Healthcare Informatics Research最新文献

Objectives: Smoking remains the leading cause of preventable disease. However, smokers have shown poor compliance with smoking cessation clinics. Smartphone applications present a promising opportunity to improve this compliance. This study aimed to explore the relationship between nicotine dependence, smartphone usage patterns, and anticipated compliance with a smoking cessation application among smokers, with the goal of informing future development of such applications.

Methods: A total of 53 current smokers were surveyed using a questionnaire. Nicotine dependence was assessed using the Fagerstrom Test for Nicotine Dependence (FTND). Variables included the number of hours spent using a phone, willingness to quit smoking, number of previous quit attempts, desired number of text messages about smoking cessation, expected duration of application usage, and FTND scores. Kendall's partial correlation, adjusted for age, was employed for the analysis.

Results: The amount of time smokers spent on their mobile devices was negatively correlated with the number of smoking cessation text messages they wanted to receive (τ coefficient = -0.210, p = 0.026) and the duration they intended to use the cessation application (τ coefficient = -0.260, p = 0.006). Conversely, the number of desired text messages was positively correlated with the intended duration of application usage (τ coefficient = 0.366, p = 0.00012).

Conclusions: Smokers who spent more time on their mobile devices tended to prefer using the cessation application for shorter periods, whereas those who desired more text messages about smoking cessation were more inclined to use the application for longer durations.

Objectives: In Indonesia, the poor prognosis and high hospital readmission rates of patients with heart failure (HF) have yet to receive focused attention. However, machine learning (ML) approaches can help to mitigate these problems. We aimed to determine which ML models best predicted HF severity and hospital readmissions and could be used in a patient self-monitoring mobile application.

Methods: In a retrospective cohort study, we collected the data of patients admitted with HF to the Siloam Diagram Heart Center in 2020, 2021, and 2022. Data was analyzed using the Orange data mining classification method. ML support algorithms, including artificial neural network (ANN), random forest, gradient boosting, Naïve Bayes, tree-based models, and logistic regression were used to predict HF severity and hospital readmissions. The performance of these models was evaluated using the area under the curve (AUC), accuracy, and F1-scores.

Results: Of the 543 patients with HF, 3 (0.56%) were excluded due to death on admission. Hospital readmission occurred in 138 patients (25.6%). Of the six algorithms tested, ANN showed the best performance in predicting both HF severity (AUC = 1.000, accuracy = 0.998, F1-score = 0.998) and readmission for HF (AUC = 0.998, accuracy = 0.975, F1-score = 0.972). Other studies have shown variable results for the best algorithm to predict hospital readmission in patients with HF.

Conclusions: The ANN algorithm performed best in predicting HF severity and hospital readmissions and will be integrated into a mobile application for patient self-monitoring to prevent readmissions.

Objectives: This study compared the scientific publication speeds of Korean medical journals before and during the coronavirus disease 2019 (COVID-19) era.

Methods: We analyzed 2,064 papers from 43 international Korean medical journals, selecting 12 papers annually from 2019 to 2022. We assessed publication speed indicators, including the time from submission to revision and from submission to publication. Additionally, we examined variations in publication speed based on journal and paper characteristics, including whether the studies were related to COVID-19.

Results: Among the 43 journals analyzed, 39.5% disclosed the peer review duration from submission to the first decision, and 11.6% reported their acceptance rates. The average time from submission to acceptance was 127.0 days in 2019, 126.1 days in 2020, 124.6 days in 2021, and 126.4 days in 2022. For COVID-19-related studies, the average time from submission to revision was 61.4 days, compared to 105.1 days for non-COVID-19 studies; from submission to acceptance, it was 87.4 days for COVID-19-related studies and 127.1 days for non-COVID-19 studies. All indicators for COVID-19-related studies showed shorter durations than those for non-COVID-19 studies, and the proportion of studies accepted within 30 or 60 days was significantly higher for COVID-19-related studies.

Conclusions: This study investigated the publication speed of Korean international medical journals before and during the COVID-19 pandemic. The pandemic influenced journals' review and publication processes, potentially impacting the quality of academic papers. These findings provide insights into publication speeds during the COVID-19 era, suggesting that journals should focus on maintaining the integrity of their publication and review processes.

Objectives: This article presents a systematic review of recent advancements in telemedicine architectures for continuous monitoring, providing a comprehensive overview of the evolving software engineering practices underpinning these systems. The review aims to illuminate the critical role of telemedicine in delivering healthcare services, especially during global health crises, and to emphasize the importance of effectiveness, security, interoperability, and scalability in these systems.

Methods: A systematic review methodology was employed, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. As the primary research method, the PubMed, IEEE Xplore, and Scopus databases were searched to identify articles relevant to telemedicine architectures for continuous monitoring. Seventeen articles were selected for analysis, and a methodical approach was employed to investigate and synthesize the findings.

Results: The review identified a notable trend towards the integration of emerging technologies into telemedicine architectures. Key areas of focus include interoperability, security, and scalability. Innovations such as cognitive radio technology, behavior-based control architectures, Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR) standards, cloud computing, decentralized systems, and blockchain technology are addressing challenges in remote healthcare delivery and continuous monitoring.

Conclusions: This review highlights major advancements in telemedicine architectures, emphasizing the integration of advanced technologies to improve interoperability, security, and scalability. The findings underscore the successful application of cognitive radio technology, behavior-based control, HL7 FHIR standards, cloud computing, decentralized systems, and blockchain in advancing remote healthcare delivery.

Objectives: The rapid spread of coronavirus disease 2019 (COVID-19) posed significant challenges to healthcare systems, prompting the widespread adoption of telehealth to provide medical services while minimizing the risk of virus transmission. This study aimed to assess the satisfaction rates of both patients and physicians with telehealth during the COVID-19 pandemic.

Methods: Searches were conducted in the Web of Science, PubMed, and Scopus databases from January 1, 2020, to January 1, 2023. We included studies that utilized telehealth during the COVID-19 pandemic and reported satisfaction data for both patients and physicians. Data extraction was performed using a form designed by the researchers. A meta-analysis was carried out using random-effects models with the OpenMeta-Analyst software. A subgroup analysis was conducted based on the type of telehealth services used: telephone, video, and a combination of both.

Results: From an initial pool of 1,454 articles, 62 met the inclusion criteria for this study. The most commonly used methods were video and telephone calls. The overall satisfaction rate with telehealth during the COVID-19 pandemic was 81%. Satisfaction rates were higher among patients at 83%, compared to 74% among physicians. Specifically, telephone consultations had a satisfaction rate of 77%, video consultations 86%, and a mix of both methods yielded a 77% satisfaction rate.

Conclusions: Overall, satisfaction with telehealth during the COVID-19 pandemic was considered satisfactory, with both patients and physicians reporting high levels of satisfaction. Telehealth has proven to be an effective alternative for delivering healthcare services during pandemics.

Objectives: This paper aimed to assess the adoption of electronic medical records (EMRs) in healthcare facilities in Dubai, the largest city in the United Arab Emirates (UAE) and a location where extensive healthcare services are provided. It explored the challenges, milestones, and accomplishments associated with this process.

Methods: A situation analysis was conducted by contacting 2,089 healthcare facilities in Dubai to determine whether they had implemented EMR in their medical practices and to identify the challenges they faced during this process. Additionally, the Electronic Medical Record Adoption Model (EMRAM) was utilized to measure the maturity level of hospitals in terms of EMR adoption. The EMRAM stages were rated on a scale from 0 to 7, with 0 representing the least mature stage and 7 the most mature.

Results: By September 2023, all hospitals (100%, n = 54) and 75% of private clinics (n = 1,460) in Dubai had implemented EMRs. Several challenges were identified, including the absence of EMRs within the healthcare facility, having an EMR with a low EMRAM score, or the lack of a unified interoperability standard. Additionally, the absence of a clear licensing program for EMR vendors, whether standalone or cloud-based, was among the other challenges noted.

Conclusions: EMR implementation in healthcare facilities in Dubai is at a mature stage. However, further efforts are required at both the decision-making and technical levels. We believe that our experience can benefit other countries in the region in implementing EMRs and using EMRAM to assess their health information systems.

Objectives: Education in biomedical and health informatics is essential for managing complex healthcare systems, bridging the gap between healthcare and information technology, and adapting to the digital requirements of the healthcare industry. This review presents the current status of biomedical and health informatics education domestically and internationally and proposes recommendations for future development.

Methods: We analyzed evidence from reports and papers to explore global trends and international and domestic examples of education. The challenges and future strategies in Korea were also discussed based on the experts' opinions.

Results: This review presents international recommendations for establishing education in biomedical and health informatics, as well as global examples at the undergraduate and graduate levels in medical and nursing education. It provides a thorough examination of the best practices, strategies, and competencies in informatics education. The review also assesses the current state of medical informatics and nursing informatics education in Korea. We highlight the challenges faced by academic institutions and conclude with a call to action for educators to enhance the preparation of professionals to effectively utilize technology in any healthcare setting.

Conclusions: To adapt to the digitalization of healthcare, systematic and continuous workforce development is essential. Future education should prioritize curriculum innovations and the establishment of integrated education programs, focusing not only on students but also on educators and all healthcare personnel in the field. Addressing these challenges requires collaboration among educational institutions, academic societies, government agencies, and international bodies dedicated to systematic and continuous workforce development.

Objectives: Skin cancer is a prevalent type of malignancy, necessitating efficient diagnostic tools. This study aimed to develop an automated skin lesion classification model using the dynamically expandable representation (DER) incremental learning algorithm. This algorithm adapts to new data and expands its classification capabilities, with the goal of creating a scalable and efficient system for diagnosing skin cancer.

Methods: The DER model with incremental learning was applied to the HAM10000 and ISIC 2019 datasets. Validation involved two steps: initially, training and evaluating the HAM10000 dataset against a fixed ResNet-50; subsequently, performing external validation of the trained model using the ISIC 2019 dataset. The model's performance was assessed using precision, recall, the F1-score, and area under the precision-recall curve.

Results: The developed skin lesion classification model demonstrated high accuracy and reliability across various types of skin lesions, achieving a weighted-average precision, recall, and F1-score of 0.918, 0.808, and 0.847, respectively. The model's discrimination performance was reflected in an average area under the curve (AUC) value of 0.943. Further external validation with the ISIC 2019 dataset confirmed the model's effectiveness, as shown by an AUC of 0.911.

Conclusions: This study presents an optimized skin lesion classification model based on the DER algorithm, which shows high performance in disease classification with the potential to expand its classification range. The model demonstrated robust results in external validation, indicating its adaptability to new disease classes.