Healthcare Informatics Research最新文献

Objectives: This paper presents a systematic mapping of studies related to information systems and technology in the field of healthcare, enabling a visual mapping of the different lines of knowledge that can provide an overview of the scientific literature in this field. This map can help to clarify critical aspects of healthcare informatics, such as the main types of information systems, the ways in which they integrate with each other, and the technological trends in this field.

Methods: Systematic mapping refers to a process of classifying information in a given area of knowledge. It provides an overview of the state of the art in a particular discipline or area of knowledge, establishing a map that describes how knowledge is structured in that particular area. In this study, we proposed and carried out a specific implementation of the methodology for mapping. In total, 1,619 studies that combine knowledge related to information systems, computer science, and healthcare were selected and compiled from prestigious publications.

Results: The results established a distribution of the available literature and identified papers related to certain research questions, thereby providing a map of knowledge that structures the different trends and main areas of research, making it possible to address the research questions and serving as a guide to deepen specific aspects of the field of study.

Conclusions: We project and propose future research for the trends that stand out because of their interest and the possibility of exploring these topics in greater depth.

Objectives: The aim of this study was to evaluate the performance of a clinical decision support system (CDSS) for therapeutic plans in geriatric dentistry. The information that needs to be considered in a therapeutic plan includes not only the patient's oral health status obtained from an oral examination, but also other related factors such as underlying diseases, socioeconomic characteristics, and functional dependency.

Methods: A Bayesian network (BN) was used as a framework to construct a model of contributing factors and their causal relationships based on clinical knowledge and data. The faster R-CNN (regional convolutional neural network) algorithm was used to detect oral health status, which was part of the BN structure. The study was conducted using retrospective data from 400 patients receiving geriatric dental care at a university hospital between January 2020 and June 2021.

Results: The model showed an F1-score of 89.31%, precision of 86.69%, and recall of 82.14% for the detection of periodontally compromised teeth. A receiver operating characteristic curve analysis showed that the BN model was highly accurate for recommending therapeutic plans (area under the curve = 0.902). The model performance was compared to that of experts in geriatric dentistry, and the experts and the system strongly agreed on the recommended therapeutic plans (kappa value = 0.905).

Conclusions: This research was the first phase of the development of a CDSS to recommend geriatric dental treatment. The proposed system, when integrated into the clinical workflow, is expected to provide general practitioners with expert-level decision support in geriatric dental care.

Objectives: Portal sites have become places to share queries about performing nursing and obtain expert know-how. This study aimed to analyze topics of interest in the field of infusion nursing among nurses working in clinical settings.

Methods: In total, 169 user query data were collected from October 5, 2018 to December 25, 2021. This exploratory study analyzed the semantic structure of posts on the nurse question-and-answer board of an infusion nursing-related internet portal by extracting major keywords through text data analysis and conducting term frequency (TF) and term frequency-inverse document frequency (TF-IDF) analysis, N-gram analysis, and CONvergence of iteration CORrelation (CONCOR) analysis. Word cloud visualization was conducted utilizing the "wordcloud" package of Python to provide a visually engaging and concise summary of information about the extracted terms.

Results: "Infusion" was the most frequent keyword and the highest-importance word. "Infusion→line" had the strongest association, followed by "vein→catheter," "line→change," and "peripheral→vein." Three topics were identified: the replacement of catheters, maintenance of the patency of the catheters, and securement of peripheral intravenous catheters, and the subtopics were blood sampling through central venous catheter, peripherally inserted central catheter management, evidence-based infusion nursing, and pediatric infusion nursing.

Conclusions: These findings indicate that nurses have various inquiries in infusion nursing. It is necessary to re-establish the duties and roles of infusion nurses, and to develop effective infusion nursing training programs.

Objectives: Although medical artificial intelligence (AI) systems that assist healthcare professionals in critical care settings are expected to improve healthcare, skepticism exists regarding whether their potential has been fully actualized. Therefore, we aimed to conduct a qualitative study with physicians and nurses to understand their needs, expectations, and concerns regarding medical AI; explore their expected responses to recommendations by medical AI that contradicted their judgments; and derive strategies to implement medical AI in practice successfully.

Methods: Semi-structured interviews were conducted with 15 healthcare professionals working in the emergency room and intensive care unit in a tertiary teaching hospital in Seoul. The data were interpreted using summative content analysis. In total, 26 medical AI topics were extracted from the interviews. Eight were related to treatment recommendation, seven were related to diagnosis prediction, and seven were related to process improvement.

Results: While the participants expressed expectations that medical AI could enhance their patients' outcomes, increase work efficiency, and reduce hospital operating costs, they also mentioned concerns regarding distortions in the workflow, deskilling, alert fatigue, and unsophisticated algorithms. If medical AI decisions contradicted their judgment, most participants would consult other medical staff and thereafter reconsider their initial judgment.

Conclusions: Healthcare professionals wanted to use medical AI in practice and emphasized that artificial intelligence systems should be trustworthy from the standpoint of healthcare professionals. They also highlighted the importance of alert fatigue management and the integration of AI systems into the workflow.

Objectives: The aim of this study was to assess the prevalence of nomophobia in university students.

Methods: A systematic search was conducted of the following databases: Web of Science/ Core Collection, Scopus, PubMed, Embase, and Ovid/ MEDLINE until March 2021. Cross-sectional studies reporting the prevalence of nomophobia in undergraduate or postgraduate university students that assessed nomophobia with the 20-item Nomophobia Questionnaire (NMP-Q) tool were included. Study selection, data extraction, and risk of bias assessment were performed in duplicate. A meta-analysis of proportions was performed using a random-effects model. Heterogeneity was assessed using sensitivity analysis according to the risk of bias, and subgrouping by country, sex, and major.

Results: We included 28 cross-sectional studies with a total of 11,300 participants from eight countries, of which 23 were included in the meta-analysis. The prevalence of mild nomophobia was 24% (95% confidence interval [CI], 20%-28%; I2 = 95.3%), that of moderate nomophobia was 56% (95% CI, 53%-60%; I2 = 91.2%), and that of severe nomophobia was 17% (95% CI, 15%-20%; I2 = 91.7%). Regarding countries, Indonesia had the highest prevalence of severe nomophobia (71%) and Germany had the lowest (3%). The prevalence was similar according to sex and major.

Conclusions: We found a high prevalence of moderate and severe nomophobia in university students. Interventions are needed to prevent and treat this problem in educational institutions.

Objectives: Low birth weight (LBW) is a global concern associated with fetal and neonatal mortality as well as adverse consequences such as intellectual disability, impaired cognitive development, and chronic diseases in adulthood. Numerous factors contribute to LBW and vary based on the region. The main objectives of this study were to compare four machine learning classifiers in the prediction of LBW and to determine the most important factors related to this phenomenon in Hamadan, Iran.

Methods: We carried out a retrospective cross-sectional study on a dataset collected from Fatemieh Hospital in 2017 that included 741 mother-newborn pairs and 13 potential factors. Decision tree, random forest, artificial neural network, support vector machine, and logistic regression (LR) methods were used to predict LBW, with five evaluation criteria utilized to compare performance.

Results: Our findings revealed a 7% prevalence of LBW. The average accuracy of all models was 87% or higher. The LR method provided a sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and accuracy of 74%, 89%, 7.04%, 29%, and 88%, respectively. Using LR, gestational age, number of abortions, gravida, consanguinity, maternal age at delivery, and neonatal sex were determined to be the six most important variables associated with LBW.

Conclusions: Our findings underscore the importance of facilitating timely diagnosis of causes of abortion, providing genetic counseling to consanguineous couples, and strengthening care before and during pregnancy (particularly for young mothers) to reduce LBW.