A Comparison of Two Cross-Sectional Studies on Successful Model of Introducing Nursing Information System in a Regional Teaching Hospital in Taiwan

Exploring the Usage Effectiveness of a Nursing Charge System

The nursing charge system for inpatient accounting has been utilized in healthcare institutions for years. However, the level of its effectiveness in meeting the needs of nursing services, including further development, has not been systematically evaluated. A cross-sectional study based in Delone and McLean's information system success model was applied to explore the level of effective nursing charge system usage across the five dimensions of system quality, information quality, service quality, user satisfaction, and net benefits. We conducted a survey of the inpatient units of a medical center in Taiwan from June 23, 2021, to July 23, 2021. A total of 214 valid questionnaires were collected. Using a 5-point Likert scale, the dimension with the highest score was information quality (3.71), followed by service quality (3.37), user satisfaction (3.36), net benefits (3.31), and system quality (3.23). Older nurses ( r = -0.176) and those with more clinical experience ( r = -0.151) viewed the nursing charge system as having less information quality. The comfort level with using the computer was positively associated with system quality ( r = 0.396), information quality ( r = 0.378), service quality ( r = 0.275), user satisfaction ( r = 0.417), and net benefits ( r = 0.355). The opinions of nurses are vital. User feedback and advice should be investigated regularly to achieve system optimization.

Constructing and implementing a performance evaluation indicator set for artificial intelligence decision support systems in pediatric outpatient clinics: an observational study

Artificial intelligence (AI) decision support systems in pediatric healthcare have a complex application background. As an AI decision support system (AI-DSS) can be costly, once applied, it is crucial to focus on its performance, interpret its success, and then monitor and update it to ensure ongoing success consistently. Therefore, a set of evaluation indicators was explicitly developed for AI-DSS in pediatric healthcare, enabling continuous and systematic performance monitoring. The study unfolded in two stages. The first stage encompassed establishing the evaluation indicator set through a literature review, a focus group interview, and expert consultation using the Delphi method. In the second stage, weight analysis was conducted. Subjective weights were calculated based on expert opinions through analytic hierarchy process, while objective weights were determined using the entropy weight method. Subsequently, subject and object weights were synthesized to form the combined weight. In the two rounds of expert consultation, the authority coefficients were 0.834 and 0.846, Kendall's coordination coefficient was 0.135 in Round 1 and 0.312 in Round 2. The final evaluation indicator set has three first-class indicators, fifteen second-class indicators, and forty-seven third-class indicators. Indicator I-1(Organizational performance) carries the highest weight, followed by Indicator I-2(Societal performance) and Indicator I-3(User experience performance) in the objective and combined weights. Conversely, 'Societal performance' holds the most weight among the subjective weights, followed by 'Organizational performance' and 'User experience performance'. In this study, a comprehensive and specialized set of evaluation indicators for the AI-DSS in the pediatric outpatient clinic was established, and then implemented. Continuous evaluation still requires long-term data collection to optimize the weight proportions of the established indicators.

Towards an evaluation framework for medical device-integrated electronic medical record

INTRODUCTION

Medical device (MD)-integrated (I) electronic medical record (EMR) (MDI-EMR) poses cyber threats that undermine patient safety, and thus, they require effective control mechanisms. We reviewed the related literature, including existing EMR and MD risk assessment approaches, to identify MDI-EMR comprehensive evaluation dimensions and measures.

AREAS COVERED

We searched multiple databases, including PubMed, Web of Knowledge, Scopus, ACM, Embase, IEEE and Ingenta. We explored various evaluation aspects of MD and EMR to gain a better understanding of their complex integration. We reviewed numerous risk management and assessment frameworks related to MD and EMR security aspects and mitigation controls and then identified their common evaluation aspects. Our review indicated that previous evaluation frameworks assessed MD and EMR independently. To address this gap, we proposed an evaluation framework based on the sociotechnical dimensions of health information systems and risk assessment approaches for MDs to evaluate MDI-EMR integratively.

EXPERT OPINION

The emergence of MDI-EMR cyber threats requires appropriate evaluation tools to ensure the safe development and application of MDI-EMR. Consequently, our proposed framework will continue to evolve through subsequent validations and refinements. This process aims to establish its applicability in informing stakeholders of the safety level and assessing its effectiveness in mitigating risks for future improvements.

Effects of a smartphone application named "Shared Decision Making Assistant" for informed patients with primary liver cancer in decision-making in China: a quasi-experimental study

BACKGROUND

It is well known that decision aids can promote patients' participation in decision-making, increase patients' decision preparation and reduce decision conflict. The goal of this study is to explore the effects of a "Shared Decision Making Assistant" smartphone application on the decision-making of informed patients with Primary Liver Cancer (PLC) in China.

METHODS

In this quasi-experimental study , 180 PLC patients who knew their real diagnoses in the Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China, from April to December 2020 were randomly assigned to a control group and an intervention group. Patients in the intervention group had an access to the "Shared Decision Making Assistant" application in decision-making, which included primary liver cancer treatment knowledge, decision aids path, continuing nursing care video clips, latest information browsing and interactive platforms. The study used decision conflict scores to evaluate the primary outcome, and the data of decision preparation, decision self-efficacy, decision satisfaction and regret, and knowledge of PLC treatment for secondary outcomes. Then, the data were entered into the SPSS 22.0 software and were analyzed by descriptive statistics, Chi-square, independent t-test, paired t-test, and Mann-Whitney tests.

RESULTS

Informed PLC patients in the intervention group ("SDM Assistant" group) had significantly lower decision conflict scores than those in the control group. ("SDM Assistant" group: 16.89 ± 8.80 vs. control group: 26.75 ± 9.79, P < 0.05). Meanwhile, the decision preparation score (80.73 ± 8.16), decision self-efficacy score (87.75 ± 6.87), decision satisfaction score (25.68 ± 2.10) and knowledge of PLC treatment score (14.52 ± 1.91) of the intervention group were significantly higher than those of the control group patients (P < 0.05) at the end of the study. However, the scores of "regret of decision making" between the two groups had no statistical significance after 3 months (P > 0.05).

CONCLUSIONS

Access to the "Shared Decision Making Assistant" enhanced the PLC patients' performance and improved their quality of decision making in the areas of decision conflict, decision preparation, decision self-efficacy, knowledge of PLC treatment and satisfaction. Therefore, we recommend promoting and updating the "Shared Decision Making Assistant" in clinical employment and future studies.