Electronic medical record (EMR) systems possess the potential to enable smart healthcare by serving as a hub for the transformation of medical data into meaningful information, knowledge, and wisdom in the health care sector. This vision will only be realized after medical imaging, wearable devices, genomic data, sensor data, environmental data, and behavioral data are coupled with machine learning algorithms [1]. Some propose that transforming EMR data will result in more accurate diagnoses, individualized therapy, and improved patient well-being and outcomes. Nonetheless, these processes have faced numerous hurdles since the inception of the EMR nearly half a century ago via its introduction in the United States in 1972 [2], subsequently progressing slowly and steadily. At the time, the EMR raised the prospect of major patient advantages in terms of utilizing past patient data to improve medical decision making and treatment outcomes. Increased connectivity allowed clinicians to see records that were physically separated or contained multiple types of files. Over time, the integration of EMR [3] into other systems across healthcare has enhanced EMR's potential to provide complete patient data and greater data sharing across the medical system, resulting in system efficiency, and improved medical care. With the introduction of data mining, textual analysis using natural language processing [4] and AI, as well as new mobile health application, Internet of Things devices [5], and telehealth [6], EMR can now be used not only to benefit individuals but also to mine, access, and analyze useful medical information at the population level [7]. This has been especially important for patients suffering from chronic illnesses [8]. Moreover, in many countries, nation-wide EMR (also known as the electronic health record) has significantly enhanced research opportunities. Patient participation in data entry into the EMR [9], as well as sensor data input [10], have created new opportunities to extend EMR capabilities and use, transforming data into wisdom. EMR not only improves medical outcomes, but it also creates significant resource savings [9]. Prior to the implementation of EMR, hospitals allocated acres of space for the storage of paper-based records and hired many workers to search and retrieve them. Even small community clinics faced a significant burden in satisfying the legal and patient-service requirements of storing patient files. EMR has freed up enormous human and physical resources that would otherwise be necessary to retain and access paper records at all levels of the medical system. However, the EMR has introduced significant challenges that have frequently prevented health organizations from fully leveraging the potential benefits of these advances [10]. Examples of such issues include (i) the high cost of implementing and maintaining EMR, which includes expenses for hardware, software, and trained personnel. Thiscost often competes with other financial needs within the healthcare system. (ii) Interoperability issues arise when integrating EMR with existing systems, especially in large hospital settings where systems are introduced gradually. (iii) The data from previous systems must be transferred to the new EMR accurately and reliably. (iv) Storing large amounts of highly sensitive digital data poses significant challenges in terms of data privacy [11], legal issues, and security risks [12]. Instances of significant data breaches have occurred in the field of electronic medical records (EMR), such as the Tricare breach in 2011, the UCLA Health Data breach in 2015, the Banner Health breach in 2016, and the Trinity Health breach in 2020. These breaches had a widespread impact, affecting millions of patients. Thus, while EMR's ability to collect and digitally preserve patient data in a form that can be easily retrieved and analyzed can provide significant benefits to both individuals and communities, it also introduces some significant hazards that deserve greater investigation and consideration.