Healthcare Informatics Research最新文献

Objectives: With the sudden global shift to online learning modalities, this study aimed to understand the unique challenges and experiences of emergency remote teaching (ERT) in nursing education.

Methods: We conducted a comprehensive online international cross-sectional survey to capture the current state and firsthand experiences of ERT in the nursing discipline. Our analytical methods included a combination of traditional statistical analysis, advanced natural language processing techniques, latent Dirichlet allocation using Python, and a thorough qualitative assessment of feedback from open-ended questions.

Results: We received responses from 328 nursing educators from 18 different countries. The data revealed generally positive satisfaction levels, strong technological self-efficacy, and significant support from their institutions. Notably, the characteristics of professors, such as age (p = 0.02) and position (p = 0.03), influenced satisfaction levels. The ERT experience varied significantly by country, as evidenced by satisfaction (p = 0.05), delivery (p = 0.001), teacher-student interaction (p = 0.04), and willingness to use ERT in the future (p = 0.04). However, concerns were raised about the depth of content, the transition to online delivery, teacher-student interaction, and the technology gap.

Conclusions: Our findings can help advance nursing education. Nevertheless, collaborative efforts from all stakeholders are essential to address current challenges, achieve digital equity, and develop a standardized curriculum for nursing education.

Objectives: The aim of this study was to review hospital-based health information system (HIS) studies that used qualitative research methods and evaluate their methodological contexts and implications. In addition, we propose practical guidelines for HIS researchers who plan to use qualitative research methods.

Methods: We collected papers published from 2012 to 2022 by searching the PubMed and CINAHL databases. As search keywords, we used specific system terms related to HISs, such as "electronic medical records" and "clinical decision support systems," linked with their operational terms, such as "implementation" and "adaptation," and qualitative methodological terms such as "observation" and "in-depth interview." We finally selected 74 studies that met this review's inclusion criteria and conducted an analytical review of the selected studies.

Results: We analyzed the selected articles according to the following four points: the general characteristics of the selected articles; research design; participant sampling, identification, and recruitment; and data collection, processing, and analysis. This review found methodologically problematic issues regarding researchers' reflections, participant sampling methods and research accessibility, and data management.

Conclusions: Reports on the qualitative research process should include descriptions of researchers' reflections and ethical considerations, which are meaningful for strengthening the rigor and credibility of qualitative research. Based on these discussions, we suggest guidance for conducting ethical, feasible, and reliable qualitative research on HISs in hospital settings.

Objectives: The objective of this research is to apply machine learning (ML) algorithms to predict the survival of cervical cancer patients. The aim was to address the limitations of traditional statistical methods, which often fail to provide accurate answers due to the complexity of the problem.

Methods: This research employed visualization techniques for initial data understanding. Subsequently, ML algorithms were used to develop both classification and regression models for survival prediction. In the classification models, we trained the algorithms to predict the time interval between the initial diagnosis and the patient's death. The intervals were categorized as "<6 months," "6 months to 3 years," "3 years to 5 years," and ">5 years." The regression model aimed to predict survival time (in months). We used attribute weights to gain insights into the model, highlighting features with a significant impact on predictions and offering valuable insights into the model's behavior and decision-making process.

Results: The gradient boosting trees algorithm achieved an 81.55% accuracy in the classification model, while the random forest algorithm excelled in the regression model, with a root mean square error of 22.432. Notably, radiation doses around the affected areas significantly influenced survival duration.

Conclusions: Machine learning demonstrated the ability to provide high-accuracy predictions of survival periods in both classification and regression problems. This suggests its potential use as a decision-support tool in the process of treatment planning and resource allocation for each patient.

Objectives: The use of technology in healthcare to manage patient records, guide diagnosis, and make referrals is termed electronic healthcare. An electronic health record system called Lightwave Health Information Management System (LHIMS) was implemented in 2018 at Cape Coast Teaching Hospital (CCTH). This study evaluated the impact of LHIMS on the quality of healthcare data at CCTH, focusing on the extent to which its use has enhanced the main dimensions of data quality.

Methods: Structured questionnaires were administered to doctors at CCTH to enquire about their opinions about the present state of LHIMS as measured against the parameters of interest in this study, mainly the dimensions of quality healthcare data and the specific issues plaguing the system as reported by respondents.

Results: Most doctors found LHIMS convenient to use, mainly because it made access to patient records easier and had to some extent improved the dimensions of quality healthcare data, except for comprehensiveness, at CCTH. Major challenges that impeded the smooth running of the system were erratic power supply, inadequate logistics and technological drive, and poor internet connectivity.

Conclusions: LHIMS must be upgraded to include more decision support systems and additional add-ons such as patients' radiological reports, and laboratory results must be readily available on LHIMS to make patient health data more comprehensive.

Objectives: The objective of this study was to investigate the effects of a digital therapeutic exercise platform for pre-frail or frail elderly individuals using augmented reality (AR) technology accessed through glasses. A tablet-based exercise program was utilized for the control group, and a non-inferiority assessment was employed.

Methods: The participants included older adult women aged 65 years and older residing in Incheon, South Korea. A digital therapeutic exercise program involving AR glasses or tablet-based exercise was administered twice a week for 12 weeks, with gradually increasing exercise duration. Statistical analysis was conducted using the t-test and Wilcoxon rank sum test for non-inferiority assessment.

Results: In the primary efficacy assessment, regarding the change in lower limb strength, a non-inferior result was observed for the intervention group (mean change, 5.46) relative to the control group (mean change, 4.83), with a mean difference of 0.63 between groups (95% confidence interval, -2.33 to 3.58). Changes in body composition and physical fitness-related variables differed non-significantly between the groups. However, the intervention group demonstrated a significantly greater increase in cardiorespiratory endurance (p < 0.005) and a significantly larger decrease in the frailty index (p < 0.001).

Conclusions: An AR-based digital therapeutic program significantly and positively contributed to the improvement of cardiovascular endurance and the reduction of indicators of aging among older adults. These findings underscore the value of digital therapeutics in mitigating the effects of aging.

Objectives: The use of videoconferencing technologies for clinician-patient online consultations has become increasingly popular. Training on online communication competence through a videoconferencing application that integrates nonverbal communication detection with feedback is one way to prepare future clinicians to conduct effective online consultations. This case report describes and evaluates two such applications designed for healthcare professionals and students in healthcare-related fields.

Methods: We conducted a literature review using five databases, including the Web of Science, Scopus, PubMed, ACM, IEEE, and CINAHL in the spring of 2022.

Results: We identified seven studies on two applications, ReflectLive and EQClinic. These studies were conducted by two research groups from the USA and Australia and were published between 2016 and 2020. Both detected nonverbal communication from video and audio and provided computer-generated feedback on users' nonverbal communication. The studies evaluated usability, effectiveness in learning communication skills, and changes in the users' awareness of their nonverbal communication. The developed applications were deemed feasible. However, the feedback given by the applications needs improvement to be more beneficial to the user. The applications were primarily evaluated with medical students, with limited or no attention given to questions regarding ethics, information security, privacy, sustainability, and costs.

Conclusions: Current research on videoconferencing systems for training online consultation skills is very limited. Future research is needed to develop more user-centered solutions, focusing on a multidisciplinary group of students and professionals, and to explore the implications of these technologies from a broader perspective, including ethics, information security, privacy, sustainability, and costs.

Objectives: Mobile health applications that are designed without considering usability criteria can lead to cognitive overload, resulting in the rejection of these apps. To avoid this problem, the user interface of mobile health applications should be evaluated for cognitive load. This evaluation can contribute to the improvement of the user interface and help prevent cognitive overload for the user.

Methods: In this study, we evaluated a mobile personal health records application using the cognitive task analysis method, specifically the goals, operators, methods, and selection rules (GOMS) approach, along with the related updated GOMS model and gesture-level model techniques. The GOMS method allowed us to determine the steps of the tasks and categorize them as physical or cognitive tasks. We then estimated the completion times of these tasks using the updated GOMS model and gesture-level model.

Results: All 10 identified tasks were split into 398 steps consisting of mental and physical operators. The time to complete all the tasks was 5.70 minutes and 5.45 minutes according to the updated GOMS model and gesture-level model, respectively. Mental operators covered 73% of the total fulfillment time of the tasks according to the updated GOMS model and 76% according to the gesture-level model. The inter-rater reliability analysis yielded an average of 0.80, indicating good reliability for the evaluation method.

Conclusions: The majority of the task execution times comprised mental operators, suggesting that the cognitive load on users is high. To enhance the application's implementation, the number of mental operators should be reduced.

Objectives: Public healthcare data have become crucial to the advancement of medicine, and recent changes in legal structure on privacy protection have expanded access to these data with pseudonymization. Recent debates on public healthcare data use by private insurance companies have shown large discrepancies in perceptions among the general public, healthcare professionals, private companies, and lawmakers. This study examined public attitudes toward the secondary use of public data, focusing on differences between public and private entities.

Methods: An online survey was conducted from January 11 to 24, 2022, involving a random sample of adults between 19 and 65 of age in 17 provinces, guided by the August 2021 census.

Results: The final survey analysis included 1,370 participants. Most participants were aware of health data collection (72.5%) and recent changes in legal structures (61.4%) but were reluctant to share their pseudonymized raw data (51.8%). Overall, they were favorable toward data use by public agencies but disfavored use by private entities, notably marketing and private insurance companies. Concerns were frequently noted regarding commercial use of data and data breaches. Among the respondents, 50.9% were negative about the use of public healthcare data by private insurance companies, 22.9% favored this use, and 1.9% were "very positive."

Conclusions: This survey revealed a low understanding among key stakeholders regarding digital health data use, which is hindering the realization of the full potential of public healthcare data. This survey provides a basis for future policy developments and advocacy for the secondary use of health data.