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Diving into the challenges of data quality in women's healthcare, exploring real-life stories that highlight the critical need for accurate data collection and its impact on diagnoses, treatment, and pharmaceutical advancements. Shedding light on the persistent issues of gender bias in healthcare and advocating for the importance of quality data.

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DATA QUALITY IN WOMEN'S HEALTHCARE

Hi! My name is Gloria Azum and this blog is about diving into data quality challenges in women's healthcare, exploring real-life stories that highlight the critical need for accurate data collection and its impact on diagnoses, treatment, and pharmaceutical advancements—shedding light on the persistent issues of gender bias in healthcare and advocating for the importance of quality data. Note, that the names in these stories have been altered to preserve their anonymity.

Also, I must give a heads-up regarding my style of blogging, it is the watered-down version of the technicalities and numbers, not its entirety comedic but to keep readers interested, as somebody who reads research journals and applications docs or manuals, I know how boring they get sometimes with all the technicalities, especially when it is not your field or expertise, so I hope whoever reads them, enjoys it and get the concepts. They help give an insight into the importance of quality data in women's healthcare and what it is about.

I chose this approach in hopes of inviting a broader audience to appreciate the significance of accurate data collection and analysis in healthcare, particularly in addressing gender bias and advancing treatments in the process. As I stated earlier, to highlight these issues without overwhelming readers with technicalities. Enjoy!



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Diving into the challenges of data quality in women's healthcare, exploring real-life stories that highlight the critical need for accurate data collection and its impact on diagnoses, treatment, and pharmaceutical advancements. Shedding light on the persistent issues of gender bias in healthcare and advocating for the importance of quality data.

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