Various digital healthcare devices and apps, such as blood glucose meters, blood pressure monitors, and step-trackers are commonly used by patients; however, digital healthcare devices have not been widely accepted in the medical market as of yet. Despite the various legal and privacy issues involved in their use, the main reason for its poor acceptance is that users do not use such devices voluntarily and continuously. Digital healthcare devices generally do not provide valuable information to users except for tracking self-checked glucose or walking. To increase the use of these devices, users must first understand the health data produced in the context of their personal health, and the devices must be easy to use and integrated into everyday life. Thus, users need to know how to manage their own data. Medical staff must teach and encourage users to analyze and manage their patient-generated healthcare data, and users should be able to find medical values from these digital devices. Eventually, a single customized service that can comprehensively analyze various medical data to provide valuable customized services to users, and which can be linked to various heterogeneous digital healthcare devices based on the integration of various health data should be developed. Digital healthcare professionals should have detailed knowledge about a variety of digital healthcare devices and fully understand the advantages and disadvantages of digital healthcare to help patients understand and embrace the use of such devices.

Various digital healthcare devices and apps, such as blood glucose meters, blood pressure monitors, and step-trackers are commonly used by patients; however, digital healthcare devices have not been widely accepted in the medical market as of yet. Despite the various legal and privacy issues involved in their use, the main reason for its poor acceptance is that users do not use such devices voluntarily and continuously. Digital healthcare devices generally do not provide valuable information to users except for tracking self-checked glucose or walking. To increase the use of these devices, users must first understand the health data produced in the context of their personal health, and the devices must be easy to use and integrated into everyday life. Thus, users need to know how to manage their own data. Medical staff must teach and encourage users to analyze and manage their patient-generated healthcare data, and users should be able to find medical values from these digital devices. Eventually, a single customized service that can comprehensively analyze various medical data to provide valuable customized services to users, and which can be linked to various heterogeneous digital healthcare devices based on the integration of various health data should be developed. Digital healthcare professionals should have detailed knowledge about a variety of digital healthcare devices and fully understand the advantages and disadvantages of digital healthcare to help patients understand and embrace the use of such devices.

Advertising in the medical and legal fields, which are among Korea's leading professions, has increasingly utilized major advertising platforms such as LawTalk and UNNI—two of the most prominent and contentious platforms in their respective fields. While it is generally unproblematic for professionals like lawyers and doctors to promote public interest through advertising on these commercial platforms, the creation of a profit-driven structure has the potential to undermine their professional ecosystems. This article explores the issues associated with advertising in the medical field through large commercial platforms, drawing on notable examples from the legal and medical fields in Korea. Specifically, we analyze two of the most popular yet controversial platforms in these sectors, LawTalk and UNNI. In Korea, the format and method of advertising are legal as long as they do not involve referring or soliciting clients, thereby making platform advertising lawful when used solely for that purpose. Nevertheless, it is crucial to prevent medical advertising platforms from establishing market monopolies by skirting various profit regulations and laws. In response to these concerns, the Korean Bar Association has prohibited all advertisements by platform companies. The medical community should closely examine the rationale and process behind this decision. Given the significant social influence of large corporate platforms and the unique social responsibilities of the medical and legal professions, future platform advertising should be subject to distinct legal and institutional regulations that differ from those applied to general services.

Remote collaborative care is a program that improves medical services by linking local and remote physicians with residents in areas where access to medical facilities is limited, utilizing information and communication technology. As a result, patients can obtain medical advice and counseling at local hospitals without needing to travel to distant facilities. This care model involves communication between doctors, facilitating the accurate transfer of medical information and reducing the risk of misunderstandings. For instance, managing conditions such as blood pressure or blood glucose is more straightforward because a local hospital can assess the patient's status while a remote hospital simultaneously provides high-quality, specialized medical services. With the rise in poorly controlled hypertension or diabetes, the need for remote collaborative care has also increased. This care model enables local hospitals to maintain continuous patient care with the support of remote facilities. This is particularly true following acute cardiovascular treatment, where local hospitals, assisted by remote institutions, can safely offer high-quality services such as rehabilitation and follow-up care. Although remote hospitals have many advantages with the increasing number of patients, many difficulties remain in commercializing unsystematized remote collaborative care. Specifically, low reimbursements for medical services must be addressed, proper equipment is needed, more time and effort must be invested, and the liability issue must also be dealt with. Nevertheless, remote collaborative care using information and communication technology will be necessary in the future. Medical staff need to objectively examine the advantages and disadvantages of remote collaborative care from various perspectives and find ways to revitalize it.

The advent of medical big data has increased the scope of the clinical use of such data; however, these data have raised serious concerns regarding personal privacy protection, which hinders their usage. For instance, as the pseudonymization or anonymization of data increases, the quality of its clinical use decreases. Thus, a balanced approach is required to maximize clinical data use while protecting personal information as much as possible. However, Korea’s existing laws mandate several kinds of consent; soliciting some of these types of consent can be cumbersome. Moreover, while the collection of medical data by hospitals requires considerable time and money, its ownership is difficult to ascertain. To bridge the enormous gap between the protection of personal information and the use of clinical data, the European Union and countries such as Finland have already proposed various modes of guaranteeing the free movement of personal information that simultaneously strengthen people’s personal rights. Similarly, Korea has initiated the MyData Service, although it faces several limitations. Therefore, this study reviews Korea’s current healthcare big data system, the laws governing data sharing and usage, and compares them with similar laws enacted by the European Union and Finland. It then provides future direction for Korea’s personal information protection legislation. Ultimately, governments must expand and elaborate upon the scope and content of personal information protection laws to enable the development of healthcare and other industries without sacrificing either personal information protection or clinical use of medical data.

Background: Remote collaborative care (ReCC) is a legally recognized form of telehealth that facilitates communication between physicians. This study aimed to analyze the effectiveness of ReCC services and establish a foundation for the usefulness and effectiveness of ReCC. Methods: This retrospective cohort study utilized data from the Digital Healthcare Information System (DHIS) managed by the Korea Social Security Information Service. We extracted data on patients who were registered from January 2017 through September 2023 to investigate the effects of various factors. Results: A total of 10,407 individuals participated in the remote collaborative consultation service provided by the DHIS. Of these participants, those aged ≥80 years represented 39.2% (4,085 patients), while those aged 70 to 79 years comprised 36.9% (3,838 patients). The conditions treated included hypertension, affecting 69.2% (7,203 patients), and diabetes, affecting 21.1% (2,201 patients). Although various measurement items were recorded, most data beyond blood pressure readings were missing, posing a challenge for analysis. Notably, there was a significant reduction in blood pressure that was sustained at follow-up intervals of 1, 3, 6, and 12 months post-baseline (all P<0.05). Conclusions Owing to the lack of data, follow-up assessments for conditions other than hypertension proved to be challenging. Medical staff should increase their focus on and engagement with the system. Remote consultations have demonstrated efficacy in managing hypertension in medically underserved areas, where access to healthcare services is often limited. This suggests the potential for expanded use of remote chronic care in the future.

Background: The most important aspect of a retrospective cohort study is the operational definition (OP) of the disease. We developed a detailed OP for the detection of sodiumglucose cotransporter-2 inhibitors (SGLT2i) related to diabetic ketoacidosis (DKA). The OP was systemically verified and analyzed. Methods: All patients prescribed SGLT2i at four university hospitals were enrolled in this experiment. A DKA diagnostic algorithm was created and distributed to each hospital;subsequently, the number of SGLT2i-related DKAs was confirmed. Then, the algorithm functionality was verified through manual chart reviews by an endocrinologist using the same OP. Results: A total of 8,958 patients were initially prescribed SGLT2i. According to the algorithm, 0.18% (16/8,958) were confirmed to have SGLT2i-related DKA. However, based on manual chart reviews of these 16 cases, there was only one case of SGLT2i-related DKA (positive predictive value = 6.3%). Even after repeatedly narrowing the diagnosis range of the algorithm, the effect of a positive predictive value was insignificant (6.3–10.0%, P > 0.999). Conclusion Owing to the nature of electronic medical record data, we could not create an algorithm that clearly differentiates SGLT2i-related DKA despite repeated attempts. In all retrospective studies, a portion of the samples should be randomly selected to confirm the accuracy of the OP through chart review. In retrospective cohort studies in which chart review is not possible, it will be difficult to guarantee the reliability of the results.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.