Growth and Projections of the UK Digital Health Market

Introduction

The 2015 report by Monitor Deloitte for the Office for Life Sciences estimated that the UK digital market is £2bn and is expected to increase to £2.9bn by 2018 (Monitor Deloitte, 2015). Dr. Joshi, who is the Clinical Lead for NHS England’s ‘Empower the Person Portfolio’ and overseeing the national citizen facing digital initiatives within the NHS, states that the most promising market for growth is the mobile health involving apps and wearables that is expected to increase by 35 per cent in the UK by 2018 (Joshi, 2018). For those researching related topics, healthcare dissertation help can be invaluable in analysing these trends. The Global Market Insights Inc. reported that the UK digital health market is estimated to exceed USD 28.3 billion by 2025 (Global Market Insights, Inc., 2019).

The data and set expectation mentioned above clearly demonstrates that awareness and implementation of digital health have increased over a period of time. At the same time, there have been recent studies conducted, which will be discussed later, which shows a disparity in digital health. This disparity has many aspects, which this essay proposes to address and recommend potential policy solutions.

This essay will analysis the factors and impacts of digital innovation and how it has increased the already divided socio-economic class of the society. This essay will identify the key disparity issue, the impact of the disparity, and the main causes of this issue. In that context, this essay will identify major issues of health inequalities, particularly digital exclusion arising from various reasons such as socio-economic inequalities, lack of resources, social norms and belief against use of technologies, and lack of motivation to use and access digital health offerings. This essay will analyse studies regarding various aspects of digital inequality, overlap between digital inequalities and other forms of inequality, and impacts on macro-level domains, particularly gender and class.

The issue, impact and causes

The issues of health inequalities and social determinants of illness and disease have been part of the public health policy focus, which seeks to bring changes relating to the fair distribution of healthcare services. From a social inequality perspective, Petersen (2019:22) states that digital health technologies are positioned to facilitate this change and deliver a fair healthcare system. However, the reality may be different where a gap could be found in the manner citizens and patients are experiencing digital health (Rich, et al., 2019). Watts (2020) states that the COVID-19 pandemic has brought out a certain aspect of the digital divide, for instance, where many outpatients are digitally excluded due to lack of access to a computer from online consultations.

More than a decade ago, Glied and Lleras-Muney (2008) journal Demography wrote that the improvements in health technologies were likely to cause health disparities across education groups since education increases the ability to take advantage of technological advances. They tested using the 1980 and 1990 data on disease-specific mortality rates, and the 1973-1993 cancer registry data. They used compulsory schooling as the education measure and related it with number of active drug ingredients to treat a disease and the relevant rate of change in mortality. They found a greater survival advantage among more-educated individuals in relation to the disease where there was more health-related technological progress (Glied & Lleras-Muney, 2008). Non-users of technology seem to be one particular cause for the disparity. This is supported by the 2019 report by the Office for National Statistics (ONS), which explored digital divide and the scale of digital exclusion. It identified non-users of the internet and the varying skills that vary for different people as barriers to digital inclusion. The report found that in 2018, 8% of people in the UK, which is 4.3 million people, had zero basic digital skills. Another 12%, which is 6.4 million adults, have limited abilities online, which means lacking at least one of the basic digital skills. ONS cited the 2015 estimate of the Centre for Economics and Business Research, which is that 7.9 million people will lack such skills in 2025 (ONS, 2019).

Robinson and colleagues (2015) explored the multi-layered aspects of digital inequality. They state that there is a link between digital inequalities and other forms of inequality. This aspect is relevant to factors such as access, usage, skills, and self-perceptions. As such, the issue of digital inequality can be stated to be linked with a broader range of outcomes concerning life chances and trajectories. Digital inequalities impact individual-level and macro-level domains, such as gender, race and class together with health care, economic activity, politics, and social capital (Robinson, et al., 2015). The digital inequalities found to affect non-user populations have become more concentrated in vulnerable groups (Helsper & Reisdorf, 2016).

This can be supported by studies conducted by Fernandes (2020), Van and Parolin (2020), and Wang and Tang (2020) found that the low-income households, who are likely to be less equipped with technological devices, in quantity and in quality, may suffer more severely from immediate and long-term economic consequences arising out of the Covid pandemic. Beaunoyer and colleagues (2020) in that regard stated that households with better economic resources are likely to have higher incentive to upgrade their devices enabling them better access to digital health offerings. This may be the reason why it could be stated that digital innovation and use are likely enhancing unequal distribution of vulnerability. Thus, Beaunoyer and colleagues (2020) stated that during this Covid pandemic, the most digitally disadvantaged are likely at more risk of suffering the negative outcomes related to the pandemic. In that regard, the most vulnerable social groups, which are the older people, the homeless or the rural residents will face the most difficulty to communicate digitally and are more likely to suffer the negative (Connal Lee, et al., 2008).

The framework developed by Van Dijk (2015) may contribute some insights into understanding the issue of access in regard to the issue of digital disparities. His main arguments are that categorical social inequalities can produce an unequal resource distribution , which can further cause unequal access to digital technologies. The unequal access to digital technologies varies subject to the characteristics of the technologies. However, such unequal access can cause unequal social participation, which in turn reinforces the categorical social inequalities and the unequal resources distributions (Dijk, 2005). Norman and Skinner (2006) stated that the health literacy inequalities are made worse by the digital literacy divide. This aspect causes the differences in the manner people approach, in terms of awareness and knowledge, ability and motivation to access, exploit digital health information in daily life addressing their disease prevention, and health care and promotion.

The World Health Organization (2018) identified social factors, such as education, income level, employment status, ethnicity and gender to influence the health status of a person. There is a wide disparity in the health status across different social groups in all the countries, irrespective of whether they are low, middle or high income countries. Likewise, individuals belonging to a lower socio-economic position are likely to be exposed to a higher risk of poor health (WHO, 2018). Watts (2020) cited Helen Milner, who is the Chief Executive of the Good Things Foundation, a UK charity that aims to make benefits of digital technology more accessible. Ms. Milner also recognises the health inequality, which according to her has worsened over the past decade. She states that there is an extensive link between digital exclusion and social exclusion. Social exclusion is closely linked with poverty, which in turn is linked with health inequalities (Watts, 2020). Milner categorised the barriers leading to exclusion as lack of access, due to mostly economic reasons; of motivation due to the belief that connectivity is not relevant to their lives; and of digital education and skills (Watts, 2020). Out of these reasons, the ONS (2019) recognises the most common reason are the belief (64%), as mentioned above, and a lack of skills (20%)

Wessels (2013) states that the manner of structuring the technology and that of accessing and using it are contributing to the digital divide. Technology allows access to the public sphere and information, which forms a crucial part of the current society. Wessels that that technology forms a crucial part of production, information flow and public participation. At the same time, it has shaped social inequalities. Thus, the materialistic approach to digital adoption has defined who is included in the social information network and the level of opportunities and participation in. the social, economic and democratic process (Wessels, 2013). As a consequence, as Wessels (2010) stated, among other factors such as ethnicity, age, gender and level of education, the socio-economic background or status plays an influential role in determining the digital divide.

Wessels (2013) further states that technology engages the political, social and other symbolic aspects of social life. It has brought about a social transformation. The levels of access and the quality of resources enable a person or a group of persons to participate in the society. However, Wessels states that the problem is when a person (or a group) does not have the required resources. For those with less level of power in the society, they missed out on the allocation of resources. This exposes them to the risk of exclusion and disadvantage (Wessels, 2013). This contradicts the perceived objectives of digital health, which is considered as a solution to a health crisis. Digital health is described to be addressing the health inequalities with policies introduced to focus on health inequalities (Rich, et al., 2019).

According to Baum and Fisher (2014), such inequalities may arise from associating behaviours with the health choices and status of people or a group of people. This approach focuses on commonly recognised behaviour that is associated with increased health risks, such as tobacco smoking, excessive alcohol use or consuming a high-fat diet. Such a behavioural-based strategy disseminates information regarding health and lifestyle risks and benefits relating to different behaviour with the aim that it would motivate people to modify their behaviour (Baum & Fisher, 2014). However, lifestyle behaviour that negatively impacts health is not due to lack of awareness of the risk. It is rather the life constraints and experiences (Anthony, et al., 2004). In this regard, Baum and Fisher (2014) pointed out that a health promotion policy has often failed to incorporate a proper understanding of social health determinants of health, including the influence of the social, environment and other socio-economic and cultural settings. To support this view, they stated chronic diseases or health behaviour including smoking are more prevalent across the socially or economically disadvantaged groups.

The behavioural explanations in policy responses seems to have driven digital health policies. As Petersen (2018) argues, digital health has not critically assessed the personal, social, economic, political and implications. To that effect, Robinson and colleagues (2015) argue that the digital inequality should be addressed along with other traditional forms of inequality.

Deursen (2020) studied the aspect of digital inequality during the Covid-19 pandemic in regard to the uses among the general population. He found that the manner of use of the internet by women differs from men. Their use is found to be more communication based, such as email and social media. Men’s internet activities are more to obtain information. This means women respond differently to crisis (Deursen, 2020). Irrespective, the ONS (2019) survey found that in 2018, 58%, which is 3.1 million women, were among the internet non-users. Further, in the same year, 61% of those with zero basic digital skills turned out to be women (Lloyds Bank, 2018). Jennings and Gagliardi (2013) stated that the use of digital health technologies, such as mobile health apps are not showing positive results concerning women's access to health resources.

A report prepared by Sian Herbert (2017) of the University of Birmingham commissioned by the UK Department for International Development and other Government departments, found the existence of and increasing digital gender gaps. Herbert cited the GSMA (2015) report that found women to be 14% less likely to own a mobile phone than men in the low and middle income countries. Herbert cited the ITU (2017) report that found that the proportion of women using the internet globally is 12% lower than that of men. These figures show that the non-use of the internet by women is across countries. Steeves and Kwami (2017: 176) stated that the gender divide is rooted in the structural gender inequalities.

Sian Herbert (2017) states that the gender gap regarding access, usage and ownership of technological devices and equipment is driven by a set of socio-economic norms and cultural barriers. This negatively affects women and girls as GSMA (2015: 6) reported that social norms have influenced their use and use of mobile technology. They act as barriers for women in that regard together with the issue of financial barriers of owning devices. GSMA (2015) also found that systemic barriers in the form of inadequate gender disaggregated data across mobile subscribers and national statistics together with an unconscious bias within institutions have removed the focus from women.

Crawford and Serhal (2020) stated that digital health is not generally designed from a gender equity perspective. As such, the collective elements comprising lower access; gender imbalance in the digital health leadership; exclusion from app design; and negative gender stereotypes found in digital health could be disadvantageous for women, particularly women from the racial or ethnic minority. In this regard, Trendall (2019) found that the National Health Service's Babylon app has an algorithm that negatively affects women. This app uses artificial intelligence to advise patients who report symptoms and provides probable. It was found that for a female smoker, aged 59 years, who reported symptoms of a heart attack, the app provided her a diagnosis of depression or panic attack. However, the app provided a male with the same symptoms a diagnosis of a possible heart attack. In a way, digital health has not understood women’ health and environment adequately. The gender and racial biases are the result of a lack of such understanding.

Possible solutions to the issue or problem in relation to social justice

This essay has so far seen that the digital health policy practices have not benefited all the sections of the society. The persistent causes of social inequalities, including categorisation of social class and bias against women, are found to overlap with digital disparities. Hence, the first and foremost task is to understand social determinants of health and integrate them in digital health policy.

Social determinants of health, as defined by the WHO’s Commission on the Social Determinants of Health, is the "the conditions in which people are born, grow, live, work and age" and "the fundamental drivers of these conditions." Social determinants also encompass health-related features of neighbourhoods, which could influence health-related behaviours.

Socio-economic factors including and wealth and education are found to be the main causes of health outcomes (Braveman & Gottlieb, 2014 ). Health equity should aim to reduce and eliminate health disparities and disparities in social determinants. Special attention must be given to those in needs and are exposed to greatest risk of poor health due to their social conditions (Braveman, 2014).

The Public Health England (2017) has recognised four levels of solutions to address health inequalities. They are intervention to reduce health inequalities; intervening at population level; intervention through services to achieve best population outcomes; and implementing tools and resources (Public Health England, 2017). What is most relevant in the current context is intervening at population level. This intervention may involve a place-based approach to health. It calls for a collaborative and collective working between local communities and local authorities in addressing specific needs of a particular location (Public Health England, 2017). This intervention involves intervention at civic, community, and service levels. The civic level will involve improving the socio-economic infrastructure, such as transport, education, planning, employment and welfare provision. At the community level, community assets including quality of community life and social support social networks can shape community health (Public Health England, 2017)..This approach can support local actions to address social inequalities. At the service level, programmes and policies should ensure to include in the focus understanding of and benefit to most disadvantaged groups (Public Health England, 2017). This was seen at the North East of England, which had witnessed the largest decline in adult smoking from 29% in 2005 to 17.2% in 2016. The approach was focussed on ‘locally together’ to support tobacco regulation that helped denormalise tobacco (Public Health England, 2017). Thus, people and communities are the core of this approach.

Marmot (2017) states that health is an outcome from social processes. To gain equality, focus must be on the outcomes. This means that there are places for all to access. He states that the health inequalities are the outcome from poor quality of governance, social programmes, and unfair economic arrangements. This is evident in the digital exclusion mentioned in the 2019 ONS report mentioning data on “internet non-users” in 2018 and those who lack basic skills to use digital service or motivation to connect. This also represents a lack of digital infrastructure and governance issues. These issues are signs of a gap between the supply chain’ digital and non-digital aspects, which may result in failure of technology implementation caused by problems of project management concerning supply chain digitalisation (Benzidia & Fabbri., 2016; Underwood & Shelbourn, 2020). It is, thus, recommended that project organisational issues, including digital exclusion, should be understood in the context of technology implementation. There must be real-time information, appropriate measures and metrics ensuring that the supply of services, products and materials are visible and (Ageron, et al., 2020).

Equality of opportunity appeals to natural justice. However, the reality of access to justice may differ based on one’s income. There is a strong association between income and health. The research by the Public Health England on social determinants of health, 2017 found a close association between income and health. Income affects multiple aspects of health. It was found that in England, the proportion of the population not reaching the minimum income standard rose from 25.7% in the 2008-2009 to 30.2% in 2014-2015. Even more, poor housing conditions are often found associated with a number of health problems (Public Health England, 2017). In this context, a multi-level accountability framework based on human rights approach could be recommended. This framework can run across judicial (judicial review and public interest litigation); quasi-judicial (human rights institution, ombudsman, international human rights bodies); administrative (human rights impact assessment); political (parliamentary committees), and social accountability (NGOs, media). These mechanisms have been identified by WHO. Such a mechanism is seen in the Birmingham City Council (WHO, 2016).

A human framework seeks the outcome of social protection, which drives economic growth and overall development. Social protection offers long-term inclusive growth allowing capital accumulation and investment, enhancing labour capacity and productivity and facilitates risk management (WHO, 2013). One form is the re-distribution of spending power from the upper income groups to the lower income groups through conditional cash transfer. Such a social protection approach can bring social stability. It impacts health and social determinants. Thus, targeted programmes where cash are transferred subject to conditions have shown positive impact on health outcomes (WHO, 2013). Thus, these programmes can be aimed at improving digital health status, for example frequent use and accesfs to digital health services.

The offering of requisite infrastructure may not meet the social justice outcome. As seen earlier, one of the causes for digital health disparities is lack of access and particularly lack of basic digital skills. Regarding women participation, use and access of digital health, the three factors of age, lack of resources and media literacy influence their participation (Fotopoulou, 2014). Supporting user engagement may help women and those at the disadvantaged position who cannot use and access the benefits of digital health. A research report by the Deloitte Centre for Health Solutions in 2019 has found that the complexity of technology acts as one of the top barriers in adopting technology. It suggested, thus, that technologies should be intuitive to use and easy to integrate. It found that interventions driven by health information are more likely to be effective and readily adopted when users are engaged and the technology is easily integrated into their daily routines. Another suggestion was that the technology should be tailored to the needs of the users. End-users feedback and data will act as the driver of inventing such technology.

These suggestions above are particularly necessary given the findings mentioned earlier that 3.1 million women are internet non-users and 61% of women are with zero basic in 2018 and that digital health technologies are not resulting to positive outcome among women accessing health resources (Lloyds Bank, 2018; ONS, 2019; Jennings & Gagliardi, 2013). The tailored design of new technology could also bring more women to the use and access of devices enabling them to better access digital platforms, offering medical and health information and services. Specific women favoured platforms may motivate them to use internet and equipment for medical and health information and services. This may eliminate the chances of digital health designed indifferent to gender equity perspective, which Crawford and Serhal (2020) pointed out earlier.

What Trendall (2019) pointed out earlier, the design of digital health may not have understood women’ health and environment. There may be evident gender biases due to this lack of understanding. Reid (2004), thus, stated that the welfare, health care and community recreation system has imbibed a perpetuated stereotypical notions of welfare recipients. She argued that health equity has inadequately represented the experiences of women, both in concept and in methods. She proposed that the social needs should be the primary focus extending dignity to all (Reid, 2004).

Conclusion

Digital technologies occupy a crucial role in people’s everyday lives. They impact health and medical care and services. They could either affect or transform the manner people engage with health care and services. The issue of digital health disparity is a sign that either the digital technologies are not being used or deployed adequately to meet health equity, or they are simply structured based on the existing social determinants, which might have so far produced social inequalities.

This essay has defined and identified the issues associated with digital health disparities and their effects on certain classes of the society. It has found that the existing social inequalities and determinants have defined the digital health framework, which has in turn caused the digital divide. This essay has found that along with the deep-rooted social inequalities, social norms have also defined the gender biasness affecting women’s use and access to digital health care and services.

This essay has found that digital innovation has enhanced the already divided socio-economic class of the society. The digital divide represents the socio-economic inequalities and issues of resources allocation and management. It seems that the digital health transformation is still lagging behind despite the awareness of the benefits that it offers. The perceived universal feature of internet, which evokes the belief that every person has access to internet and technological devices cannot still penetrate the barrier of social inequalities, based on class, gender, sex, or geographical location among others. There is a need for a more concerted and integrated effort emphasising on managing technological change and the change in the manner of use and access to new innovation in the field of health care. Better flow and accessible regulation together with a certain level of conditional resource allocation may accelerate and improve the quality of digital health offerings. This may further reduce and ultimately eliminate the digital gap.

The local-based approach, a part of intervening at population level, which was seen in the denormalisation of use of tobacco in North East of England may be the best strategy to tackle the main causes of the digital divide. It can tackle social determinant-based inequalities to address digital health disparity arising from class categorisation. It can create specially designed programmes for women with having understood the impact of social norms upon them and their manner of and ability to use and access digital health technologies.

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