A forthcoming nonfiction book examines how an insured patient was classified as self-pay inside a major healthcare institution and was told he would not be seen unless he paid a $5,000 pre-service deposit, a dispute that ultimately produced a consumer fraud finding in arbitration.
Thomas E. Zordani traveled from Denver to Mayo Clinic Arizona seeking answers for a serious neurological condition, according to the announcement of his book, Classification: How an Insured Patient Was Made Self-Pay—and Took on Mayo Clinic and Won. He had provided his insurance information, supplied hundreds of pages of medical records, and been told Mayo was “good to move forward.”
He never saw the clinician. After arriving for his appointment, Zordani was sent to the Business Office and told he would not be seen unless he paid the $5,000 deposit. “I did not go to Mayo looking for a case,” Zordani writes in the book. “I went to Mayo Clinic to be treated and, I hoped, healed.”
The following morning, the story changed. Zordani discovered a Mayo estimate showing an expected patient cost of $565. The same document identified his coverage as “Self-Pay.” He had active insurance, had provided that insurance to Mayo, and intended to use it.
What began as a failed medical appointment became an investigation. Drawing on nearly three decades of experience analyzing organizational systems, benefits, compliance, risk, and decision processes, Zordani began reconstructing what had happened inside Mayo. He compared recordings against written notes, insurance information against internal classifications, telephone records against claimed contact attempts, and financial estimates against the $5,000 requirement that ultimately controlled access to care.
“I had gone to Mayo looking for answers about what was happening inside my body,” he writes. “I left with an entirely different question: What had just happened inside Mayo?”
The imbalance of the fight became part of the story. Zordani was one patient pursuing a dispute worth only several thousand dollars against one of the most recognized healthcare institutions in the world—an organization with vast resources, sophisticated legal representation, and a reputation that made some attorneys hesitant to engage.
The economics alone could have ended the matter. Instead, Zordani continued building the record, working with a small Arizona legal team while tracing the people, processes, classifications, communications, and documents behind the encounter.
The dispute eventually reached arbitration. On September 12, 2025, the arbitrator issued a written decision stating: “I find for the Plaintiff on his consumer fraud claim.”
The finding changed the character of the dispute. Classification reconstructs how one patient's challenge to a $5,000 demand grew into an examination of insurance transactions, financial classifications, recorded calls, internal records, legal correspondence, discovery, testimony, and what Mayo's own records showed when placed beside the institution's explanations.
For Zordani, the book's significance extends beyond his own case. “Most patients do not arrive at a hospital prepared to investigate a classification, challenge a financial workflow, or reconstruct what happened inside a revenue-cycle system,” Zordani said. “They are there because they need care. If my experience can help patients recognize when something does not make sense—and encourage institutions to examine the systems producing those decisions—then telling this story serves a purpose beyond what happened to me.”
The announcement frames the book around a broader question: what happens when a label inside a complex system begins controlling real-world consequences before anyone stops to ask whether the label is right? The implications reach patients, providers, and the revenue-cycle processes that determine who is billed as insured and who is treated as self-pay.
Classification: How an Insured Patient Was Made Self-Pay—and Took on Mayo Clinic and Won will be released November 1, 2026. Zordani is described as a systems-risk analyst, writer, and veteran HCM and PEO professional whose work focuses on tracing visible outcomes back to the rules, processes, classifications, and decision structures that produced them.
