcare

Digital Healthcare Access and BAME Groups   

05/08/2023 

Background

Digital health designates the use of technology to deliver healthcare and improve the quality of patient care (Frank, 2000). With the emergence of digital health, we saw how the internet and telephones became common tools for delivering healthcare, but this has since expanded so as to include AI technologies, mobile applications, analytics, telemedicine, and wearable devices (Nebeker, Torous, and Bartlett, 2019). Although the term ‘digital health’ was coined by Seth Frank in 2000, technologies were already used during the late 19th century to deliver various forms of care. In 1897, the Lancet reported that a physician treated a child with croup by telephone (Spencer and Daugird, 1990). In the 1970s, clinical data was communicated using radios. And with the rapid technological advancements of the early 2000s, we saw an increasing desire to digitise healthcare, and new terms such as mHealth, eHealth, and Personalised Health began to appear (The Medic Network, 2021).  

Digital health has been associated with a range of benefits; it can improve self-care, long term condition management, provide both preventative and targeted care, and enable more effective patient engagement (Imison et al., 2016). However, not everyone is able to reap the benefits of digitised healthcare. Research indicates that Black and Minority Ethnic (BAME) individuals and groups, who are disproportionately impacted by health inequalities, are often unable to access digitalised healthcare. The risk, thus, is that digital healthcare may exacerbate rather than improve underlying health inequalities. This research brief will outline the aims of digital health and explore some of the barriers to accessing digital health services currently experienced by those from BAME backgrounds.  

The Role of Digital Health

Digital health offers the possibility of providing care from a distance (WHO, 2010). The benefits of digital health include clinicians being able to meet several clients in a short timeframe (Hollander and Carr, 2020), reducing costs, providing accessible healthcare for individuals who cannot leave their home due to disabilities or distance (Valdez et al., 2021), and overall comfort and convenience compared to in-person healthcare (Gajarawala and Pelkowski, 2021). It, furthermore, allowed vulnerable individuals to access healthcare during the pandemic, reducing the risk of exposure to the virus (Charles, 2000; Wax and Christian, 2020).

Current studies show that digital healthcare can offer the same quality of care compared to in-person equivalents. A study by Finkelstein, Speedie, and Potthoff (2006) found no difference in mortality rates between groups that used digital care options and those who relied on in-person care. This was confirmed in a systematic review of meta-analyses regarding the effect of telehealth on mortality, which suggested that telehealth “did not increase mortality rates, and in some studies, the rates of mortality even reduced for patients who were managed by telehealth” (Snoswell, Stringer, and Smith, 2021). Digital health also empowers individuals with chronic illnesses to engage in self-management of their health condition and encourages learning (Suter, Suter, and Johnston, 2011; McLean, 2013).

A US study during the COVID-19 pandemic found that people of Black and Latino backgrounds were 17% more likely to utilise digital health than their white counterparts (Campos-Castillo and Anthony, 2021). In the United Kingdom, research indicates that BAME individuals are more likely to experience poorer health compared to the white population (Raleigh, 2023), are less satisfied with their experience of NHS and hospital services, and are less likely to have access to the Internet (Wadhawan et al., 2023). 

Digital Exclusion in BAME Groups

Although multiple definitions of digital exclusion exist, it can broadly be defined as “being unable to access or use internet-enabled technology and Web-based services” (Greer et al., 2019). Digital exclusion can be caused by a wide range of exacerbating factors. BAME individuals experiencing economic precarity are more likely to have difficulties accessing the internet, and may struggle to afford the necessary devices to access online services (LaVeist, 2005; Wegermann et al., 2021).

Digital exclusion may also be caused or exacerbated by cultural factors, especially language barriers. Technology develops at a fast rate, and individuals may need to enrol in courses to learn how to utilise devices and navigate software (Mackey and Jacobson, 2011), but for many BAME individuals who have limited proficiency in the English language, accessing these courses and platforms may prove challenging (Healthwatch, 2021).

Another cause of digital exclusion relates to a lack of digital literacy. Digital literacy, however, involves not only the ability to use software or devices to communicate and access information, but encompasses a broad range of cognitive, motor, and emotional skills needed to function effectively in digital environments (Eshet-Alkalai, 2004). Owen et al (2003) investigated the experience and provision of ICTs in deprived areas, showing that people from BAME groups typically have less access to and experience of using ICT compared to their white counterparts. It was seen that age, economic status, and income exert significant influence on this pattern. Given such a pattern, it is no surprise that BAME individuals, on average, struggle to access digital health services.

Health inequalities and disparities are, therefore, exacerbated by digital exclusion. Racial and economic barriers felt by individuals of BAME backgrounds typically translate into barriers for the use of different forms of digital health (Wegermann et al., 2021). As such, digital exclusion causes additional barriers and extra hurdles, leading to further disadvantages for some of society’s most vulnerable groups. As Ramasawmy, Poole and Banerjee (2021) note, given the disproportionate impact of the COVID-19 pandemic on ethnic minority communities in the UK, “including higher rates of diagnosis, hospitalisation and mortality”, we need to critically reflect on the interrelationship between ethnicity, health inequalities, and the rapid development of digital healthcare solutions (see also Batty et al., 2021). During the unfolding of the pandemic, it was seen that the communication of critical COVID-19 health messages was slow and tended to exclude minority groups from receiving key information, “due to variation in digital access, literacy and language barriers” (Ramasawmy, Poole, and Banerjee, 2021).

This situation posed a significant challenge to the Government’s Digital First policy. In the Lancet Public Health, Poole, Ramasawmy, and Banerjee (2021) suggested that recent data indicates that digital health initiatives may not have reduced health inequalities, contrary to the ambitions of Digital First. In this paper, they set out five key challenges to this policy: (1) As mentioned above, age-adjusted data strongly indicates that the mortality rate for COVID-19 was significantly higher among “minority ethnic people compared with people from a White British background”. (2) Although the UK Office for National Statistics showed that 92% of adults were “recent internet users”, this usage was the lowest among older people from minority ethnic backgrounds. (3) Given that digital exclusion is clustered in minority ethnic groups, this posed a significant challenge when it came to accessing digital health services. Because minority ethnic groups exhibited less financial resilience during the pandemic, this caused a “triple disadvantage” of limited digital access, low digital literacy, and financial hardship (Poole, Ramasawmy, and Banerjee, 2021). (4) As only 33% of black and minority ethnic individuals downloaded the NHS’s COVID-19 app, compared with 51% of people from a White ethnic background, we can see that there was an unequal adoption of COVID-19 focused digital technologies. (5) Vaccination hesitancy or scepticism was highest among minority ethnic groups which may have been partly caused by a reluctance or inability to engage with electronic booking platforms, “particularly if they are also older or socioeconomically disadvantaged, or both” (2021).

This is an ethical concern, and the inequality gap seems to continue to widen with more barriers being formed. It is imperative for digital exclusion to be addressed to achieve equity in healthcare for all groups.

Policy Recommendations

In order to mitigate the growing digital health divide currently experienced by those from ethnic minority backgrounds, we propose the following policy recommendations:

• Following Kapadia et al (2022) of the NHS Race & Health Observatory, we suggest that digital literacy support, “perhaps in the form of community digital hubs”, should be offered to those who struggle to use online services due to a lack of digital literacy.
  
  
• Digital healthcare provisioners must design applications and online tools with the realities of diverse service users in mind, including people’s entire experience, and social and cultural context. This may involve building ethnically diverse digital teams, investing in training with the aim of deconstructing racism and encouraging inclusive practices. Digital teams should, moreover, partner with relevant organisations, groups, and stakeholders to address ethnic disparities in health outcomes. It is important to “Design with compassion, and where possible start by co-designing with communities and groups that are seldom heard” (Kapadia et al., 2022, p. 51).
  
  
• Healthcare information should be communicated in a variety of languages with a clear focus on the support for those with limited proficiency in the English language (Healthwatch, 2021, p. 12).

References

Batty, G.D., Gaye, B., Gale, C.R., Hamer, M., and Lassale, C. (2022). Explaining ethnic disparities in COVID-19 mortality: population-based, prospective cohort study. American Journal of Epidemiology, 191(2): 275-281.  

Campos-Castillo, C., and Anthony, D. (2021). Racial and ethnic differences in self-reported telehealth use during the COVID-19 pandemic: a secondary analysis of a US survey of internet users from late March. Journal of the American Medical Informatics Association, 28(1): 119-125.

Charles, BL. (2000). Telemedicine can lower costs and improve access. Health Finance Manager, 54(4).

Eshet-Alkalai, Yoram. (2004). Digital literacy: A conceptual framework for survival skills in the digital era. Journal of Educational Multimedia and Hypermedia, 13(1).

Frank, S.R. (2000). Digital Health Care—the convergence of health care and the internet. Journal of Ambulatory Care Management, 23(2): 8-17.

Finkelstein, S.M., Speedie, S.M., and Potthoff, S. (2006). Home telehealth improves clinical outcomes at lower cost for home healthcare. Telemedicine Journal & e-Health, 12(2): 128-136.

Gajarawala, S.N., and Pelkowski, J.N. (2021). Telehealth benefits and barriers. The Journal for Nurse Practitioners, 17(2): 218-221.

Greer, B., Robotham, D., Simblett, S., Curtis, H., Griffiths, H., and Wykes, T. (2019). Digital exclusion among mental health service users: qualitative investigation. Journal of Medical Internet Research, 21(1).

Healthwatch. (2021). Locked out: Digitally excluded people’s experience of remote GP appointments. Available at: https://www.healthwatch.co.uk/sites/healthwatch.co.uk/files/Digital%20Exclusion%20v4.pdf.

Hollander J.E.,  and Carr, B.G. (2020). Virtually perfect? Telemedicine for covid-19. New England Journal of Medicine.

Imison, C., Castle-Clarke, S., Watson, R., and Edwards, N. (2016). Delivering the benefits of digital health care. Nuffield Trust. Available at: https://www.nuffieldtrust.org.uk/research/delivering-the-benefits-of-digital-health-care.

Kapadia, D., Zhang, J., Salway, S., Nazroo, J., Booth, A., Villarroel-Williams, N., Becares, L., and Esmail, A. (2022). Ethnic Inequalities in Healthcare: A Rapid Evidence Review. NHS Race & Health Observatory. Available at: https://www.nhsrho.org/publications/ethnic-inequalities-in-healthcare-a-rapid-evidence-review/.

LaVeist, T.A. (2005) Disentangling race and socioeconomic status: a key to understanding health inequalities. Journal of Urban Health, 82: 26-34.

Mackey, T. P., and Jacobson, T. E. (2011). Reframing information literacy as a metaliteracy. College & Research Libraries, 72(1): 62–78.

McLean, S., Sheik, A., Cresswell, K., Nurmatov, U., Mukherjee, M., Hemmi, A., and Pagliari, C. (2013). The impact of telehealthcare on the quality and safety of care: a systematic overview. PLoS One, 19(8): e71238. https://doi.org/10.1371/journal.pone.0071238.

Nebeker, C., Torous, J. and Bartlett Ellis, R.J. (2019). Building the case for Actionable Ethics in digital health research supported by Artificial Intelligence. BMC Medicine, 17(1).

Owen, D., Green, A.E., McLeod, M., Law, I., Challis, T. and Wilkinson, D. (2003). The use of and attitudes towards information and communication technologies (ICT) by people from black and minority ethnic groups living in deprived areas. Department for Education and Skills Research Report, 450. Available at: https://warwick.ac.uk/fac/soc/ier/publications/2003/owen_et_al_2003_atts_ict.pdf.

Poole, L., Ramasawmy, M., and Banerjee, A. (2021). Digital first during the COVID-19 pandemic: does ethnicity matter. The Lancet Public Health. https://doi.org/10.1016/S2468-2667(21)00186-9.

Raleigh, V. (2023). The health of people from ethnic minority groups in England. Available at: https://www.kingsfund.org.uk/publications/health-people-ethnic-minority-groups-england.

Ramasawmy, M., Poole, L., and Banerjee, A. (2021). Learning our lesson: using past policies to improve digital and ethnic inequalities beyond the pandemic. Archives of Public Health, 79(218).

Snoswell, C., Stringer, H., and Smith, A. (2021). An overview of the effect of telehealth on mortality: A systematic review of meta-analyses. Journal of Telemedicine and Telecare. https://doi.org/10.1177/1357633X211023700.
 

Spencer D., and Daugird, A. (1990). The nature and content of telephone prescribing habits in a community practice. Family Medicine, 205(9).

Suter, P., Suter, W.N., and Johnston, D. (2011). Theory-based telehealth and patient empowerment. Population Health Management, 14(2): 87-92.

The Medic Network. (2021). A Brief History of Digital Health. https://medium.com/that-medic-network/a-brief-history-of-digital-health-b238f1f5883c.

Valdez, R.S., Rogers, C.C., Claypool, H., Trieshmann, L., Frye, O., Wellbeloved-Stone, C. and Kushalnagar, P. (2021). Ensuring full participation of people with disabilities in an era of telehealth. Journal of the American Medical Informatics Association, 28(2): 389-392.

Wadhawan, M., Nguyen, Z., Linden, B., Barry, C., and Abiola, A. (2023). Digital apps and reducing ethnic health inequalities. NHS Race & Health Observatory. Available at: https://www.nhsrho.org/wp-content/uploads/2023/01/Digital-apps-and-reducing-ethnic-health-inequalities-January-2023-1.pdf.

Wax, R.S., and Christian, M.D. (2020) Practical recommendations for critical care and anaesthesiology teams caring for novel coronavirus (2019-nCoV) patients. Canadian Journal of Anaesthesia, 67(5): 568-576.

Wegermann, K., Wilder, J.M., Parish, A., Niedzwiecki, D., Gellad, Z.F., Muir, A.J., and Patel, Y.A. (2021). Racial and socioeconomic disparities in utilization of telehealth in patients with liver disease during COVID-19. Digestive Diseases and Sciences, 67(1): 93-99.

WHO. (2010). Telemedicine: opportunities and developments in member states. Report on the second global survey on eHealth. World Health Organization.