By Alicia Arrick, Chief Growth Officer, P-n-T Data Corp.
LinkedIn: Alicia Arrick
LinkedIn: P-n-T Data Corp.
AI is no longer a futuristic concept. It’s here, and it’s reshaping everything from claims processing to radiology. But with great power comes the great responsibility of safeguarding the integrity, privacy, and security of healthcare data in a rapidly evolving digital landscape.
That tension — the promise of AI alongside the rising stakes of data security — was front and center at the virtual summit “Measuring the Impact of AI in Healthcare,” hosted by HealthIMPACT Live and Answers Media Network.
One session explored how leading payers and providers are addressing these challenges through advanced data logistics. Leading the conversation were P-n-T Data Corp Board Chair, Dr. Mark Boxer and Chief Strategy & AI Officer, Jeffrey Eyestone, alongside moderator Matthew Fisher, partner at Hancock Daniel.
AI in Healthcare: From Science Experiment to Scaled Reality
Kicking off the discussion, Eyestone emphasized that AI is a present reality, transforming how payers and providers manage claims, prior authorization, and other critical workflows.
Administrative and financial use cases, such as claims adjudication, prior authorization, and payment integrity are benefiting from tools that can process unstructured data, summarize clinical notes, and reduce friction between stakeholders.
On the clinical side, Dr. Boxer likened the state of AI in healthcare to a tale of two cities: full of promise but also accompanied by risk. AI is enabling earlier disease detection, faster radiology reads, and personalized treatment planning, advancements that are improving patient outcomes in real time. But he cautioned that the same technology is also creating new cyber risk profiles, particularly at the intersection of AI and emerging technologies.
A New Security Paradigm: You Can’t Steal What You Don’t Have
Traditionally, healthcare organizations have sought to collect and store data to fuel analytics. But that model increases exposure. P-n-T Data Corp. has taken a different approach: facilitating the secure movement of data without ever retaining protected health information (PHI).
As Dr. Boxer emphasized, if an organization doesn’t retain sensitive data, that data can’t be stolen — a core principle behind P-n-T Data Corp’s approach. By eliminating data retention, organizations can dramatically reduce risk while still enabling AI-powered insights. The challenge then shifts to ensuring data integrity including accuracy, completeness, and consistency from creation to use through robust governance, auditing, and monitoring.
Vendors as the Extended Enterprise
Another theme that emerged was the growing importance of vendor management. Breaches increasingly occur through third parties, making vendor ecosystems a primary attack vector.
The panel urged organizations to treat vendors as part of the extended enterprise, requiring continuous diligence rather than one-time checklists. That includes evaluating how vendors manage security, privacy, and intellectual property in an era of generative AI and agentic solutions.
Boxer stressed that adopting AI is not about abdicating responsibility but about adding new layers of it, particularly ensuring that data flows into and out of models with integrity and trust.
Real-World Use Cases
The conversation also surfaced practical examples where AI is delivering measurable value today:
- Claims & Prior Authorization: AI can pre-validate submissions against plan rules, summarize clinical documentation, and reduce denials — saving time and administrative costs.
- Population Health: Aggregating payer and provider data helps identify at-risk patients, close care gaps, and optimize resource allocation.
- Clinical Care: From breast cancer diagnosis to diabetes detection, advanced models are putting evidence-based insights into the hands of physicians faster than ever.
Still, the panelists cautioned that adoption must be paired with governance and oversight. Patient-facing chatbots, for example, can support self-care but also risk misdiagnosis if not guided by clinicians.
Looking Ahead: Building Trust Through Data Logistics
The discussion closed on a forward-looking note. To truly scale AI in healthcare, payers and providers must look beyond the technology itself and invest in the data logistics that make AI work securely and responsibly:
- Data governance that prioritizes integrity and transparency.
- Vendor diligence that treats partners as part of the enterprise.
- Security models that reduce exposure by minimizing data retention.
- Clinical oversight to ensure AI augments rather than replaces human judgment.
It’s not about whether healthcare will use AI, it’s about how. The organizations that succeed will be those that combine cutting-edge innovation with thoughtful risk management — making data logistics the backbone of secure, trusted healthcare transformation. Watch the full recording below.