Health Informatics Journal最新文献

Background and aim: Due to changes in lifestyle, bariatric surgery is expanding worldwide. However, this surgery has numerous complications, and early identification of these complications could be essential in assisting patients to have a higher-quality surgery. Machine learning has a significant role in prediction tasks. So far, no systematic review has been carried out on leveraging ML techniques for predicting complications of bariatric surgery. Therefore, this study aims to perform a systematic review for better prediction insight. Materials and methods: This review was conducted in 2023 based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We searched scientific databases using the inclusion and exclusion criteria to obtain articles. The data extraction form was used to gather data. To analyze the data, we leveraged the narrative synthesis of the quantitative data. Results: Ensemble algorithms outperformed others in large databases, especially at the national registries. Artificial Neural Networks (ANN) performed better than others based on one-single-center database. Also, Deep Belief Networks (DBN) and ANN obtained favorable performance for complications such as diabetes, dyslipidemia, hypertension, thrombosis, leakage, and depression. Conclusion: This review gave us insight into using ensemble and non-ensemble algorithms based on the types of datasets and complications.

Objective: Community health workers work directly in the communities and are the intermediaries between the population's needs and the primary health care teams. Their work focuses on health education and preventing diseases and disorders, accompanying citizens, families, and households in a particular neighborhood. This study sought to analyze the use of the e-SUS Território application in the work of community health workers in Brazil. Methods: Usability data extracted from Google Analytics from 2019 to 2022 were analyzed, including productivity indicators, number and location of users, and engagement. An overview of the application's main features was also provided. Results: The application is an important work tool used by these professionals, who stopped using printed sheets to record their activities and began recording them in a digital, unified, asynchronous way anywhere in Brazil, regardless of internet connectivity. The application had 425,000 active users in 2022, reaching 141,000 monthly active users in June of the same year, representing 54.8% of all community health workers in Brazil. Conclusion: This study demonstrates the wide and exponential adherence of the e-SUS Território application over the years and the increase in the productivity of professionals who use it, facilitating and encouraging the recording of health information.

Background: As healthcare depends on health information technology, there is a growing need for Health Informatics competencies in daily practice. This review aimed to explore how the teaching of education in HI has been arranged. 28 publications, published in English between 2016 and 2020 and obtained from selected bibliographic databases, were reviewed. The data was analyzed using deductive content analysis with the following pre-formulated topics: target audience, course content and learning arrangements. The results highlight three key competencies: documentation and communication, management, and understanding of health information technology. It underlines a blended teaching method to improve the competencies of healthcare professionals, graduates, undergraduates, and suggests adding active interactions, multi-professional interactions, and hands-on skills. This study highlights the importance of adapting to changes in healthcare, improving HI competencies in healthcare, and fostering positive digital experiences. It underlined the need for practical training, in theory and hands-on sessions, including key competencies in documentation and communication, management and health information systems.

Physical inactivity among older adults remains a global burden, leading to a variety of health challenges and even mortality. This study evaluated the impact of a novel virtual humanoid coach-driven physical exercise program among older adults. A non-randomized (quasi) experimental research was conducted in two community senior centers. The recruited participants (n = 130) were primarily female older adults with a mean age of 66.40 and agreed to be purposively assigned either experimental or control groups. Trained healthcare providers performed health assessments in three time points using valid and reliable tools. Descriptive statistics, t-tests, and RM-ANOVA were used to quantitatively analyze the data using SPSS version 22. There are significant mean differences between the groups across all functional capacity assessments and Time 2-3 assessment of sleep quality. RM-ANOVA revealed significant differences in physical assessment over time between the two groups. The analyses of time and group interaction revealed significant improvement in health assessments among the members of the mixed reality group compared to the traditional groups. The impact of virtual coaches in community-based enhancing physical activity programs is comparable to the traditional mode and introduces a novel approach to promoting physical activity among older adults.

Objectives: This article describes how a home visit solution was developed in a co-design process between patients in treatment for severe spasticity, their caregivers and hospital nurses. The solution was developed using a participatory design approach and was based on the identified needs of the participants. Methods: We developed a home visit solution through an iterative process and a collective 'reflection-in-action' approach with patients, caregivers and healthcare professionals. Results: The study revealed the complexities of establishing new routines around home visits. The solution included a new workflow for the nurses and a new route and appointment planning tool. Conclusion: Through a participatory design approach, the users developed a home visit solution that minimised disruption to patients' daily lives and facilitated a dialogue between the nurses and the caregivers about the treatment and the patients' spasticity, which helped to adjust the treatment in line with the patient´s needs.

Background: Despite the many benefits of Health Information Exchange (HIE), Studies reported patients concerns about the privacy and security of sharing their health information. To address these concerns, it is important to understand their needs, preferences, and priorities in the design and implementing HIE systems.

Objective: The aim of this study is to investigate patients' preferences for HIE consent option and examine the extent to which they are comfortable sharing the different parts of their medical records.

Method: A self-administered survey was conducted. The survey was administrated online and the total number of respondents was 660 participants.

Results: The most popular option selected by participants for sharing HIE information was to share information with their permission once when they register (33.3%) followed by the option to share their information temporarily on demand during their clinical visit (23.8%). The types of information which participants were willing to share the most were general data such as age, weight, height, and gender, followed closely by data needed for medical emergency. In contrast, the information which participants were less likely to share were data related to financial status or income, followed by data related to sexual disease, and mental illnesses.

Using data from two ED. departments of 773 patients admitted with SARS-CoV-2, ICD-10 codes derived from the General Practitioner - Summary Care Record (GP-SCR) and Emergency Department (ED.) records were analysed for code discrepancies and whether this related to increased mortality. The average number of ICD-10 codes in both GP-SCR and ED. records was higher for patients who died than patients who survived (all p < .0001). Pre-existing GP digital data provides a better prediction of mortality than data collected manually during admission clerking in the ED. Up to 78.47% of GP-SCR codes were missed in the ED. records and up to 45.49% of the ED. record codes were not in the GP-SCR. A subset of missed ICD-10 codes were identified as being able to predict outcome; a trend towards increasing death rate as the proportion of missed codes increases. Initiatives to make the GP-SCR available to the wider healthcare community should improve patient care and reduce bias during development of machine learning based algorithms.

This study aimed to develop a prototype mobile application to enhance fetal dosimetry among pregnant radiographers in #### through a design thinking approach. Eleven participants were recruited to engage in a participatory design workshop, which encompassed five stages: Empathise, Ideate, Define, Prototype and Test. The participants were divided into two teams. Qualitative datasets from the workshop included field notes and FIGMA screens. The data were analysed through thematic analysis, from which three major themes emerged: (1) Unsafe working environments for pregnant radiographers, (2) The need for enhanced fetal radiation dose monitoring by pregnant radiographers as an occupational health and safety requirement, and (3) Co-designing of the prototype mobile application, PregiDose. The participants contributed towards a prototype mobile application which addressed challenges experienced in the real-life setting. Hence, the prototype can be used as an effective framework by which to guide the development of the final artefact.

Objective: The study aimed to analyze the public interest in wisdom teeth-related search terms as well as regional and seasonal trends based on information from the Google search engine.

Methods: With the help of the online search query tool, Google Trends, the public interest in the primary search term "wisdom teeth" for the timeframe between January 1st, 2004 and September 31st, 2021 was analyzed. To do so, a country-specific search was conducted in English-speaking countries (the USA, the UK, Canada, and Australia) in the northern and southern hemispheres. The extracted time series was examined for reliability, and a Cosinor analysis evaluated the statistical significance of seasonal interest peaks.

Results: The reliability of averaged time series data on the search term "wisdom teeth" was excellent in all examined countries. In all countries analyzed, "wisdom teeth removal" was one of the most common related search terms. Significant interest peaks for wisdom teeth-related search terms were found in Canada and the USA during summer (p < .001). In Canada and the USA, significant seasonal patterns with the highest interest during the summer months, could be displayed.

Conclusion: This phenomenon could be caused by increased wisdom teeth-related complaints induced by seasonal climate changes.

Population Health Management - often abbreviated to PHM - is a relatively new approach for healthcare planning, requiring the application of analytical techniques to linked patient level data. Despite expectations for greater uptake of PHM, there is a deficit of available solutions to help health services embed it into routine use. This paper concerns the development, application and use of an interactive tool which can be linked to a healthcare system's data warehouse and employed to readily perform key PHM tasks such as population segmentation, risk stratification, and deriving various performance metrics and descriptive summaries. Developed through open-source code in a large healthcare system in South West England, and used by others around the country, this paper demonstrates the importance of a scalable, purpose-built solution for improving the uptake of PHM in health services.