<![CDATA[Electronic Patient Records (EPRS) represent critical elements of digital healthcare systems, as they contain confidential and sensitive medical information essential for patient care and clinical decision-making. Due to their sensitive nature, EPRs frequently face threats from unauthorized intrusions, security breaches and malicious attacks. Safeguarding such information has emerged as an urgent concern in medical data security. Steganography offers a compelling solution by hiding confidential data within conventional carrier objects like medical imagery. Unlike traditional cryptographic methods that merely alter the data representation, steganography conceals the existence of the information itself, thereby providing discretion, security, and resilience against unauthorized disclosure. However, embedding patient information inside medical images introduces a new challenge. The method must maintain the image's visual fidelity to prevent compromising diagnostic precision, while ensuring reversibility for complete restoration of both original imagery and concealed information. To address these challenges, this research proposes MedProtect, a reversible steganographic framework customized for medical applications. MedProtect procedure integrates pixel interpolation techniques and center-folding-based data transformation to insert sensitive records into medical imagery. This method combination ensures accurate data recovery of the original image while maintaining the image quality of the resulting image. To clarify the performance of MedProtect, this study evaluates two well-established image quality metrics, Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The discovery shows that the framework achieves PSNR values of 48.190 to 53.808 dB and SSIM scores between 0.9956 and 0.9980. These outcomes display the high level of visual fidelity and imperceptibility achieved by the proposed method, underscoring its effectiveness as a secure approach for protecting electronic patient records within medical imaging systems.]]>
