Phil Groom explains why he believes digitised lateral flow testing has a role to play in creating greater equality in healthcare
In her book, Invisible Women: Exposing Data Bias in a World Designed for Men, the author Caroline Criado Perez highlights how modern life has been created, based on skewed information.
From car design through to city planning and medical protocols, ‘averages’ and ‘average uses’ have traditionally been based on male data. More specifically, she suggests, they’re likely to be based on white, male, middle-class data.
This isn’t necessarily part of a deliberate ploy to discriminate against women. It’s more that inventors, engineers, researchers, doctors and thousands of other creators and designers routinely rely on data sets that don’t feature female human beings.
Data gaps permeate society – and healthcare
Criado Perez calls this absence of women in data sets a ‘data gap’. And these gaps have come about for a number of reasons.
In pharmaceutical research, for example, Criado Perez says women have traditionally been seen as tricky test subjects. Childcare commitments can mean it’s more difficult to enrol women for drug trials. And because of reproductive cycles, women’s reactions to treatments and medications can be variable, so it can be easier for scientists to discount their ‘outlier’ data, or to ‘simplify’ trials by only inviting men to participate.
In the author’s words, this means women “routinely take drugs that are not suitable for their bodies”. It also means that recognised symptoms and medical protocols are weighted towards male, rather than female, experiences.
For example, as recently as 2017, researchers highlighted that because men and women can have very different symptoms of heart attack, women may not get life-saving help when they need it. Tracey Keteepe-Arachi and Sanjay Sharma reported: “There is a lack of gender-specific evidence due to the under-representation of women in clinical trials and a long-held myth that CVD [cardio vascular Disease] is limited to men.”
Data gaps entrench disadvantage into approaches to care
These data gaps don’t just affect women. They also disadvantage non-white ethnicities. For example, until 2020, there was no medical reference data on how diseases presented on black and brown skin.
Malone Mukwende, a second year medical student at St George’s, University of London, – realised he was learning how to identify rashes and skin disease on white skin only. None of the textbooks contained information or images on what disease looked like on black skin. This means that medical students weren’t being trained to recognise illnesses including skin cancer, Kawasaki disease, meningitis and even Covid-19 on dark skin.
Mukwende published a book in 2020 called Mind the Gap: A Handbook of Clinical Signs in Black and Brown Skin.
Data gaps are a big reason that disadvantage becomes entrenched in approaches. So what can be done to fill them – and level out inequalities in doing so?
Filling the data gaps
There are many factors that lead to data gaps. Some are attitudinal, some relate to unconscious biases and some are probably down to a simple lack of creative thinking. Others still are due to human error – like the millions of test records that have been lost in the NHS Track and Trace system.
It’s not that healthcare researchers and professionals can’t access data. We live in an age where data is everywhere – and when health services like the UK’s NHS are partnering with digital giants like Google to improve patient outcomes.
The problem is that available data isn’t representative of the whole population – and this is where we believe digitised lateral flow testing has an important role to play.
Digitised lateral flow testing makes self-reporting more acceptable to scientists who believe reliable results can only be obtained in controlled environments.
Medical professionals are scientists who’ve been trained to trust data obtained in controlled lab or hospital environments. They’re less trustful of self-reported data, which are argued to be subjective, unreliable and prone to bias.
This means that for their data to form part of the bigger picture, individuals have to be able to get to a lab – and of course, be invited to it in the first place. This centralisation of data gathering is one of the reasons gaps appear.
Lateral flow tests, on the other hand, decentralise subject testing. They can be made available via the post, in workplaces, in schools and universities, via community hubs, pharmacies and over-the-counter. Tests can be carried out at home, at work, at school – in fact in hundreds of places that are not a lab. As well as providing a significantly cheaper way to test than a lab, they also provide a standardised way of assessing results that aren’t subject to reporting biases.
And when tests are integrated with a data management system like Transform®, they can capture additional data, ranging from subject age, gender, location and ethnicity – through to symptoms and co-existing conditions. Data privacy is safeguarded – so that only a GP can connect results to a particular patient.
Digitised lateral flow testing to increase available data and understanding
This use of technology allows data to be gathered reliably at a very large scale outside of traditional lab conditions. And this means that it can be sourced from more types of people.
In the future, regular digitised lateral flow testing could be used to increase our understanding of poorly understood conditions and life stages, such as menopause.
A report in Nature in 2015 said: “Menopause is a complex, multifactorial process and the exact steps leading to the loss of ovarian function are incompletely understood. More research and a better understanding of the underlying mechanisms would be helpful to develop new diagnostic and therapeutic options.
“Unfortunately menopause is not a priority on the agenda of many pharmaceutical companies and funding agencies. Menopause affects all women, some of them for decades, and more safe and effective therapies are needed.”
Lateral flow tests are ideal for assessing hormone levels. They’re easy to use and they’re relatively cheap. So this is just one of the data gaps that could be bridged with technologies including digitised lateral flow testing.
If you’re interested in the potential of digitised lateral flow testing to create a more equal approach to health data gathering, let’s talk