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New Series on THCB — The Health Data Goldilocks Dilemma: Privacy? Sharing? Both?
Once upon a time, there lived a little girl whose name was Goldilocks. She was a wise girl who was aware that there was great value in health data. One day she decided to go for a walk in the forest of the U.S. healthcare system.
Goldilocks learned that there are risks of TOO LITTLE health data being shared:
- That she and her care providers would not have the best information for clinical decision making
- That clinical researchers would be stifled from conducting groundbreaking analyses and studies
- That next generation technologies, which rely on vast quantities of data (e.g., AI and machine learning) could be suffocated
- That the promises of personalized medicine would be repressed
She also learned that there are risks of TOO MUCH health data being shared:
- That her privacy and personal safety could be violated
- That trust in care providers and the healthcare system would be eroded
- That the value created by health care data would be captured by third parties, e.g., large technology companies
The Goldilocks Dilemma has U.S. policymakers driving toward two seemingly conflicting goals:
- Broader data interoperability and data sharing, and
- Enhanced data privacy and data protection.
On The Health Care Blog, my colleague Deven McGraw and I are hosting a new ongoing series to explore the Health Data Goldilocks Dilemma.
On the Roadmap Page to the series you’ll find:
- The Scope of the Series
- A list of Posts Published and Pending
- An Invitation to Guest Authors
- Brief Bios of the Series Hosts – Vince Kuraitis and Deven McGraw
- An Updated List of Congressional Privacy LegislationÂ
The Health Data Goldilocks Dilemma has many tentacles: Federal/State privacy legislation; health IT tech, policy & interoperability; data for AI & machine learning; data for clinical research; ethical issues; compensating individuals for their data; health data business models & many others.
We’d love to see contributions from guest authors. Please consider commenting and sharing your thoughts.
This work is licensed under a Creative Commons Attribution-Share Alike 3.0 Unported License. Feel free to republish this post with attribution.