Health Informatics Journal最新文献

Objective: This paper describes Victoria, an empirically built data pipeline for SNDS to: - Build an automated, scalable pipeline supporting changes to the data model inherent to the use of large databases, - Deliver a documented pipeline with clear processes, enabling scientific, epidemiological researches, - Ease access to SNDS data in compliance with regulatory requirements. Methods: This paper describes the 2-steps process of the Victoria pipeline and its final output. The initial cleaning step consists in formatting, deleting empty, error or duplicate records and renaming variables without changing their values, accordingly with the official SNDS documentation. The second step consists in creating 2 linearised data models: every line of each table is an event, and each table is indexed with a unique patient identifier, without the need for a central patient or identifier table. These 2 models are: - the epidemiological model, used for answering most of the research questions requiring population phenotyping (demography, diagnosis, procedures characteristics). - the medico-economic model is used for costs and healthcare consumption analyses. It contains more complex information about reimbursements rates and the data quality assessment is focused on costs rather than medico-administrative information. Results: The pipeline was executed on 2 different datasets representing ∼85 000 and ∼870 000 beneficiaries with the following configuration: one master with 4 cores and 16Go of RAM and respectively 4 and 6 workers. The total execution time for the smaller dataset was 25 h and 96 h for the larger one. The longest part of those times is represented by the format conversion to parquet. The cleaning step took only 4 h in both cases. The epidemiological model took 344 min for the smaller dataset and 1934 min for the larger one. The medico-economic model took the longest time with 704 min and 2145 min, respectively. Conclusion: Victoria pipeline is a successfully implemented SNDS pipeline. Compared to previous pipelines, reviewability is part of its design as unit tests and quality assessments can natively be developed to ensure data and analysis quality. The pipeline has been used for 2 published studies. The recent work toward OMOP conversion will be integrated in upcoming versions and, as Victoria is set to run on a CD platform, the potential evolution if SNDS format can be considered.

Prompt administration of radiotherapy (RT) is one of the most effective treatments against cancer. Each day, the radiotherapy departments of large hospitals must plan numerous irradiation sessions, considering the availability of human and material resources, such as healthcare professionals and linear accelerators. With the increasing number of patients suffering from different types of cancers, manually establishing schedules following each patient's treatment protocols has become an extremely difficult and time-consuming task. We propose an optimization algorithm that automatically schedules and generates patient appointments. The model can rearrange fixed appointments to accommodate urgent cases, enabling hospitals to schedule appointments more efficiently. It respects the different treatment protocols and should increase staff and patient satisfaction. The optimization algorithm can be connected to a mobile application allowing patients to accept or refuse appointment changes for rescheduling radiotherapy treatments.

Background: The efficacy of mHealth apps in managing hypertension has been proven; however, low usage intention remains a significant challenge, warranting an in-depth exploration of the influencing factors. Objectives: This study aimed to examine the factors influencing hypertensive new users' intention to use mobile health applications through a cross-sectional survey. Methods: Fuzzy-set qualitative comparative analysis (fsQCA) was employed to investigate the combinations of various determinants, including technology acceptance, adoption factors, compliance behavior initiation factors, and time motivation factors for decision making. Results: A total of 100 middle-aged and elderly hypertensive individuals participated in the survey, with 98 responses included in the final statistical analysis. The analysis identified four distinct configurations that contribute to high usage intentions, with solution consistency and coverage values of 0.93 and 0.36, respectively. Conclusion: The findings suggest that intervention strategies should account for the various pathways leading to usage intentions.

Objective: Purpose of this cross-sectional study was to investigate young adults' eHealth literacy levels, use, and readiness to use eHealth and eWelfare. Methods: An electronic survey based on Readiness and Enablement Index for Health Technology (READHY) was aimed at young adults in the geographical are of one wellbeing services county in Southern Finland. Data were analyzed using non-parametrical statistical methods. Results: Young adults (N = 110) actively used eHealth and eWelfare and assessed themselves as having good general digital skills. They were confident in their eHealth literacy and readiness for the use of eHealth and eWelfare. However, young adults not in education, employment, or training (NEETs, n = 21) were significantly less confident than non-NEETs (n = 89) in three of the five domains describing eHealth literacy, and readiness for the use of health technology. Conclusions: The differences between NEETs and non-NEETs indicate that further research on NEETs' and other subgroups' abilities to use eHealth and eWelfare is needed to ensure that these services can be fully utilized.

Background: Gestational diabetes mellitus (GDM) is one of the risk factors for postpartum urinary incontinence. Pelvic floor muscle training (PFMT) improves pelvic floor dysfunction in puerperal women, but patient compliance is low. Mobile Health (mHealth) is a promising solution. Objective: To investigate PFMT compliance and effects on pelvic floor muscles in GDM puerperal women guided by the mobile fitness app Keep. Methods: This randomized controlled trial included puerperal women with GDM (n = 72) who were delivered at a tertiary general hospital, selected from November 2021 to April 2022 using convenience sampling, and randomly divided into control (n = 36) and experimental (n = 36) groups. The control group performed PFMT based on routine postpartum PFMT training instruction. The experimental group performed PFMT based on Keep. Both groups had a 4-week intervention period. The PFMT compliance, International Consultation on Incontinence Questionnaire Short Form (ICIQ-SF), Pelvic Muscle Self-efficacy Scale, and the Knowledge, Attitude, Belief, and Practice (KAP) scores of PFMT in puerperal women in the groups were compared pre- and post-intervention. Pelvic floor surface electromyographic biofeedback was used to compare the post-intervention pelvic floor muscle strength between the two groups. Results: Compared with the control group, the test group had higher post-intervention maternal PFMT compliance, pelvic floor muscle strength, pelvic floor muscle self-efficacy, and KAP scores (p < 0.05); incontinence scores were lower (p < 0.05). Pelvic floor muscles in both groups recovered better post-intervention (p < 0.05). Conclusion: The Keep app can improve PFMT adherence, urinary incontinence, KAP scores, self-efficacy, and pelvic floor muscle strength in GDM puerperal women and promote pelvic floor rehabilitation after delivery.

Objectives: Metabolic syndrome (MetS) has a significant impact on health. MetS is the umbrella term for a group of interdependent metabolic threats that contribute to the emergence of diseases that can lead to death. This study was designed to better predict the risks associated with MetS to enable medical personnel to make more optimal preventive medical decisions. Study design: Data from a large hospital survey database was used to train data mining classification techniques to predict patient-level risk subsequent to extensive data engineering that included aggregating predictors from multiple visits. Methods: A prospective group of seemingly healthy volunteers from the database was studied based on data obtained during their regular annual health checkups. Results: After aggregating the variables over time, the findings indicated that the predictive power of our model outperformed methods presented in other studies (AUC = 0.947). Specific lifestyle factors were identified as contributing to MetS. Conclusion: Involvement to avoid recurring diseases can significantly decrease medical problems and treatment expenses. The findings emphasize the importance of using predictive tools in healthcare and preventive medicine. The results can be used for future prevention strategies that encourage lifestyle changes and implement directed medical treatment protocols to decrease the burden of illness.

Introduction: Spanish speakers rely on social media for health information, with varying quality of its content. This study evaluates the reliability, completeness, and quality of type 2 diabetes (T2D) information available in Spanish-language videos on YouTube and Facebook. Methods: Analytical observational study that included Spanish-language videos on TD2 available on Facebook and YouTube. General characteristics, interaction and generating sources are described. Standardized tools were used to assess reliability, completeness and overall quality. Results: We included 172 videos, 90 from Youtube® and 82 from Facebook®. The median number of views was 1725 (IQR 213-10,000), with an average duration of 5.93 minutes (IQR 3.2-16.8) and an internet time of 834 days (IQR 407-1477). Most videos were uploaded by independent users (58.72%). Reliability (evaluated with DISCERN tool) had a median of 3 (IQR 2-3), completeness (content score) had a median of 2 (IQR 1-3), and overall quality, evaluated with the Global Quality Score (GQS) tool had a median of 3 (IQR 3-4). Using a global classification of "subjective reliability" 92.4% of the videos were considered reliable. Better completeness was observed in Facebook videos (p < .001). Reliability was better for videos from government or news organizations. Conclusion: Our results suggest that videos about T2D in Spanish on social media such as YouTube and Facebook have good reliability and quality, with greater exhaustiveness in content in Facebook videos and greater reliability for videos from government or news organizations.

Purpose: In the context of Chinese clinical texts, this paper aims to propose a deep learning algorithm based on Bidirectional Encoder Representation from Transformers (BERT) to identify privacy information and to verify the feasibility of our method for privacy protection in the Chinese clinical context. Methods: We collected and double-annotated 33,017 discharge summaries from 151 medical institutions on a municipal regional health information platform, developed a BERT-based Bidirectional Long Short-Term Memory Model (BiLSTM) and Conditional Random Field (CRF) model, and tested the performance of privacy identification on the dataset. To explore the performance of different substructures of the neural network, we created five additional baseline models and evaluated the impact of different models on performance. Results: Based on the annotated data, the BERT model pre-trained with the medical corpus showed a significant performance improvement to the BiLSTM-CRF model with a micro-recall of 0.979 and an F1 value of 0.976, which indicates that the model has promising performance in identifying private information in Chinese clinical texts. Conclusions: The BERT-based BiLSTM-CRF model excels in identifying privacy information in Chinese clinical texts, and the application of this model is very effective in protecting patient privacy and facilitating data sharing.

Objective: Explore deep learning applications in predictive analytics for public health data, identify challenges and trends, and then understand the current landscape. Materials and Methods: A systematic literature review was conducted in June 2023 to search articles on public health data in the context of deep learning, published from the inception of medical and computer science databases through June 2023. The review focused on diverse datasets, abstracting applications, challenges, and advancements in deep learning. Results: 2004 articles were reviewed, identifying 14 disease categories. Observed trends include explainable-AI, patient embedding learning, and integrating different data sources and employing deep learning models in health informatics. Noted challenges were technical reproducibility and handling sensitive data. Discussion: There has been a notable surge in deep learning applications on public health data publications since 2015. Consistent deep learning applications and models continue to be applied across public health data. Despite the wide applications, a standard approach still does not exist for addressing the outstanding challenges and issues in this field. Conclusion: Guidelines are needed for applying deep learning and models in public health data to improve FAIRness, efficiency, transparency, comparability, and interoperability of research. Interdisciplinary collaboration among data scientists, public health experts, and policymakers is needed to harness the full potential of deep learning.

Background: Increasing digitisation in healthcare is flowing through to quality improvement strategies, like audit and feedback. Objectives: To systematically review electronic audit and feedback (e-A&F) interventions in hospital settings, examining contemporary practices and quantitatively assessing the relationship between features and effectiveness. Methods: We performed a systematic review using a structured search strategy from 2011 to July 2022. Searches yielded a total of 5095 unique publications, with 152 included in a descriptive synthesis, reporting publication characteristics and practices, and 63 in the quantitative synthesis, to evaluate the effect size of intervention features. Results: The search returned publications across characteristics, including countries of origin, feedback topics, target health professionals, and study design types. We also identified an association with effectiveness for all but one of the features examined, with a Cohen's d ranging from above +0.8 (a large positive effect), to -0.67 (a medium negative effect). Socio-technical features related to supportive organisations and the involvement of engaged health professionals were most associated with effective interventions. Conclusion: Key findings have confirmed that a common set of features of e-A&F systems can influence effectiveness. Results provide practitioners with insight into where resources should be focused during the implementation of e-A&F.