Informatics for health & social care最新文献

The socioeconomic costs of neurodegenerative diseases (NDs) are highly affected by comorbidities. This study aims to enhance our understanding of the prevalent complications of NDs through the lens of network analysis. A multimorbidity network (MN) was constructed based on a longitudinal EHR dataset of 93,647,498 diagnoses of 824,847 patients. The association between the conditions was measured by two metrics, i.e. Phi-correlation and Cosine Index (CI). Based on multiple network centrality measures, a fused ranking list of the prevalent multimorbidities was provided. Finally, class-level networks depicting the prevalence and strength of diseases in different classes were constructed. The general MN included 928 diseases and 337,253 associations. Considering a 99% confidence level, two networks of 575 relationships were constructed based on Phi-correlations (73 diseases) and CI (102 diseases). Five out of 19 ICD-9 categories did not appear in either of the networks. Also, ND's immediate MNs for the top 50% of the significant associations included 42 relationships, whereas the Phi-correlation and CI networks included 36 and 34 diseases, respectively. Thirteen diseases were identified as the most notable multimorbidities based on various centrality measures. The analysis framework helps practitioners toward better resource allocations, more effective preventive screenings, and improved quality of life for ND patients and caregivers.

Introduction: The world's population is aging rapidly, leading to increased public health and economic burdens due to age-related cardiovascular and neurodegenerative diseases. Early risk detection is essential for prevention and to improve the quality of life in elderly individuals. Plus, health risks associated with aging are not directly tied to chronological age, but are also influenced by a combination of environmental exposures. Past research has introduced the concept of "Phenotypic Age," which combines age with biomarkers to estimate an individual's health risk.

Methods: This study explores which factors contribute most to the gap between chronological and phenotypic ages. We combined ten machine learning regression techniques applied to the NHANES dataset, containing demographic, laboratory and socioeconomic data from 41,474 patients, to identify the most important features. We then used clustering analysis and a mixed-effects model to stratify by sex, ethnicity, and education.

Results: We identified 28 demographic, biological and environmental factors related to a significant gap between phenotypic and chronological ages. Stratifying for sex, education and ethnicity, we found statistically significant differences in the outcome distributions.

Conclusion: By showing that health risk prevention should consider both biological and sociodemographic factors, we offer a new approach to predict aging rates and potentially improve targeted prevention strategies for age-related conditions.

This study empirically evaluates the functionality coverage of 18 mobile applications (apps) for Postnatal care including a recently developed app in Morocco "Mamma&Baby". This evaluation is based on a comparison of the COSMIC _ISO 19,761 functional size of these apps with the score obtained in a previous evaluation based on functions extraction through a quality assessment questionnaire. This comparison allows to discuss the relationship between the functional size of the 18 apps, their users' ratings in the Play Store as well as the number of downloads. While for most of the assessed apps, there is only a small shift between the rankings of the two evaluations, for some apps, the shift is huge due to the number of features added and not covered by the score previously obtained. This study illustrates the use of COSMIC as an effective method for corrective or evolutionary updates since it takes into account all the functions and features of postnatal apps. For the "Mamma&Baby" app, efforts are required to boost the number of downloads, optimize its visibility, and attract the highest number of users.

Poor adherence to tuberculosis (TB) treatment leads to further disease transmission, worsened outcomes, and the development of drug resistance. Digital adherence technologies may facilitate a more patient-centered approach for improving TB treatment outcomes than current strategies. The objective of this study was to evaluate and explore improving usability of the TB Treatment Support Tools (TB-TST) mobile application. We used an iterative convergent mixed-method design consisting of two quantitative surveys and a qualitative think-aloud interview. Testing was conducted in three testing cycles consisting of a total of 16 interviews and 26 surveys. Results were thematically analyzed and reported to the development team during weekly team meetings. Participants rated the TB-TSTs application as having high usability and the iterative approach resulted in several refinements to the application in response to participant feedback. These refinements were well received during qualitative interviews but did not result in a statistically significant improvement in usability testing scores between cycles. Using an iterative convergent mixed-method design was an effective method for refining our mHealth application. Data collected from think-aloud interviews, the MAUQ, and the Health-ITUES identified key areas of application design that needed refinement.

To assess the overall experience of a patient in a hospital, many factors must be analyzed; nonetheless, one of the key aspects is the performance of nurses as they closely interact with patients on many occasions. Nurses carry out many tasks that could be assessed to understand the patient's satisfaction and consequently, the effectiveness of the offered services. To assess their performance, traditionally, expensive, and time-consuming methods such as questionnaires and interviews have been used; nevertheless, the development of social networks has allowed the patients to convey their opinions in a free and public manner. For that reason, in this study, a comprehensive analysis has been performed based on patients' opinions collected from a feedback platform for health and care services, to discover the topics about nurses the patients are more interested in. To do so, a topic modeling technique has been proposed. After this, sentiment analysis has been applied to classify the topics as satisfactory or unsatisfactory. Finally, the results have been compared with what the patients think about doctors. The results highlight what topics are most relevant to assess the patient satisfaction and to what extent. The results remark that the opinion about nurses is, in general, more positive than about doctors.

The COVID-19 pandemic has exposed significant gaps in healthcare access, quality, and the urgent need for enhancing the capacity of digital health human resources, particularly in Latin America. During the pandemic, online courses and telehealth initiatives supported by governmental agencies, the Pan American Health Organization, and other public and private resources, have played a crucial role in meeting training demands. This article discusses the role of capacity building programs in digital health within the context of Latin America, with a specific focus on the Peruvian case. We highlight the development of digital health competencies and related policies, while also describing selected experiences related to capacity building in this field. Additionally, we discuss the pivotal role of collaborative partnerships among institutions and countries, emphasizing the importance of culturally relevant training programs in digital health. These initiatives have the potential to accelerate training and research opportunities in Latin America, drawing on the involvement of government agencies, non-governmental organizations, industry, universities, professional societies, and communities.

Telehealth transforms the healthcare system and provides the population with equal access to healthcare services at distance. This study aimed to investigate nursing students' perceptions toward telenursing. Students' knowledge, attitudes, advantages, disadvantages, barriers, and factors that affect the intention toward telenursing implementation were addressed. This study was conducted using a descriptive design. The study participants were 313 undergraduate nursing students. Data were gathered through a web-based survey from June to August 2022 and analyzed using SPSS version 22. Fifty-four-point-six percent (54.6%) of the students were male and 45.4% were female. Around one-fourth were internship students. Most students had access to the internet 97.4%. The results revealed that nursing students have positive perceptions toward telenursing. Their overall competencies in terms of knowledge, proficiency, awareness, and familiarity were moderate. Students raised some disadvantages and difficulties regarding telenursing; meanwhile, more advantages and suggestions to overcome the disadvantages were reported. It is of utmost importance that nursing education integrates telenursing content and practice to prepare future nurses for the successful implementation of telenursing. More research is still needed to examine the impact of telenursing on nursing practice. Nursing administrators must develop appropriate and prompt interventions to respond to the dramatically changing healthcare environment.

Underdiagnosis, misdiagnosis, and patterns of social inequality that translate into unequal access to health systems all pose barriers to identifying and recruiting diverse and representative populations into research on Alzheimer's disease and Alzheimer's disease related dementias. In response, some have turned to algorithms to identify patients living with dementia using information that is associated with this condition but that is not as specific as a diagnosis. This paper explains six ethical issues associated with the use of such algorithms including the generation of new, sensitive, identifiable medical information for research purposes without participant consent, issues of justice and equity, risk, and ethical communication. It concludes with a discussion of strategies for addressing these issues and prompting valuable research.

There is a growing literature on the role of mobile health applications (mHealth apps) in supporting older adults and the self-management of personal health. The purpose of this pilot study is to assess the usability of a government-funded mobile health app amongst older Australians and to evaluate whether cognitive function and demographic characteristics (i.e. age, gender, education) are associated with usability. A total of 28 older adults living in a regional city in Australia took part in the study. The participants were recruited using purposive sampling. Data collection instruments consisted of validated cognitive tests, task-based usability tests, and a questionnaire. The data was analyzed using non-parametric strategies. The findings of this study demonstrated that a government-funded, mHealth app was usable by older adults. Users were able to perform basic tasks in an effective and efficient manner. The hypothesis that elderly age would be significantly associated with performance on cognitive tests, as well as usability, was not supported. Performance on some cognitive tests was significantly and positively related to usability. Education and gender were not related to usability. The results suggest that traditional stereotypes surrounding aging and cognitive decline need to be reexamined.

Background: Google Trends data can be a valuable source of information for health-related issues such as predicting infectious disease trends.

Objectives: To evaluate the accuracy of predicting COVID-19 new cases in California using Google Trends data, we develop and use a GMDH-type neural network model and compare its performance with a LTSM model.

Methods: We predicted COVID-19 new cases using Google query data over three periods. Our first period covered March 1, 2020, to July 31, 2020, including the first peak of infection. We also estimated a model from October 1, 2020, to January 7, 2021, including the second wave of COVID-19 and avoiding possible biases from public interest in searching about the new pandemic. In addition, we extended our forecasting period from May 20, 2020, to January 31, 2021, to cover an extended period of time.

Results: Our findings show that Google relative search volume (RSV) can be used to accurately predict new COVID-19 cases.  We find that among our Google relative search volume terms, "Fever," "COVID Testing," "Signs of COVID," "COVID Treatment," and "Shortness of Breath" increase model predictive accuracy.

Conclusions: Our findings highlight the value of using data sources providing near real-time data, e.g., Google Trends, to detect trends in COVID-19 cases, in order to supplement and extend existing epidemiological models.