Data and Tech Use Within Our Health System — A Primer

Edafe Onerhime
Data, Tech & Black Communities
6 min readMar 8, 2021

--

Data and tech use within our health system

The purpose of this blog was to provide a primer to participants attending a roundtable on the use of data and technology. It explains key terms used and provides relevant examples of how data and technology can negatively impact upon Black lives in the UK. Many of the points we raise apply to other marginalised communities too.

What do we mean by data and technology?

Data and technology are words that are understood in a multitude of ways. A common definition of data is “a collection of facts such as numbers, words, pictures”. A basic definition of technology is that it’s the practical application of knowledge to create tools or processes to create goods and services.

But these definitions don’t tell us very much about how these things affect our day-to-day lives. Thinking about what these things look like in practice and how they’re used is probably a more useful way of thinking about them.

For example, data could be like the Scotland Coronavirus (COVID-19): daily data, which is updated each day to provide the latest available data on COVID-19 in Scotland during the pandemic, including cases and testing, deaths, and vaccinations. An example of technology might be insulin pumps. These discreet electronic devices deliver regular insulin doses day and night, making needles a thing of the past.

When it comes to definitions, it’s also helpful to think about what we mean by “health”. The World Health Organization (WHO) says, “Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity”. This definition hasn’t changed since 1948; the year the National Health Service (NHS) was established. It’s a high bar, so how well are Black people in the UK by this measure? How are data and technology helping us be healthier, keeping things as they are, or making our health worse?

How is data and technology used within the health system?

Content warning: Mentions of mental health, suicide, and death.

Data and technology have been around as long as humans have used information and tools. They are undoubtedly helpful in aiding our learning and thinking — but they can also entrench and compound existing power dynamics and increase existing social inequalities.

Health is essential, not just for us to feel healthy, but also because how we live and how well off we are can significantly impact our health. If you’re poor, most likely, your overall health will be worse thanks to ‘social determinants of health’. You can also expect not to live as long (on average) as someone better off. The more disadvantaged you are, the more likely you won’t finish school or get as good a job as people who aren’t poor. And all of this affects your health too. Black Africans (45%) have some of the highest income poverty rates. In comparison, black Caribbeans (30%) have lower rates of income poverty in the UK.

So how are data and technology being used right now in health? And what’s on the horizon?

When it comes to healthy minds, we know that it’s not easy to get access to help when we need it. We also know that as Black people, we face more hurdles individually and collectively, which can range from racism, aggression, or in the case of migrants, language barriers. Among 16 to 24-year-olds, unemployment rates are highest for people from a Black background (26%) in comparison with their white counterparts (11%), a massive source of stress.

Black African migrants may struggle to access health promotion information and services because of language barriers and advice that isn’t tuned into their cultural context. Here, AI language assistants could help migrants understand symptoms or better understand what their doctor told them. M-health or mobile health services like Shifra provide evidence-based and respectful health knowledge in every language.

We know that suicide rates are higher among young men of Black African, Black Caribbean origin, and among middle-aged Black African, Black Caribbean and South Asian women than among their white British counterparts. In this environment, technology like a medical chatbot telling a fake patient to kill themselves would be a disaster.

When it comes to getting conditions diagnosed, doctors are being trained on white skin. This means cases of sepsis, skin cancer, even lips turning blue are missed. It took Malone Mukwende, a student at St George’s, University of London, to write a guide, Mind the Gap, for healthcare professionals. Mind the Gap, written in 2020, shows how conditions manifest on Black and brown people. Representation can save lives, but AI being trained in dermatology without adequate representation is putting the lives of Black people at risk.

And bringing new life into this world shouldn’t mean leaving it too. Black women are five times more likely to die in pregnancy, childbirth or in the six-month postpartum period than white women. It doesn’t end there; we also find worse outcomes in “infant outcomes, breast and cervical cancer diagnosis, and mental health support”. The root cause of this disparity has been linked to bias from consultants and historical distrust of public institutions leading to lower take-up of cervical smears. Most importantly, these services are not centred on Black women and don’t treat them with respect and dignity. AI like this one that finds problems doctors miss, especially for Black people, are helping to reduce and highlight the gaps. Data and technology highlight the issues, but humans still need to fix them.

This is why it’s important to understand why data is being collected (or not), how it might be used, as well as which technologies are being deployed. We think this is especially true for marginalised groups. That’s why we are collating reports and articles which capture the impact of data and technology on Black health and Black health practitioners here. Do let us know of other resources we can add to this open library.

In the meantime, here is another example to get you thinking. With the ups and downs of the pandemic and lockdown, the government is thinking about vaccine passports. A vaccine passport or certificate proves you’ve received a Covid-19 vaccination. It could be a piece of paper or part of a mobile app. The WHO doesn’t recommend vaccine passports, but they are in use in Israel. There, people who aren’t or can’t be vaccinated find themselves unable to take part in society. Would a vaccine passport set us free to return to work and our social lives or expose and deepen the current unequal enjoyment of good health?

What should we do?

The round table is not going to be the forum for solving the issue of greater scrutiny and accountability around the use of data and technology across the UK health system. But we will explore:

  • our collective understanding and concerns about how data and technology are used in health and how these things play out in our own lives and communities;
  • ways we can stay informed about how data and technology are being developed and deployed;
  • what could a network capable of holding institutions and organisations to account for their use of data and technology look like?
  • who else should be included in this discussion?

Key Terms

Data: curated information such as numbers, words, pictures collected for a specific purpose.

Technology: digital tools and systems (sometimes further enabled by data and machine learning) capable of delivering or supporting specific tasks.

Algorithms: a list of rules to follow in order to solve a problem. (See this great presentation from Edafe).

Artificial Intelligence (AI): A catch-all phrase that can be misleadingly used. It covers everything from the aspirational (and currently unachieved) idea of a machine capable of general intelligence and true learning, e.g. RoboCop — through to what is often called narrow AI, where machines are very good at a single task like playing chess.

Machine Learning: a branch of (narrow) AI where computer algorithms discover patterns within data and use them to build models that applies to new data that it is presented with. Accessible guide here

Data Science: A subject/field of practice that transforms data into information, most commonly, by using machine learning techniques. Accessible guide here

We ran roundtables in March 2021 connecting UK Black communities around Data, Tech &: Education, Crime & Justice, Employment & Enterprise, Health, and a final presentation.

View our presentation here: Roundtable Presentation

Data, Tech & Black Communities is a project funded by The National Lottery Community Fund. We respect your privacy.

--

--

Edafe Onerhime
Data, Tech & Black Communities

Edafe Onerhime specialises in making impact with data. Her motto: Data + Design + Culture. She lives in Glasgow, Scotland with her wife and cat. She/Her.