<p>As digital autonomy becomes a central expectation of modern healthcare, the ability of patients to permanently delete their electronic health data raises urgent ethical, clinical, and operational concerns. This educational review explores the multifaceted implications of patient-directed data deletion across five core domains: patient autonomy, clinical care, research integrity, health system administration, and future-ready policy design. <b>We distinguish between a right to control health information and a proposed right to permanently delete it</b>,<b> and we situate deletion requests within real-world motivations such as stigma reduction</b>,<b> correction of persistent inaccuracies</b>,<b> and distrust of institutional governance. </b>&#xa0;[<CitationRef CitationID="CR1">1</CitationRef>]While autonomy and privacy are foundational values, the permanent loss of medical data can disrupt treatment continuity, increase clinical risk, degrade research quality, and undermine institutional compliance. Current legal frameworks, such as HIPAA (U.S.), GDPR (EU), and PHIPA (Canada), generally require long-term record retention, reflecting the critical role of health data in care and accountability. <b>We clarify the regulatory treatment of anonymized data under these regimes and distinguish legal permissibility from ethical legitimacy in post-deletion data retention.</b> Emerging debates, such as the 2025 autism registry controversy, illustrate how public distrust can escalate when data policies are unclear or misaligned with patient expectations. The review outlines potential policy responses—including tiered deletion models, de-identified data retention, metadata-based audit trails, and transparency tools—to reconcile patient rights with collective health system responsibilities. <b>Drawing on parallels with structured implementation of clinical AI systems</b>,<b> we argue that deletion frameworks require staged governance</b>,<b> workflow redesign</b>,<b> education</b>,<b> and auditability to prevent unintended safety risks</b>. Ultimately, we argue for a balanced, future-facing approach to health data governance—one that protects both individuals and the broader goals of safety, equity, and scientific progress.</p>

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Patient-Initiated Permanent Deletion of Their Electronic Health Record Data: implications for Artificial Intelligence and Big Data in Healthcare

  • Clyde T. Matava,
  • Yvonne Fahy,
  • Asad Siddiqui,
  • Gregory Johnson,
  • Melissa McCradden,
  • Allan F. Simpao

摘要

As digital autonomy becomes a central expectation of modern healthcare, the ability of patients to permanently delete their electronic health data raises urgent ethical, clinical, and operational concerns. This educational review explores the multifaceted implications of patient-directed data deletion across five core domains: patient autonomy, clinical care, research integrity, health system administration, and future-ready policy design. We distinguish between a right to control health information and a proposed right to permanently delete it, and we situate deletion requests within real-world motivations such as stigma reduction, correction of persistent inaccuracies, and distrust of institutional governance.  [1]While autonomy and privacy are foundational values, the permanent loss of medical data can disrupt treatment continuity, increase clinical risk, degrade research quality, and undermine institutional compliance. Current legal frameworks, such as HIPAA (U.S.), GDPR (EU), and PHIPA (Canada), generally require long-term record retention, reflecting the critical role of health data in care and accountability. We clarify the regulatory treatment of anonymized data under these regimes and distinguish legal permissibility from ethical legitimacy in post-deletion data retention. Emerging debates, such as the 2025 autism registry controversy, illustrate how public distrust can escalate when data policies are unclear or misaligned with patient expectations. The review outlines potential policy responses—including tiered deletion models, de-identified data retention, metadata-based audit trails, and transparency tools—to reconcile patient rights with collective health system responsibilities. Drawing on parallels with structured implementation of clinical AI systems, we argue that deletion frameworks require staged governance, workflow redesign, education, and auditability to prevent unintended safety risks. Ultimately, we argue for a balanced, future-facing approach to health data governance—one that protects both individuals and the broader goals of safety, equity, and scientific progress.