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Modern technological innovations and breakthroughs in the medical sector have significantly enhanced life expectancy. While smart healthcare systems are currently being transformed by artificial intelligence (AI) and the Internet of Things (IoT), the confidentiality and security of medical data continue to be a critical challenge. Reversible data hiding (RDH) is extensively investigated within medical environments to facilitate secure information transmission, as well as in fields such as cloud computing and satellite imagery transfer. The transmission of medical images serves as a cornerstone of the smart healthcare industry.For clinical images, even a marginal distortion during reconstruction can result in diagnostic errors by physicians, thereby endangering patient safety. Consequently, while numerous RDH methods have been developed, a limited number focus specifically on clinical imagery, particularly regarding high-quality DICOM formats.The introduced AdaBPE RDH framework provides a robust solution for securely sharing patient health reports (PHRs) and other confidential data with medical professionals. This strategy introduces a methodology that establishes an optimal balance between leveraging smooth pixels for maximized intra-block data embedding and ensuring lossless image reconstruction. Within this framework, an encrypted 16-bit DICOM image serves as the host medium to conceal the patient's confidential data. The algorithm divides the cover image into equally sized, non-overlapping blocks, adaptively embedding data into these encrypted sections based on the underlying characteristics of the original pixel values via an MSB prediction error approach.Performance evaluation is conducted using both 16-bit DICOM and 8-bit natural images. The proposed method successfully meets the core RDH benchmarks of BER = 0, PSNR = ∞, and SSIM = 1, yielding an average embedding capacity of 5.7067 bpp for premium medical images and 1.6769 bpp for natural scenes. The empirical findings demonstrate substantial advantages, outperforming existing state-of-the-art approaches.