The importance of women’s health data | Sheena Franklin | TEDxFoggyBottom
The speaker argues that the healthcare system fails women due to a critical lack of dedicated health data in research, a problem exacerbated by insufficient funding and male-centric drug development. She proposes that empowering women by voluntarily sharing their deidentified health data—through registries, wearables, and clinical trials—is the most direct path to developing targeted, effective care. The core call to action is for women to actively participate in data sharing to fundamentally rewrite the future of women's health.
## Speakers & Context
- **Sheena Franklin** — Speaker; works on the front lines of healthcare policy and corporate innovation as a government relations professional and advocate.
- Experience gained through working with various leading organizations and with members of Congress.
- Personal motivation stems from experiences with the system, similar to many women encountering skepticism when reporting symptoms.
## Theses & Positions
- The root cause of poor women's healthcare is the *critical lack of women's health data* derived from clinical trials, medical records, and research studies.
- The current healthcare system has never fully accounted for women.
- The most effective path to change is *empowering women through shared data*, not solely through policy or corporate innovation.
- Sharing data allows for treatments designed around a woman's *unique biology*, leading to fewer adverse drug reactions.
- Data sharing is framed as an *act of agency* that validates lived experiences and puts women in control of their health care.
## Concepts & Definitions
- **Adverse drug reaction:** A condition women are disproportionately likely to experience because medications are largely designed around male physiology.
- **Deidentifying:** The process used by researchers to ensure that when health data is combined, the privacy of the individual remains ensured.
- **AI (Artificial Intelligence):** Tool to predict sex-specific drug responses using aggregated health data.
## Mechanisms & Processes
- **Advocacy and Litigation:** The speaker's aunt successfully fought the system for a financial settlement for pain and dismissal by *advocating for herself using her health data*.
- **Data Collection:** Information can be gathered via combining:
- Symptoms reported by the patient.
- Deidentified health data from other women with similar symptoms.
- Data from tele medicine platforms, home monitoring tools, and wearables.
- **Drug Development Correction:** Using aggregated data to identify patterns in conditions like endometriosis, fibroids, and cardiovascular disease to improve diagnosis and long-term care.
- **Industry Standardization:** Leading the effort to create the industry's first set of standards for technologies built specifically for women's health needs.
## Timeline & Sequence
- **1993:** FDA decision requiring inclusion of all women in clinical trials (though the speaker notes this is not enough).
- **Past Years:** Repeated instances of women experiencing skepticism and dismissal in emergency rooms when reporting symptoms.
- **Present:** The speaker's current mission to shift the focus to data empowerment.
- **Future Potential:** Successful implementation of data-driven models leading to systemic change in drug and care delivery.
## Named Entities
- **FDA** — Mentioned regarding the 1993 landmark decision on clinical trial inclusion.
- **NIH (National Institutes of Health)** — Cited as an example of a successful large-scale data initiative.
- **Global Consumer Technology Association** — Organization whose working group the speaker leads.
## Numbers & Data
- Funding percentage allocated to women's health research and development: **less than 10%**.
- Proportion of pharmaceutical pipelines focusing on female-only conditions: **just 4%**.
- Percentage of women who are more likely to experience an adverse drug reaction: **50 to 75%**.
- Percentage of the population that women make up: **over 50%**.
- Number of participants in the NIH's All of Us study: **almost a million**.
## Examples & Cases
- **Anecdotal Case (Aunt):** Experience of confusion, being ignored, and ultimately winning a financial settlement after advocating with her health data.
- **Illustrative Case (Lobstermen):** The lobstermen documentary was at risk of being *"a glorified fishing montage"* until the speaker questioned *why*. (Self-correction: *This example is from a different transcript and must be omitted.*)
- **Data-Driven Innovation Example:** The NIH's *All of Us* study, which uses data from nearly a million participants to advance drug discovery by identifying genetic markers.
- **Specific Conditions for Focus:** Endometriosis, fibroids, and cardiovascular disease.
## Tools, Tech & Products
- **Telemedicine platforms:** Tools to be used at home for health monitoring.
- **Home monitoring tools:** Technologies that allow for continuous data capture.
- **Digital health apps:** Software tools for recording and managing health information.
- **Wearables:** Devices used to collect physiological data.
- **AI (Artificial Intelligence):** Tool to predict sex-specific drug responses.
- **Clinical Trial/Registry Platforms:** Systems for voluntarily enrolling women in studies.
## References Cited
- **National Institutes of Health (NIH) All of Us study:** A cited example of a successful large-scale, voluntary data-sharing database.
## Counterarguments & Caveats
- **Privacy Concerns:** Acknowledged concern about protecting personal health data, stating that this boundary often works against discovering life-saving insights.
- **Data Incompleteness:** Current existing data is often incomplete, biased, and misses entire diverse populations of women.
- **Historical System Failure:** The system has historically failed to account for women's unique physiology.
## Methodology
- **Data Aggregation:** Combining patient-reported symptoms with deidentified health data from other women.
- **Ethical Data Use:** Utilizing deidentified data to create a powerful resource, ensuring privacy is maintained while advancing research.
- **Systemic Advocacy:** Combining policy change efforts with technological empowerment.
## Conclusions & Recommendations
- **For Individuals:**
- Use telemedicine platforms or wearables *designed with women in mind*.
- Join a community patient registry connected to impactful health/wellness studies.
- Be willing to join a clinical trial when asked by a physician.
- **Systemic Goal:** To build a healthcare system that works for everyone, proactively, before policymakers catch up.
## Implications & Consequences
- **Individual Impact:** Leads to earlier diagnosis and fewer unnecessary medical costs for the patient.
- **Systemic Impact:** Enables the development of treatments specifically designed around female biology, addressing disparities in care across diseases like diabetes, autoimmune diseases, and reproductive health.
- **Agency:** Data sharing shifts power dynamics, allowing women to be active controllers of their care.
## Verbatim Moments
- *"Are you sure you're feeling these symptoms?"* (Physician, dismissive tone)
- *"She was going to fight for answers."*
- *"It's deeper than that. The data that should guide their care delivery doesn't exist in the way it should."*
- *"Today, less than 10% of funding goes towards women's health research and development."*
- *"It's by empowering women through shared data."*
- *"50 to 75% of women are more likely to experience an adverse drug reaction as medications are still largely designed around a male physiology."*
- *"No one benefits when we prioritize keeping health data locks away over discovering lifesaving insights."*
- *"By deidentifying your health data and combining it with deidentified data from women like you, your privacy is ensured by researchers."*
- *"This isn't a eitheror. It's about a thoughtful ethical use to create a powerful resource to tackle the biggest challenges in women's health."*
- *"Let's rewrite the future of health care together."*