Due to the growing quantity of medical images, the patient data recognition is the important one for treatment clinical institutions. So, digital image watermarking has widespread importance at the time of sharing of medical images among treatment clinical institutions. But in watermarking the extraction of the patient data and preserving image quality are the major important factors, after sharing of medical images. In this paper, a novel spatial domain-based reversible digital image watermarking scheme proposed on medical images to ensure authenticity, integrity, and confidentiality. In this scheme, the input ground truth image (or cover image) is divided into 4 X 4 block sizes. The watermark image is embedded with the original ground truth image by performing L-level Quantization. The watermark image is quick response (QR) code of patient information. The compression applied to the remainder part of the cover ground truth image using the mean arithmetic method to make the process most robust. The reversible watermarking scheme applied over watermarked images by performing inverse L-level Quantization and decompression. The proposed scheme is compared with Reza et al. existing scheme: “An Image Watermarking Algorithm for Medical Computerized Tomography Images”. The proposed scheme has better experimental results compared to the existing scheme in terms of Peak Signal-to-noise ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index (SSIM). The PSNR, MSE, and SSIM metrics are used to evaluate the robustness of the proposed scheme for extracting cover images and QR code of patient information (in terms of watermark images) from watermarked images. In addition, the Similarity (SIM) and the correlation coefficient (CRC) are used to evaluate how well the suggested approach for patient data and cover ground truth medical image extraction holds up robustness. The experimental numerical values of metrics achieved in the proposed scheme have better performance for compression and decompression watermarking medical images.