Self-Healing Health Records: Autonomous Data Integrity Models for Corruption-resistant Electronic Medical Records (EMR)
Seun Michael Oyekunle, Oluwaseun Oladeji Olaniyi, Emonena Patrick Obrik-Uloho, Olufisayo Juliana Tiwo, Rukayat Oluwabukola Olasege
Journal of Engineering Research and Reports · pp. 58–75 · Published 21 Oct 2025
10.9734/jerr/2025/v27i111685Abstract
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.
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