Purpose: This study aimed to identify research topics and trends on living liver donors over time through text network analysis and topic modeling. Methods: Five electronic databases (PubMed, CINAHL, Embase, Web of Science, and PsycINFO) were reviewed for studies published through September 2023, and 392 studies were included. Text network analysis was used to identify the basic characteristics and centrality of the network. The topics were named after extracting meaningful topics through topic modeling using latent Dirichlet allocation. Results: A total of 1,111 keywords were extracted from the abstracts of 392 selected studies, among which “length of stay,” “morbidity,” “mortality,” “pain,” and “quality of life” showed high frequency and centrality. Through topic modeling analysis, the following four topics were derived: objective health indicators (topic 1), subjective health indicators (topic 2), hepatobiliary-related indicators (topic 3), and early health indicators (topic 4). An analysis of trends in these topics over time showed that the proportion of topics 1, 3, and 4 increased or remained stable. In contrast, there was no significant change in topic 2, representing subjective health indicators. Conclusion This study explored research trends on living liver donors using text network analysis and topic modeling. Based on the main topics derived, research on postoperative outcomes for living liver donors has focused on objective health indicators, hepatobiliary-related indicators, and early health indicators compared to subjective health indicators. We suggest that future studies utilize integrated indicators of physical and psychosocial aspects.

Objectives: With growing attention on the healthcare industry as a potential market for big data and artificial intelligence in the Fourth Industrial Revolution, countries around the world are introducing and developing various policies and projects related to health information technology (HIT). To assist prioritizing HIT topics in policy making, this study adopts the Delphi technique to garner expert opinions from various fields of health informatics. Methods: Data were collected from November 2019 to February 2020 using the Delphi technique through two rounds of surveys administered via email. The Delphi panel consisted of 16 experts with a high level of experience in health informatics. They were from the Health Information Policy Advisory Committee of the Ministry of Health and Welfare of Korea, and the board of directors of the Korean Society of Medical Informatics. The experts were asked to assess the importance, urgency, and difficulty of HIT topics in three domains: technology, application, and infrastructure. Results: Of the 40 topic items, a 100% agreement was reached for the importance of 6 items, including 2 items in technology, 1 item in application, and 3 items in infrastructure domains. Especially, Quadrant I of a 2×2 matrix showing high importance and high urgency included 7 items in the technology domain, 2 items in the application domain, and 13 items in the infrastructure domain. Conclusions Most items with high importance and urgency belonged to the infrastructure domain. The findings indicated that fostering an infrastructural environment should be polices with top priorities of HIT.

Objectives: Systematic evaluations of the benefits of health information technology (HIT) play an essential role in enhancing healthcare quality by improving outcomes. However, there is limited empirical evidence regarding the benefits of IT adoption in healthcare settings. This study aimed to review the benefits of artificial intelligence (AI), the internet of things (IoT), and personal health records (PHR), based on scientific evidence. Methods: The literature published in peer-reviewed journals between 2016 and 2022 was searched for systematic reviews and meta-analysis studies using the PubMed, Cochrane, and Embase databases. Manual searches were also performed using the reference lists of systematic reviews and eligible studies from major health informatics journals. The benefits of each HIT were assessed from multiple perspectives across four outcome domains. Results: Twenty-four systematic review or meta-analysis studies on AI, IoT, and PHR were identified. The benefits of each HIT were assessed and summarized from a multifaceted perspective, focusing on four outcome domains: clinical, psycho-behavioral, managerial, and socioeconomic. The benefits varied depending on the nature of each type of HIT and the diseases to which they were applied. Conclusions Overall, our review indicates that AI and PHR can positively impact clinical outcomes, while IoT holds potential for improving managerial efficiency. Despite ongoing research into the benefits of health IT in line with advances in healthcare, the existing evidence is limited in both volume and scope. The findings of our study can help identify areas for further investigation.