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What would healthcare look like if no one was left behind? Behind every statistic or piece of data related to healthcare inequality is an individual story of someone working to make healthcare a more equitable place. Join Havas Lynx Group for a panel discussion as they explore how making healthcare more inclusive can reach more people, bring effective medicines to market, support more patients, and maybe even improve commercial and clinical outcomes.
Women’s health
Claire Gannon
(Medical Writer, Havas Lynx Group)
Nur Pirbhai
(Medical Writer, Havas Lynx Group)
Imagine if you ordered a pint and you got given a half. Or if the taxi taking you home only took you halfway. Would you think it was a great service? Probably not.
Yet when our healthcare system sees ‘women’s health’ and ‘women’s problems’ as purely ‘women’s challenges’, that’s what’s happening. We see an under-representation of women in medical leadership, and infrastructure that isn’t built to meet the needs of half of the population.
By thinking of women’s healthcare as intrinsic to human healthcare, we can open avenues of research and opportunities to improve healthcare that have been closed before.
Yet when our healthcare system sees ‘women’s health’ and ‘women’s problems’ as purely ‘women’s challenges’, that’s what’s happening. We see an under-representation of women in medical leadership, and infrastructure that isn’t built to meet the needs of half of the population.
By thinking of women’s healthcare as intrinsic to human healthcare, we can open avenues of research and opportunities to improve healthcare that have been closed before.
This talk is biased
Claire Passmore
(Associate Director of Scientific Services, Havas Lynx Group)
For evidence-based medicine, we require, well, evidence. Or, in an increasingly digital world, data.
But how we collect that data, who from, and what we do with it matters. When the biases in everyday life creep into things we see as inherently impartial (like data collection and analysis), it affects the results.
In healthcare, bias affects who gets diagnosed, who gets treated and who benefits from treatment. But if we’re aware of those biases we can address them, and design healthcare that actually works for everyone.
But how we collect that data, who from, and what we do with it matters. When the biases in everyday life creep into things we see as inherently impartial (like data collection and analysis), it affects the results.
In healthcare, bias affects who gets diagnosed, who gets treated and who benefits from treatment. But if we’re aware of those biases we can address them, and design healthcare that actually works for everyone.
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