Dennis Timp’s Post

I was excited to see this recently released report from the Center for Law and Social Policy (CLASP) illuminating the crucial role of comprehensive and rigorous race and ethnicity data in the pursuit of health equity. "Combatting entrenched health disparities demands a concerted, cross-sectoral approach involving healthcare providers, insurers, researchers, community organizations, advocacy groups, and policymakers. This collective endeavor hinges on the availability of robust, transparent data on race and ethnicity to establish a common foundation for understanding and assessing interventions aimed at promoting health equity. Encouraging stakeholders to embed principles of data equity into their practices, CLASP and the Data Equity Coalition advocate for enhanced data standards across governmental and healthcare spheres." At its core, data serves as the linchpin for comprehending the scope and nuances of health inequalities. I've seen a lot of movement by health systems and public agencies toward collecting more meaningful race and ethnicity data, but we still have a long way to go. #healthequity #dataequity https://lnkd.in/g82f-BGf]

Just finished reading 'Storytelling with Data' by Cole Nussbaumer Knaflic! 📚 It was such a great read that I nearly finished it in one evening. I've learned a lot about data visualization and decided to apply my learnings to create a chart on gender segregation in clinical laboratories. The healthcare industry is female-dominated. Women make up 78.7% of the workforce in healthcare (www.bls.gov). Clinical laboratory jobs are similar, with 75.2% positions filled with women. These numbers are much higher than the average percentage of women in all jobs, which is 46.8%. I performed a z-test to compare the proportion of women in medical laboratories to the proportions of women in the total workforce and in healthcare. There is a significant difference in the proportion of women between laboratory technologists/technicians and the total workforce. However, there is no significant difference between the proportion of women in healthcare and medical laboratories. I think gender segregation is a remnant of the past that affects everyone. Fixing this requires cultural and political efforts. I believe the first step is raising awareness. The book can improve communication with data on any topic. I highly recommend it to anyone. As I continue to learn, I am eager to hear your feedback.

The long-awaited update to federal standards for collecting race and ethnicity data by the Office of Management and Budget (OMB) is a significant step forward. This revision, the first since 1997, aims to improve the accuracy and inclusivity of health data collection. The Blue Cross Blue Shield Association (BCBSA) and the National Minority Quality Forum (NMQF) established the Data Equity Coalition in September 2023. The coalition advocated for clearer data collection standards, including race, ethnicity, language, sexual orientation, and gender identity. They also emphasized the importance of disaggregated race and ethnicity data collection and the uniform adoption of the updated OMB standard across all healthcare stakeholders. Learn more: https://hubs.ly/Q02rvf9G0

🖥️ Join us for the launch of the "Women in Health Data" meetup and connect with a community of passionate data professionals in health & care, with guest speakers, insights and more! To register, follow the link in the comments or get in touch. #WomenInHealthDataNetwork #DiversityInData AphA Analysts Cambridge Spark

Women with self-identified ER+, HER2- #MetastaticBreastCancer (MBC) commute an average of 25.4 miles to their oncologist’s office, according to a recent survey. Learn more about other survey findings on access to care: https://bit.ly/3zbhu6E

👋 I'm sharing with you all insights from the #EndGenderBias Survey Results Summary Report, shedding light on crucial aspects of gender bias in healthcare. https://lnkd.in/gvMw4jka Over the last few weeks, I have been seeing more and more articles and studies about women’s health, how women are disproportionately affected by barriers in the current healthcare system and the lack of knowledge we have on how health issues both present themselves and are treated in women. This March the National Women’s Health Advisory Council conducted a survey aiming to understand the experiences of Australian women in navigating the healthcare system. The findings are not just eye-opening but also serve as a call to action for systemic change. Here are some of the key drivers of inequity in healthcare:  👩⚕️ Interpersonal experiences, high rates of being dismissed and disbelieved by healthcare providers 🚺 Structural barriers, affecting the accessibility and affordability of health care. 🔍 The evidence base underpinning health care and medical innovation - research has been conducted on predominantly male groups, and female groups often present and respond to treatments differently. This gap not only perpetuates bias but also hampers advancements in healthcare innovation. 💡Intersectional experiences of gender bias - related to other aspects such as age and rurality bear more of this burden. 🌐 As a result of each layer of gender bias, gender bias in health care has far-reaching impacts on women’s lives. Additionally, the study includes expert stakeholders (medical professionals) who expressed concerns about women's access to safe and choice-driven healthcare, with many perceiving inadequate access and choice for women. So, what's the next step?  Let's amplify these findings, spark meaningful discussions, and work together towards a future where gender bias in healthcare is no longer a barrier to women's well-being. Together, we can make a difference! 💪 #HealthEquity #WomenInHealthcare #GenderBias #HealthcareReform

July reading list! The first few lines of the preface capture you straight away, and a plethora of fact-based insights take you through the reality of the significant data gaps and underrepresentation of women in data sets #data #databias #womenindata

Data quality is a crucial aspect of providing equitable outcomes for patients. Many organizations are unaware of the degree to which patients are excluded or not provided the same quality of care, and that lack of information contributes significantly to an organization's ability to provide care. If we aren't measuring where the lapses in care actually are, it is nearly impossible to make the needed corrections and make a real difference for patients. #HealthEquity #PatientOutcomes #Data

1,000,000,000,000 That's the potential boost to the global economy by 2040 if we close the women's health gap. Many assume that the women’s health gap primarily manifests later in life. It’s also often connected to women’s living longer on average. But… We've found that majority of the gap appears during women's prime working years.👩💻 Over half the women in tech leave the industry by the midpoint of their careers** More than double the rate of men** Not addressing this could have huge economic implications. Because if women were healthier, they could be more productive.🤗 Female biology is different from male biology—beyond the reproductive system. Some diseases manifest differently in women than in men. Raising more awareness and addressing those differences in healthcare can result in better health and economic outcomes.❤️ *McKinsey & Company | Lucy Perez: Improving women’s health could improve the world’s economy ➡️https://lnkd.in/eH3Bv8-s   **See our post on this topic ➡️https://lnkd.in/eJkF7V-J

Our Premenstrual Symptoms Impact Survey (PMSIS) is an important #COA tool in the field of #womenshealth. Read more about how the survey helps measure health-related quality of life (HRQoL) for women with #PMS and #PMDD: https://lnkd.in/gJzwE46c #COA #patientinsights