Why Healthcare Data Sharing is Broken – and What Comes Next
This five-part blog series from Jeffrey Eyestone, Chief Strategy and AI Officer at P-n-T Data Corp., explores why healthcare’s data sharing infrastructure is inadequate and how a new, agent-based model is helping providers, payers, and partners move beyond security and scalability limitations in order to improve key data-centric workflows like claims and prior authorization adjudication, AI and analytics, and more. Drawing on real-world insights from AI deployments and next-gen platform design, the series explores the link between healthcare data sharing challenges and improved data engineering.
Healthcare data sharing is inadequate for numerous data-centric workflows. Despite decades of investment and technological advancement, the industry continues to struggle with inefficient infrastructure for data sharing, resulting in security gaps, poor interoperability and data quality. Furthermore, data integrity is at stake: your personal health information (PHI) is all over the place, raising security risks (“you” being your company and your personal data) – and your data is even being used and sold out from under you by legacy solution providers.
The Data Breach Landscape…and more
The numbers don’t lie:
- In just the last nine years, more than 815 million healthcare records have been exposed with some recent high-profile data breaches in the news.
- Healthcare breach costs are now 60% higher than in any other industry.
Let’s be blunt: legacy solution providers aren’t solving the problem — they are perpetuating the problem.
Beyond data breach issues there are still significant issues with interoperability and data quality that get in the way of numerous data-centric healthcare workflows.
Current Models are Failing
Despite billions of dollars invested into cybersecurity and IT modernization, we still see:
- Weak data security foundations that leave organizations exposed to breach and compliance risk
- Friction-filled workflows caused by poor data quality and transformation bottlenecks
- AI and analytics initiatives that stall due to data security, data integrity and risk management issues
It Doesn’t Have to be This Way
Post-n-Track Gen 3 was designed around a fundamentally different principle: zero long-term data residency, no ownership liability, data integrity, and real-time performance. By eliminating the need to retain sensitive health information, Gen 3 minimizes risk while maximizing control, compliance, and trust.
Here’s how that translates into real-world impact:
- Zero long-term PHI/PII residency drastically reduces breach risk and compliance exposure
- No data ownership model eliminates liability and compliance complexity
- Real-time transaction processing offers fast delivery and superior performance tracking
- Agent-based architecture enables intelligent data access, quality, transformation, and governance
What’s Next in This Series…
I’ll dive deeper into what makes Gen 3 different:
- Post 2: “Why the Gen 3 Agent-Based System Outperforms Legacy Healthcare Solution Providers”
- Post 3: “High-Value Use Cases That Legacy Solution Providers Do Not Enable”
- Post 4: “Why Healthcare AI Initiatives Fail – The Data Engineering Problem No One Talks About”
- Post 5: “Post-n-Track Gen 3 Addresses Your Most Pressing Data Sharing Needs”
Ready to challenge everything you thought you knew about healthcare data sharing? It starts by admitting current approaches are flawed — and it ends with a smarter, more secure, and more scalable model. At P-n-T Data, we’re rethinking what’s possible in healthcare data sharing.
What would your organization do with a platform designed for the future, not the past?
Jeffrey Eyestone is Chief Strategy and AI Officer of P-n-T Data Corp.