Informatics for Health & Social Care最新文献

To explicate how experiences with patient-accessible electronic health records correspond to the expectations of the users, we present qualitative results of older adults' experiences with the Finnish national patient-accessible health record My Kanta and similar services. 24 persons, 17 women and 7 men aged 55-73, took part in the study. We interviewed six focus groups of 3-5 participants with previous experience of My Kanta, in two cities in Finland. We used a convenience sample and video- and audio-recording as well as note taking. The interviews were transcribed verbatim. The inductive analysis was based on content analysis. We identified major uses, enablers, barriers, and outcomes of My Kanta. In addition to earlier reported barriers and enablers, the findings show that launch-time lack of useful content and features in systems still under development can cause frustration and hinder their effective use at the time and in the long run. Concerns and barriers relating to use were socio-techno-informational and tightly associated with the contents of the system. Improved security, usability and additional information and functions might increase use. Furthermore, coherent and timely information from health-care providers should be available in the e-health services.

Burden due to infectious and noncommunicable disease is increasing at an alarming rate. Social media usage is growing rapidly and has become the new norm of communication. It is imperative to examine what is being discussed in the social media about diseases or conditions and the characteristics of the network of people involved in discussion. The objective is to assess the tools and techniques used to study social media disease networks using network analysis and network modeling. PubMed and IEEEXplore were searched from 2009 to 2020 and included 30 studies after screening and analysis. Twitter, QuitNet, and disease-specific online forums were widely used to study communications on various health conditions. Most of the studies have performed content analysis and network analysis, whereas network modeling has been done in six studies. Posts on cancer, COVID-19, and smoking have been widely studied. Tools and techniques used for network analysis are listed. Health-related social media data can be leveraged for network analysis. Network modeling technique would help to identify the structural factors associated with the affiliation of the disease networks, which is scarcely utilized. This will help public health professionals to tailor targeted interventions.

Childhood stunting is a serious public health concern in Bangladesh. Earlier research used conventional statistical methods to identify the risk factors of stunting, and very little is known about the applications and usefulness of machine learning (ML) methods that can identify the risk factors of various health conditions based on complex data. This research evaluates the performance of ML methods in predicting stunting among under-5 aged children using 2014 Bangladesh Demographic and Health Survey data. Besides, this paper identifies variables which are important to predict stunting in Bangladesh. Among the selected ML methods, gradient boosting provides the smallest misclassification error in predicting stunting, followed by random forests, support vector machines, classification tree and logistic regression with forward-stepwise selection. The top 10 important variables (in order of importance) that better predict childhood stunting in Bangladesh are child age, wealth index, maternal education, preceding birth interval, paternal education, division, household size, maternal age at first birth, maternal nutritional status, and parental age. Our study shows that ML can support the building of prediction models and emphasizes on the demographic, socioeconomic, nutritional and environmental factors to understand stunting in Bangladesh.

The World Health Organization called for mobile health initiatives to improve population health outcomes, particularly in limited-resource settings. The aim of our study was to reflect upon approaches embedded in cultural humility and recognize areas where improvement was needed in the social innovation collaborative development of an mHealth app to improve hypertension health literacy in India. A qualitative descriptive case study approach was employed to elicit concepts of cultural humility and areas for improvement derived from communications between project stakeholders. Overarching themes included fostering coalescence and strengthening partnerships in addition to multiple subthemes. Enveloping cultural humility in multidisciplinary, interprofessional and cross-cultural healthcare projects and processes is imperative for the development and implementation of successful culturally congruent health initiatives. Team fostering of coalescence and recognizing challenges and adapting to mitigate challenges can strengthen partnerships, a desired consequence of cultural humility.

Objective: Given the association between vitamin D deficiency and risk for cardiovascular disease, we used machine learning approaches to establish a model to predict the probability of deficiency. Determination of serum levels of 25-hydroxy vitamin D (25(OH)D) provided the best assessment of vitamin D status, but such tests are not always widely available or feasible. Thus, our study established predictive models with high sensitivity to identify patients either unlikely to have vitamin D deficiency or who should undergo 25(OH)D testing.Methods: We collected data from 1002 hypertensive patients from a Spanish university hospital. The elastic net regularization approach was applied to reduce the dimensionality of the dataset. The issue of determining vitamin D status was addressed as a classification problem; thus, the following classifiers were applied: logistic regression, support vector machine (SVM), random forest, naive Bayes, and Extreme Gradient Boost methods. Classification accuracy, sensitivity, specificity, and predictive values were computed to assess the performance of each method.Results: The SVM-based method with radial kernel performed better than the other algorithms in terms of sensitivity (98%), negative predictive value (71%), and classification accuracy (73%).Conclusion: The combination of a feature-selection method such as elastic net regularization and a classification approach produced well-fitted models. The SVM approach yielded better predictions than the other algorithms. This combination approach allowed us to develop a predictive model with high sensitivity but low specificity, to identify the population that could benefit from laboratory determination of serum levels of 25(OH)D.

To evaluate the usability of the COMPASS application with mixed-methodology, using a citizen science approach. Parents/tutors of 10-11 years old children attending a primary school in Barcelona, Spain, were invited to take part in the study. We conducted semi-structured interviews on a subset (n = 7) of participants, two weeks after using the app for the first time. A list of suggestions of improvement was extracted from the interviews. The System Usability Scale (SUS, range 0-100) was administered to all participants before and after the improvements were implemented. We provide both a quantitative analysis (t-test of change in SUS scores) and a qualitative thematic analysis of the interviews. A total of 22 participants were included in the study. The mean score before implementation of changes was 68.5 (Standard deviation, SD = 11.1), and improved to 73.1 (10.5) (p-value = 0.025). Regarding the qualitative assessment, we obtained 24 codes and grouped them into 3 categories. It uncovered problems in the installation phase and the main barriers to use: lack of time and the need for the app to evolve. The new version of COMPASS, improved by taking into account the participants' comments and suggestions, was more usable than the initial version.

Several studies have shown that, due to their features, mobile applications have a great potential to address mental health in depression and anxiety. We carried out a systematic review of publications from the last 10 years: from 1 January 2010 until 31 March 2020. Systematic reviews and meta-analyses related to the research question were also selected to identify other potentially eligible studies. The literature search in selected databases returned a total of 3,011 records from which a total of 22 articles were finally selected. The main conclusion of the study is that most of the scientific evidence found supports the hypothesis that mobile applications significantly improve the symptoms associated with depression and anxiety. Therefore, their effectiveness as a digital tool in the treatment of such health problems is proven. However, further studies and further evaluations of mobile applications are required (also in other languages) to incorporate this resource into the healthcare context. In addition, since mobile applications allow reinforcing concepts such as patient empowerment, shared decision-making and health literacy, their use would be highly positive for depression and anxiety, where there is a strong element of self-managing the disease.

Healthcare has been shifting toward individuals participating in decision-making and empowered to be active in their treatment, and health monitoring. The term "participatory health informatics" (PHI) started to appear in literature. A clear definition of PHI is missing, and facets of PHI still have to be shaped. The objective of this paper is to offer a definition of PHI considering themes and technologies that make healthcare participatory. We searched Pubmed, ACM Digital Library, IEEE Xplore, EMBASE, and conference proceedings for articles that reported about use of information technology or informatics in the context of PHI. We performed qualitative synthesis and reported summary statistics. 39 studies were eligible after screening 382 titles and abstracts and reviewing 82 full texts. The top 5 person-centered key themes related to PHI included empowerment, decision-making, informed patient, collaboration, and disease management. Finally, we propose to define PHI as multidisciplinary field that uses information technology as provided through the web, smartphones, or wearables to increase participation of individuals in their care process and to enable them in self-care and shared decision-making. Goals to be achieved through PHI include maintaining health and well-being; improving the healthcare system and health outcomes; sharing experiences; achieving life goals; and self-education.

Chronic pain is common in young people aged 10-14 years. Interdisciplinary, clinician-delivered treatments, while effective, are often criticized for failing to be readily accessible. Mobile health applications (mHealth apps) have been proposed as effective treatment adjuncts that address these challenges, while meeting the needs of tech-savvy young people. The objectives of this study were to co-create a mHealth app with consumers and health care professionals and evaluate the acceptability and feasibility of the resulting mHealth app (myPainPal). A phased, qualitative approach within a consumer engagement framework was employed. Interviews with young people (n = 14), parents (n = 12) and health care professionals (n = 8) identified key health needs that formed the underlying structure of the myPainPal app. Testing showed that the app is an acceptable and feasible platform to facilitate young people's self-management of chronic pain. The myPainPal app has the potential to positively influence young people's experiences of chronic pain. Further testing in controlled settings is required.