Informatics for health & social care最新文献

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.

Pushes toward earlier detection of Alzheimer's disease (AD)-related cognitive changes are creating interest in leveraging technologies, like cellphones, that are already widespread and well-equipped for data collection to facilitate digital monitoring for AD. Studies are ongoing to identify and validate potential "digital biomarkers" that might indicate someone has or is at risk of developing AD dementia. Digital biomarkers for AD have potential as a tool in aiding more timely diagnosis, though more robust research is needed to support their validity and utility. While there are grounds for optimism, leveraging digital monitoring and informatics for cognitive changes also poses ethical challenges, related to topics such as algorithmic bias, consent, and data privacy and security. As we confront the modern era of Alzheimer's disease, individuals, companies, regulators and policymakers alike must prepare for a future in which our day-to-day interactions with technology in our daily life may identify AD-related cognitive changes.

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.

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.

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.

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.

Introduction: Stimulating recreational and leisure activities (RLAs) are essential to physical and mental well-being; however, people living in memory care units (MCUs) may lack access to them. Technology has the potential to facilitate and enrich activity engagement in this context.

Objectives: In this 6-month study, we evaluated a commercially available system designed to encourage the engagement of people with dementia in activities and social interactions, using a mixed-methods approach in a MCU.

Methods: Quantitative measures included those to evaluate cognition, depression, quality of life, and resource utilization. We qualitatively evaluated the system using semi-structured interviews with family members and staff. Five residents with dementia, four family members, and seven staff were included in the 6-month study.

Results: Staff and family members reported benefits for residents such as enjoyment, interactions and connections with others, and mental stimulation. Findings also highlight challenges such as technical and ethical concerns. Factors that influence system use and integration are also discussed.

Conclusion: It was feasible to introduce a system designed for recreation and engagement in a MCU, and staff, family members, and residents experienced benefits. However, barriers existed in the introduction and use of the system.