Health Informatics Journal最新文献

BackgroundTelerehabilitation (TR) has been increasingly used to deliver psychological and neuropsychological care remotely, especially since the COVID-19 pandemic. As health services continue to shift toward telehealth, ensuring ethical and equitable TR delivery is essential to establish sustainable TR models.ObjectiveThe objective of this review is to synthesize existing evidence on the ethical and equity-related benefits and pitfalls associated with the use of TR in a psychological and neuropsychological context for individuals with physical disabilities.MethodsThis rapid review included reviews (2010-2020) and original studies (2020-2023) that focused on TR interventions for people with physical disabilities in the context of psychology and neuropsychology rehabilitation.ResultsA total of 16 reviews and 82 original articles were included. Key ethical concerns centered around privacy, confidentiality, caregiver burden, and clinician-patient relationship quality. Equity concerns centered around access disparities (e.g., geographic location, income), digital literacy, and demographic underrepresentation.ConclusionThis review is part of a pan-Canadian initiative aimed at informing policy development and clinical practice in TR. Findings highlight the need for clear guidelines and targeted interventions to ensure that TR in psychology and neuropsychology is both ethically sound and equitable.

Objective: This study presents a structured evaluation of large language models (LLMs) in predicting suicide methods based exclusively on indirect forensic psychiatric indicators. Methods: Ninety-two forensic psychiatric cases (2019-2024), involving survivors of suicide attempts formally examined in medico-legal contexts, were retrospectively analyzed. Variables included age, sex, psychiatric diagnosis, previous suicide attempts, psychiatric medication use, impulsivity, and consciousness at emergency admission. Six LLMs were tested: ChatGPT-4o, ChatGPT-4o Mini, ChatGPT-O3 (OpenAI), Gemini 2.0 Flash, Gemini 2.5 Pro, and Gemini 2.5 Flash (Google DeepMind). Each case was converted into a standardized anonymized prompt. Model predictions were categorized by blinded forensic physicians and evaluated using accuracy, precision, recall, F1-score, and Cohen's Kappa for 1-month reproducibility. Results: Gemini 2.5 Flash achieved the highest performance with 76.09% accuracy, 46.9% F1-score, and 45.2% recall. It accurately predicted the dominant method, medication overdose, but underperformed for rare categories. Temporal reproducibility was moderate (κ = 0.582), while other models exhibited lower and less stable performance. Conclusion: LLMs can infer suicide methods from indirect psychiatric data with encouraging accuracy. However, limitations in detecting rare methods and maintaining temporal consistency suggest the need for further methodological refinement and external validation prior to forensic application.

Objectives: Addressing data duplication is one of the most important issues in electronic health record (EHR) processing since the nature of data collection in the field. It does not only affect the data quality in healthcare management, but also the reliability in the downstream analyses. In this paper, we propose a comprehensive data de-duplication framework tailored for medical databases to tackle data duplication for a kidney disease identification, Acute Kidney Failure (AKF). Methods: The proposed work begins with the data joining from various sources, basic data de-duplication which automatically removes the dirty texts, medical note-event extraction since the data could be sources for further de-duplication, NLP data de-duplication based on a pre-trained model, data mapping for integration, unrelated data and outlier elimination, and eventually data imputation by a clustered based imputer. Results: We illustrated our de-duplication framework on MIMIC-III database both on the de-duplication task and the classification task based on AKF. The experiments demonstrated that the proposed work could achieve up to 99.59% accuracy or 23% higher than the traditional method and could achieve a high classification accuracy at 86 % and the F1-score at 0.87, which outperformed the traditional method, and the original dataset without any modification. Conclusion: These results demonstrated that the framework can potentially address the data duplication issue in healthcare effectively.

Background: The rapid expansion of digital health services (DHS) highlights the need to assess their accessibility and effectiveness, particularly among older adults. Despite increasing digitalization, many older individuals still face barriers, including limitations in digital competence and access. Objective: This study examines the use, barriers, and perceived benefits of DHS among individuals aged 75 and older in Finland. Methods: A nationwide survey was conducted in March 2023 using both electronic and paper questionnaires. In addition to descriptive analysis, regression analysis was performed to identify variables associated with perceived benefits of digital health services. Results: Of the 1124 responses (1011 electronic, 113 paper), 1100 were fully completed. Overall, 84% of respondents had used DHS, with usage being higher among those under 85 years (87%) than those over 85 (57%). The majority of respondents (82%) reported using the national Omakanta service, which grants access to personal health information. Digital competence and the number of services used were the strongest predictors of perceived benefits, alongside higher satisfaction, service frequency, and female gender. Conclusions: DHS adoption among older adults, especially in Finland, may be higher than previously reported. However, digital social services remain underdeveloped. Addressing the digital divide is essential to ensuring equitable access.

Objective: Women with prior hypertensive disorders of pregnancy (HDP) have increased risk of developing future cardiovascular disease. The objective of this scoping review was to map the literature regarding the use of eHealth measures in cardiovascular follow-up after HDP and identify research gaps. Methods: A systematic search was conducted in four databases. Primary research articles and guidelines were included. Abstract screening, full-text assessment and data extraction was performed to summarize the findings. Results: The search identified 4830 articles and 12 guidelines. Eleven publications and one guideline were included in the analyses. Various eHealth interventions were assessed, such as remote blood pressure monitoring, physical activity and weight management, with follow-up time from 6 weeks to 4 years. eHealth interventions targeting blood pressure and physical activity showed statistically significant positive effects. Conclusion: The scoping review identified eHealth interventions for cardiovascular follow-up after HDP that may empower women to optimize their cardiovascular health.

ObjectiveAlthough mobile health applications (mHealth apps) are widely used, few studies have explored their adoption from parents' perspectives for early disease detection in toddlers.MethodThis study employs the Uses and Gratifications (U&G) theory to identify key adoption factors. Structural equation modeling (SEM) was conducted using AMOS SEM 26 and SPSS 22 for statistical analysis.ResultsA survey of 308 parents using mHealth apps revealed that intrinsic motivation does not moderate the relationship between information-seeking and satisfaction, whereas social interaction positively moderates this relationship. Parents with strong social interaction tendencies exhibit higher satisfaction in information-seeking.ConclusionThis study contributes to the literature on mHealth adoption, offering insights for developers and policymakers to enhance early detection initiatives and improve parental engagement with mHealth apps.

Background: Effective nurse scheduling promotes patient safety, nurse well-being, and compliance with regulations. Objective: To develop and evaluate a robotic process automation (RPA)-based scheduling system to improve accuracy, reflect nurse preferences, and align with national guidelines. Methods: A total of 102 nurses from a 500-bed hospital were assigned to the experimental or control group. The RPA system was integrated into nurse managers' workflows to automate monthly shift planning. Nurses submitted shift preferences and the system generated schedules accordingly. Pre- and post-intervention data on work characteristics, health status, and work-life balance were analyzed using chi-square and paired t-tests with SPSS. Results: The RPA-based scheduling significantly improved nurses' work-life balance. No significant differences were found in health status or work characteristics. Conclusion: Integrating RPA into nurse scheduling can enhance work-life balance and support fairer scheduling practices. Broader organizational adoption and supportive policies are recommended to ensure sustainable impact and improved care quality.

Suicide is a critical public health issue worldwide, influenced by environmental factors such as economic stress and limited social support, as well as individual risk factors. Patients with chronic health conditions may face heightened vulnerability due to overlapping psychological and medical challenges. This research explores the application of Machine Learning (ML) techniques to identify suicide risk among such patients, utilizing data from the National Health and Nutrition Examination Survey (NHANES). The study incorporated demographic, clinical, and psycho-social variables, including depression, substance use, hypertension, and diabetes, to develop predictive models. Several ML algorithms were trained and evaluated using standard performance metrics to assess predictive accuracy. Among the models, Gradient Boosting Machine (GBM) achieved the strongest performance, with a receiver operating characteristic area under the curve (ROC-AUC) of 0.9479. Random Forest also performed exceptionally, with a ROC-AUC of 0.9301, while four additional models showed competitive results. These algorithms effectively captured complex nonlinear relationships and interactions between multiple risk factors, demonstrating their suitability for multivariable health data. The findings underscore the potential of integrating ML into Electronic Medical Records (EMRs) as decision-support tools to identify high-risk patients. Early detection enables timely interventions, which may significantly improve mental health outcomes and reduce suicide risk.

BackgroundData quality encompasses completeness, accuracy, integrity, timeliness, and confidentiality. In many low- and middle-income countries, including Somalia, data from RHIS are often poor, limiting their usefulness for public health actions. This study assessed the quality of RHIS data and associated factors among public health facilities in the Banadir region, Somalia.MethodsA facility-based cross-sectional study was conducted from October to December 2024 across 36 public health facilities using a multistage sampling approach. Data were collected through document reviews, interviews, and observations using PRISM-based standardized tools. Data were analyzed in SPSS version 27 after checking logistic regression assumptions. Data quality was assessed by the dimensions of accuracy (≥80%), completeness (≥85%), and timeliness (≥85%). Bivariable and multivariable logistic regression analyses identified associated factors.ResultsA total of 398 healthcare workers (59.5% female) participated, yielding a 98% response rate. Overall, good-quality data were observed in 65.3% of departments. Departments in health centers were 2.7 times more likely to report good-quality data than hospitals. Feedback, refresher training, and user-friendly reporting formats were significantly associated with better data quality.ConclusionData quality across the three dimensions was scored at (65.3%). Strengthening supervision, feedback, and context-specific training can improve data reporting and management.

ObjectiveIndigenous people face inequities when accessing primary care. It is critical to center Indigenous people and their lived experiences within primary care improvements to address these ongoing inequities.MethodsUsing the Theoretical Domains Framework (TDF), the purpose of this study was to understand the barriers and facilitators to implementation of an Indigenous patient experience tool in virtual care.ResultsNineteen interviews were completed with participants that included 12 patients and seven physicians and data were analyzed using reflexive thematic analysis. Themes endorsed by participants were directly related to four TDF domains: 1) Beliefs about consequences; 2) Environmental context and resources; 3) Skills; and 4) Knowledge. This study found that both patients and providers found the implementation and use of the Access, Relationships, Quality, and Safety (ARQS) tool both useful and relevant.ConclusionFuture research should explore if sustained and recurrent use of the tool within therapeutic relationships leads to improvements in the delivery of virtual primary healthcare.