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This paper proposes an integrated model for investigating how physicians' perceived individual and technology characteristics affect their technological stress (technostress) that is derived from using mobile electronic medical records (MEMRs). Individual characteristics comprise constructs of mobile self-efficacy and technology dependence, whereas perceived technology characteristics comprise constructs of perceived usefulness, complexity, and reliability. We employed the survey method to collect 158 valid questionnaires from physicians working at three branch hospitals to determine perceptions regarding MEMRs, yielding a response rate of 33.62%. Partial least squares, a structural equation modeling technique, was used for model examination and hypothesis testing. The results show that physicians have a low perception of MEMR dependence and technostress. Furthermore, physicians' perceived MEMR technology dependency, mobile self-efficacy, and complexity were proven to significantly affect physician technostress when using MEMRs, whereas perceived usefulness and reliability were not. The explanatory power of the research model reached 67.8%. The results of this study provide valuable insights and significant knowledge for technostress in health care, particularly from academic and practical perspectives. [ABSTRACT FROM AUTHOR]

Background: Adopting mobile electronic medical record (MEMR) systems is expected to be one of the superior approaches for improving nurses' bedside and point of care services. However, nurses may use the functions for far fewer tasks than the MEMR supports. This may depend on their technological personality associated to MEMR acceptance. The purpose of this study is to investigate nurses' personality traits in regard to technology readiness toward MEMR acceptance.Methods: The study used a self-administered questionnaire to collect 665 valid responses from a large hospital in Taiwan. Structural Equation modeling was utilized to analyze the collected data.Results: Of the four personality traits of the technology readiness, the results posit that nurses are optimistic, innovative, secure but uncomfortable about technology. Furthermore, these four personality traits were all proven to have a significant impact on the perceived ease of use of MEMR while the perceived usefulness of MEMR was significantly influenced by the optimism trait only. The results also confirmed the relationships between the perceived components of ease of use, usefulness, and behavioral intention in the Technology Acceptance Model toward MEMR usage.Conclusions: Continuous educational programs can be provided for nurses to enhance their information technology literacy, minimizing their stress and discomfort about information technology. Further, hospital should recruit, either internally or externally, more optimistic nurses as champions of MEMR by leveraging the instrument proposed in this study. Besides, nurses' requirements must be fully understood during the development of MEMR to ensure that MEMR can meet the real needs of nurses. The friendliness of user interfaces of MEMR and the compatibility of nurses' work practices as these will also greatly enhance nurses' willingness to use MEMR. Finally, the effects of technology personality should not be ignored, indicating that hospitals should also include more employees' characteristics beyond socio-demographic profiles in their personnel databases. [ABSTRACT FROM AUTHOR]

This study evaluated physicians' attitudes towards medical AI across three Taiwanese hospitals, focusing on constructs of trust, resistance, job insecurity, and adoption willingness, with a survey based on the Dual-factor Model yielding 282 responses and a 94% response rate. Results showed positive trust in AI, low resistance and job insecurity concerns, and a high willingness to adopt AI, indicating a favorable view of AI as a supportive tool rather than a replacement. Key adoption factors were identified as regulatory standards, accuracy, workflow integration, and result clarity, providing valuable insights for future AI development in medicine.

This study explored machine learning's potential in predicting the nutritional status and outcomes for pneumonia patients. It focused on 4,368 patients in a Taiwan medical center from Jan 2016 to Feb 2022, excluding ICU cases. The average age was 77.6 years, with 10.2% well-nourished, 76.3% at-risk, and 13.5% malnourished. Machine learning models, particularly LightGBM and XGBoost, showed high accuracy in predicting hospital stays, mortality rates, and readmissions. These findings emphasize the role of data-driven methods in enhancing patient care and managing conditions like pneumonia more effectively.

Our study at Chi Mei Medical Center introduced "A+ Nurse," a ChatGPT-based LLM tool, into the nursing documentation process to enhance efficiency and accuracy. The tool offers optimized recording and critical reminders, reducing documentation time from 15 to 5 minutes per patient while maintaining record quality. Nurses appreciated the tool's intuitive design and its effectiveness in improving documentation. This successful integration of AI-generated content in healthcare illustrates the potential of AI to streamline processes and improve patient care, setting a precedent for future AI-driven healthcare innovations.

Background and aim: Patients with chronic hepatitis B patients (CHB) with low-level viremia (LLV) are not necessarily at low risk of developing hepatocellular carcinoma (HCC). The question of whether CHB patients with LLV require immediate antiviral agent (AVT) or long-term AVT remains controversial. The study aims to investigate the risk of HCC development and the risk factors in CHB patients with LLV and construct a nomogram model predicting the risk of HCC. Methods: We conducted a retrospective cohort study that enrolled 16,895 CHB patients from January 2008 to December 2020. The patients were divided into three groups for comparison: the LLV group, maintained virological response (MVR) group and HBV-DNA>2000 group. The cumulative incidence of progression to HCC was assessed. Cox regression analysis was performed to determine the final risk factors, and a nomogram model was constructed. The 10-fold Cross-Validation method was utilized for internal validation. Results: A total of 408 new cases of HCC occurred during the average follow-up period of 5.78 years. The 3, 5, and 10-year cumulative HCC risks in the LLV group were 3.56%, 4.96%, and 9.51%, respectively. There was a significant difference in the cumulative risk of HCC between the HBV-DNA level > 2000 IU/mL and LLV groups (p = 0.049). Independent risk factors for HCC development in LLV group included male gender, age, presence of cirrhosis, and platelets count. The Area Under the Curve (AUC) values for the 3-year and 5-year prediction from our HCC risk prediction model were 0.75 and 0.76, respectively. Conclusion: Patients with LLV and MVR are still at risk for developing HCC. The nomogram established for CHB patient with LLV, incorporating identified significant risk factors, serves as an effective tool for predicting HCC-free outcomes. This nomogram model provides valuable information for determining appropriate surveillance strategies and prescribing AVT., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Chimei Medical Center developed an intuitive intelligent inpatient medical record system which integrates structured, unstructured, and textual medical records and provides health insurance payment suggestions from the collaborative medical record writer's point of view. Combined with the use of intelligent technology (Python language with the smart Content Difference Recognition software component), the system can ensure medical record quality by focusing on the differencse in recorded progress notes.

ST elevation myocardial infarction (STEMI), one main type of acute myocardial infarction with high mortality, requires percutaneous coronary intervention (PCI) with balloon inflation. Current guidelines recommend a door-to-balloon (D2B) interval (i.e., starts with the patient's arrival in the emergency department and ends when PCI with a catheter guidewire and balloon inflation crosses the culprit lesion) of no more than 90 min. However, promptly implementing PCI requires coordinating various medical teams. Checklists can be used to ensure consistency and operating sequences when executing complex tasks in a clinical routine. Developing an effective D2B checklist would enhance the care of STEMI patients who need PCI. Mobile information and communication technologies have the potential to greatly improve communication, facilitate access to information, and eliminate duplicated documentation without the limitations of space and time. In a research project by the Chi Mei Medical Center, "Developing a Mobile Electronic D2B Checklist for Managing the Treatment of STEMI Patients Who Need Primary Coronary Intervention," a prototype version of a mobile checklist was developed. This study describes the research project and the four phases of the system development life cycle, comprising system planning and selection, analysis, design, and implementation and operation. Face-to-face interviews with 16 potential users were conducted and revealed highly positive user perception and use intention toward the prototype. Discussion and directions for future research are also presented.

The purpose of this study was to investigate the factors influencing nurses' intentions toward the use of mobile electronic medical records, based on the Theory of Diffusion of Innovations. The cross-sectional study used a structured questionnaire for data collection, focusing on the nurses of a large hospital in southern Taiwan. A total of 720 valid questionnaires were returned yielding a response rate of 82.0%. Multiple regression analysis of the responses identified three innovative characteristics, compatibility, complexity, and observability, as significantly influencing nurses' intentions toward adopting mobile electronic medical records, whereas relative advantage and trialability did not. In addition, nursing seniority affected nurses' intentions significantly toward adopting mobile electronic medical records. Implications of effects of the factors and future research directions are discussed.

E-learning is thought of as an innovative approach to enhance nurses' care service knowledge. Extensive research has provided rich information toward system development, courses design, and nurses' satisfaction with an e-learning system. However, a comprehensive view in understanding nursing e-learning system success is an important but less focused-on topic. The purpose of this research was to explore net benefits of nursing e-learning systems based on the updated DeLone and McLean's Information System Success Model. The study used a self-administered questionnaire to collected 208 valid nurses' responses from 21 of Taiwan's medium- and large-scale hospitals that have implemented nursing e-learning systems. The result confirms that the model is sufficient to explore the nurses' use of e-learning systems in terms of intention to use, user satisfaction, and net benefits. However, while the three exogenous quality factors (system quality, information quality, and service quality) were all found to be critical factors affecting user satisfaction, only information quality showed a direct effect on the intention to use. This study provides useful insights for evaluating nursing e-learning system qualities as well as an understanding of nurses' intentions and satisfaction related to performance benefits.

The main purpose of this study was to investigate the perceptions of the electromagnetic interference (EMI) caused by radio frequency identification (RFID) with medical devices among hospitals as well as to call the attention of medical institutions to the development of RFID applications. A survey sponsored by the Department of Health of Taiwan was conducted and the target subjects were every hospital in Taiwan (486 in total). The survey topics included testing of RFID interference with medical devices and perceptions of safety issues of RFID. The main targets of the survey were the Chief Information Officers (CIOs) or the main person responsible for RFID systems in each hospital. Of the original 486 questionnaires mailed, 273 were returned. A return rate of 56.17% was obtained. The survey results revealed that only six hospitals had carried out tests on interference by RFID with medical devices, and the results of these tests indicated that RFID does not interfere with medical devices. A majority of hospitals understood that RFID may interfere with medical devices but did not think that this would seriously harm patients. The application of RFID in the healthcare industry is certainly promising; however, EMI issues must be appropriately handled. This study asserts that most hospitals do not understand or pay insufficient attention to the issue of RFID interference with patient safety or medical devices. In addition, most hospitals believe that the problem of RFID should be resolved by RFID vendors. Therefore, this study argues that medical institutions should develop more understanding of RFID issues and that more attention should be given to the potential problems of RFID interference when developing RFID applications.

The purpose of this study was to explore the status of e-healthcare maturity in Taiwan following a nationwide investigation sponsored by the Department of Health. Based on Nolan's stage model and related studies, we propose a multidimensional model to gain a better understanding of the current status of e-healthcare maturity in hospitals. The target subjects included every hospital in Taiwan. A total of 538 hospitals were successfully interviewed, showing a high response rate of 94.4%. The results indicate that the overall e-healthcare maturity of Taiwanese hospitals is fairly high. Such a high degree of maturity is critical for formulating e-healthcare policy to stimulate the exchange of electronic medical record. This article provides a brief comparison of the situation in other countries and posits that Taiwan holds a position of relative maturity in the world of e-healthcare development.

Objective: The objective of this study was to explore key factors of influence on the adoption of telecare by care institutions. This investigation was based on an extensive Technology-Organization-Environment framework comprising the four constructs of technology, organization, environment, and the project planning as well as corresponding variables., Materials and Methods: This study utilized a self-administered questionnaire for data collection, focusing on the managers of all 339 nursing homes in Taiwan for the census survey., Results: A total of 70 valid questionnaires yielded a response rate of 20.65%. After analyzing the responses through the Partial Least Squares approach, government support, technological knowledge, supplier support, team skills, and compatibility were found to be the five key factors that positively impact intention to adopt telecare from an institutional perspective., Conclusions: The results of this study indicated that care institutions, for the most part, are willing to develop telecare and are already incorporating preliminary applications of telecare, such as distance learning and resident positioning. Solutions to potential issues related to implementing telecare in practice are also proposed in this study.