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BACKGROUND: Improved access to media and medical knowledge has elicited stronger public health awareness. OBJECTIVE: This study developed a smart drug interaction reminder system for patients to increase knowledge and reduce nurse workload. METHODS: This study used a single-group pre-test/post-test design and applied mining techniques to analyze the weight and probability of interaction among various medicines. Data were collected from 258 participants at a teaching hospital in northern Taiwan using convenience sampling. An app was used to give patients real-time feedback to obtain access to information and remind them of their health issues. In addition to guiding the patients on medications, this app measured the nurses' work satisfaction and patients' knowledge of drug interaction. RESULTS: The results indicate that using information technology products to assist the app's real-time feedback system promoted nurses' work satisfaction, improved their health education skills, and helped patients to better understand drug interactions. CONCLUSION: Using information technology to provide patients with real-time inquiring functions has a significant effect on nurses' load reduction. Thus, smart drug interaction reminder system apps can be considered suitable nursing health education tools and the SDINRS app can be integrated into quantitative structure-activity relationship intelligence in the future. [ABSTRACT FROM AUTHOR]

In neurosurgical or orthopedic clinics, the differential diagnosis of lower back pain is often time-consuming and costly. This is especially true when there are several candidate diagnoses with similar symptoms that might confuse clinic physicians. Therefore, methods for the efficient differential diagnosis can help physicians to implement the most appropriate treatment and achieve the goal of pain reduction for their patients. In this study, we applied data-mining techniques from artificial intelligence technologies, in order to implement a computer-aided auxiliary differential diagnosis for a herniated intervertebral disc, spondylolithesis, and spinal stenosis. We collected questionnaires from 361 patients and analyzed the resulting data by using a linear discriminant analysis, clustering, and artificial neural network techniques to construct a related classification model and to compare the accuracy and implementation efficiency of the different methods. Our results indicate that a linear discriminant analysis has obvious advantages for classification and diagnosis, in terms of accuracy. We concluded that the judgment results from artificial intelligence can be used as a reference for medical personnel in their clinical diagnoses. Our method is expected to facilitate the early detection of symptoms and early treatment, so as to reduce the social resource costs and the huge burden of medical expenses, and to increase the quality of medical care. [ABSTRACT FROM AUTHOR]

Mobile health (M-Health) has become a novel method for HIV prevention and the effects need to be promoted. The study purpose was to exam how a smartphone application (app) reduces HIV risky behaviour in men who have sex with men (MSM). The Safe Behaviour and Screening (SBS) app was developed, and included five features: record, output, and resources connection; information provision; testing services; interaction; and online statistics. A random assignment was used. The experimental group used the SBS app for six months. The control group did not use any intervention. There were 130 participants in the experimental group, and 135 in the control group. The average age of all subjects was 27.38 (SD = 5.56). Compared to the control group, the experimental group had significantly higher mean score of safe behaviour knowledge, motivation, and skills; percentage of condom use during anal intercourse; frequency of searching for testing resources and getting HIV and syphilis tests. The frequency of anal intercourse and recreational drug usage were significantly lower in the experimental group. The SBS app could decrease the HIV risky behaviour among MSM and be applied to HIV prevention and nursing intervention. [ABSTRACT FROM AUTHOR]

ObjectiveThe present study investigated the current status of fruit and vegetable intake among seniors and assessed the relationship between personal background factors, social psychological factors and environmental factors of the study participants and their fruit and vegetable consumption behaviour.DesignResearch data were collected through individual interviews using a questionnaire developed by the authors. SPSS for Windows 15·0 statistical software was used to process and analyse the data.SettingElderly individuals sampled from all twenty-nine administration units of Keelung City’s Renai District were interviewed.SubjectsStudy participants included 398 residents aged 65 years or older.ResultsOn average, study participants ate five daily servings of fruits and vegetables on 2·86 d/week. The important variables influencing fruit and vegetable consumption were education level, outcome expectancy, social support, self-efficacy, frequency of dining out and role modelling. Educated participants consumed more fruits and vegetables than those without education. Outcome expectancy, social support, self-efficacy and role modelling had positive impacts on fruit and vegetable intake, but frequency of dining out had a negative impact on fruit and vegetable intake. The significant predictors of fruit and vegetable intake behaviour were education level, outcome expectancy, social support and frequency of dining out. Among those variables, social support was the most influential factor.ConclusionsOur findings supported the conclusion that health education strategies to increase fruit and vegetable intake among seniors should include the variables of social support and outcome expectancy. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Aim Methods Results Conclusions Hypertension is a chronic disease that consumes considerable medical resources.The aim of this study is to evaluate a model able to develop and assess the effectiveness of a mobile application disease self‐management programme regarding mental health and self‐management for patients with hypertension.A randomised controlled trial and single‐blind random assignment was employed to group the participants. A total of 100 patients were collected from the cardiology clinic of a teaching hospital in Taiwan. There were 50 participants in each group: the mobile application group (experimental group) and the routine care (control group). The primary outcome measures were mental health and self‐management.This study discovered that the intervention improved the mental health and self‐management of the experimental group. The results for the experimental group revealed significantly improved effects and outcomes superior to those of the control group.The study results verified that a mobile application disease self‐management programme could improve the mental health and disease self‐management of patients with hypertension. [ABSTRACT FROM AUTHOR]

Background: Although adolescent birth rates have declined globally, the sexual and reproductive health of adolescent mothers remains an area of specific concern, and these were impacted by the COVID-19 pandemic. This study investigates characteristics, utilization of reproductive health services (RHS) and artificial intelligence (AI) prediction during the pandemic., Methods: We conducted an exploratory study using data for 2020-2022 from the Taipei City Government Health Bureau. Adolescent mothers under the age of 20 received post-birth telephone-based RHS, covering contraception, abortion, postpartum care, and social welfare support. The data analysis included descriptive statistics, and various machine learning techniques were employed, including random forest, SVM, KNN, logistic regression, and Bayesian network analysis., Results: Of 112 participants, most were aged 17 to 19 (80.4%) and married (58.0%). The majority had full-term deliveries (86.6%) with healthy infants. A high percentage had not used contraception before conception (60.7%), and some had had earlier abortion or termination experiences (13.4%). In the examination of eight influential factors, the machine learning models, specifically the random forest and Bayesian network analyses, exhibited the highest accuracy, achieving 90.91% and 89%, respectively, in predicting service acceptance. The key determinants identified were abortion experience and marital status, directly influencing the acceptance of services., Conclusion: The COVID-19 pandemic reduced hospital visits for adolescent mothers, but the RHS provided timely guidance. Telemedicine consultations and internet-based psychological consultations may play a crucial role in facilitating such services in the future., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

Heart disease can lead to physical disability and mortality, ranking second among the top 10 causes of death according to the Ministry of Health and Welfare. This study aims to examine the impact of the interactive assessment application on patients' public health knowledge. In this study, a single-group pretest and posttest experimental design was adopted. Thirty-six hospitalized patients diagnosed with heart failure participated in the pretest and posttest assessments. The findings demonstrate that the developed application led to an increase in the number of recorded physiological measurements, effectively enabling patients to manage their blood pressure. The heart failure self-management application was observed to improve patients' understanding and awareness of heart failure disease, improving their self-management skills., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc.)

Breast cancer is the second leading cause of death in the world and the age of diagnosis is younger and younger. The research is aimed to make a prediction model related to environmental hormones and breast cancer incidence. First, we analyzed lab data to figure out the risk factor of breast cancer. By using Chi-square, Neural network and logistic regression, we find out that Lead, Copper, Zinc, Mercury, Chromium, Chloramphenicol, Sulfonamides, Penicillin and metabolites of phthalates MEP, MBHP related to incidence of breast cancer. These risk factors will be verified by questionnaire of daily habit survey of breast cancer patients. We will establish the relationship between breast cancer and environmental hormones and make public attention to risks of environmental hormones.

This study explores using AI for detecting lymph node metastasis in breast cancer, aiming to analyze extensive medical imaging data. AI assists clinicians in identifying metastases and formulating precise treatment plans. Employing image analysis and machine learning, the research assesses AI's potential to recognize patterns within medical imaging data. The study evaluates the feasibility of integrating AI-based detection into diagnostic workflows. Collaborative efforts with hospitals include collecting annotated breast cancer lymph node data, optimizing workflows, and clinically validating results. Anticipated outcomes aim to highlight AI's crucial role in enhancing early detection and treatment decisions for breast cancer patients. Anticipated results aim to underscore the crucial role of AI in improving early detection and treatment decision-making for breast cancer patients while optimizing efficiency in the operating room nursing workflow.

Heart failure (HF) is a prevalent global health issue projected to escalate, notably in aging populations. The study aimed to identify predictive markers for Heart Failure with preserved Ejection Fraction (HFpEF). We scrutinized vital parameters like age, BMI, eGFR, and comorbidities like atrial fibrillation, coronary artery disease (CAD), diabetes mellites (DM). Evaluating phonocardiogram indicators-third heart sound(S3) and Systolic Dysfunction Index (SDI)-our logistic regression revealed age (≥ 65years), BMI (≥ 25 kg/m2), eGFR (<60 mL/min/1.73m2), CAD, DM, S3 intensity ≥5, and SDI ≥5 as HFpEF predictors, with AUC = 0.816 (p < .001). ROC diagnosis curve showed that the sensitivity, specificity and Youden's index J of the model were 0.755, 0.673 and 0.838, respectively. Nonetheless, further exploration is crucial to delineate the clinical applicability and constraints of these markers.

Contemporary society faces significant public health challenges with prevalent unhealthy behaviors and environmental risks leading non-communicable diseases. This study assesses health awareness campaigns'to effectiveness in a specific Taiwanese municipality, focusing on the impact of a health care management system. Objectives include enhancing public health literacy and evaluating community health promotion. The proposed system includes a personalized assistant app, personnel education, and events covering topics like hypertension, diabetes, diet, exercise, and chronic disease prevention. In summary, the research advocates for effective public health interventions, integrating digital technologies, personalized health management, and community engagement to promote health literacy and healthier lifestyles.

Smart care has become a trend in care institutions and households in recent years. Ambient-assisted living (AAL) has been a topic of increased academic interest over the past decade in line with societal aging and the proliferation of internet and mobile technologies. At the extreme end of AAL is "over-science", a situation in which human functions are over replaced by scientific technologies. This may not only jeopardize the health of older individuals but exacerbate the progress of their dysfunctions by ignoring their desire for self-respect and autonomy. Therefore, the aim of AAL should be to create a web ecosystem rather instead of creating a linearly clustered combination of computerized gadgets.

Background: This study aims to investigate the effects of a nursing quality control and audit application (app) on the autonomous learning of nursing staff and nursing quality management by nursing supervisors. A multilevel interactive app is developed to assist nursing staff in conducting online autonomous learning and nursing supervisors in identifying problems and creating nursing quality improvement plans. The app could also present the different evaluation results of wards in visual charts for supervisors to review., Methods: A single-group pre- and post-test design was applied. Data were collected from 131 nurses between October 2019 and October 2020 to analyze the differences between nursing staffs' willingness to engage in autonomous learning and the integrity of nursing quality improvement plan writing before and after the intervention. The structured questionnaires included open-ended questions that cover aspects of nursing quality control, the audit app, and the information acceptance intention of nurses., Results: The participants' age and job title are negatively correlated with the app's usability, while the ability to use 3C (Computer, Communication, and Consumer Electronics products including mobile phones and laptops) equipment is positively correlated with the willingness to use the app. Nurses' satisfaction with the convenience of the online autonomous learning method is 92%, which indicates that the app could improve their willingness to learn. Following the intervention of the app, nursing supervisors' satisfaction with the integrity of nursing quality improvement plan writing increased from 41 to 88%., Conclusions: Using information technology products to assist in nursing quality management in clinical practice has a significant effect on nurses' load reduction and head nurses' satisfaction. Multilevel interactive nursing quality control and audit apps can improve nursing staff's willingness to learn independently, nursing quality, and the integrity of plan writing. Thus, nursing quality control and audit apps can be considered as suitable nursing quality control tools., (© 2021. The Author(s).)

Electronic health record (EHR) systems have been used widely in research. However, most of the EHRs are highly dimensional and it is challenging to analyze such large data set. Bioinformatics is an interdisciplinary science with a focus on data management and interpretation for complex biological phenomena. We investigated biomarkers of nutrition from 3001 patients. Multivariate-adjusted hazard ratios of mortality were calculated according to both albumin and sodium levels. We explore the association of aging predicted by all-cause mortality in future.

In 2009, the Department of Health, part of Taiwan's Executive Yuan, announced the advent of electronic medical records to reduce medical expenses and facilitate the international exchange of medical record information. An information technology platform for nursing records in medical institutions was then quickly established, which improved nursing information systems and electronic databases. The purpose of the present study was to explore the usability of the data mining techniques to enhance completeness and ensure consistency of nursing records in the database system.First, the study used a Chinese word-segmenting system on common and special terms often used by the nursing staff. We also used text-mining techniques to collect keywords and create a keyword lexicon. We then used an association rule and artificial neural network to measure the correlation and forecasting capability for keywords. Finally, nursing staff members were provided with an on-screen pop-up menu to use when establishing nursing records. Our study found that by using mining techniques we were able to create a powerful keyword lexicon and establish a forecasting model for nursing diagnoses, ensuring the consistency of nursing terminology and improving the nursing staff's work efficiency and productivity.