Self-Healing Health Records: Autonomous Data Integrity Models for Corruption-resistant Electronic Medical Records (EMR)
Seun Michael Oyekunle
*
Ekiti State University, Ado-Iworoko Road, P.M.B. 5363, Ado-Ekiti, Ekiti State, Nigeria.
Oluwaseun Oladeji Olaniyi
University of the Cumberlands, 104 Maple Drive, Williamsburg, KY 40769, United States of America.
Emonena Patrick Obrik-Uloho
Prairie View A&M University, 100 University Dr, Prairie View, TX77446, United States.
Olufisayo Juliana Tiwo
University of Lagos, University Road Lagos Mainland Akoka, Yaba, Lagos, Nigeria.
Rukayat Oluwabukola Olasege
Ottawa University, 1001 South Cedar Street, Ottawa, KS 66067, United States.
*Author to whom correspondence should be addressed.
Abstract
This study investigates self-healing health records through the development of autonomous data integrity models for corruption-resistant Electronic Medical Records (EMRs). Using a convergent parallel mixed-methods design, a systematic review of 204 studies (2010–2025) identified key EMR integrity challenges, notably data completeness and accuracy issues. Comparative analysis of 72 self-healing technologies revealed database-level systems as most effective, demonstrating high reliability and rapid recovery performance. A four-phase framework: Detection, Diagnosis, Recovery, and Learning was developed, reducing error recurrence by over 60%. Case studies, including implementation at the Cleveland Clinic, confirmed significant improvements in patient safety and cost efficiency. While integration complexity and moderate user acceptance remain challenges, the framework provides a scalable, evidence-based foundation for autonomous EMR systems. This research advances healthcare informatics by enhancing data integrity, promoting patient safety, and paving the way for intelligent, self-correcting health record infrastructures.
Keywords: Self-healing systems, electronic medical records, data integrity, autonomous recovery, healthcare informatics