Informatics for Health & Social Care最新文献

To develop software to stimulate cognitive functions of attention, memory, reasoning, planning, language, and perception in Mexican older adults, and to evaluate the usability of software based on system utility, information quality, and interface quality.For the development of the cognitive stimulation software, an inductive-deductive methodology was used in three stages: Analysis (system requirements), design and coding (cognitive stimulation software), evaluation (usability results).The usability of the software was assessed in 89 older adults between the ages of 60 and 84 years, through a usability questionnaire with evidence of reliability and validity.Eight exercises about attention, seven on memory, three on reasoning, one about planning and language, and two on perception were developed. We evaluated the usability of the developed software using the Computer System Usability Questionnaire, obtaining medium-high usability in 76.2% of the participants regarding the system utility, in 77.7% concerning the information quality and, in 84.2% in the interface quality.The software was developed considering aspects of usability and based on changes and losses associated with aging, as well as on the stimulation of cognitive functions related to instrumental activities of daily living, including exercises based on traditional pencil-paper exercises.

The lockdown restrictions that have emerged during the COVID-19 pandemic have reshaped the way people live, work, and interact with each other. At the same time, it changed the way health-care professionals and national health-care systems around the world are fighting in this battle for public health. Social media (SoMe) have played their informational role in this fight with almost one-third of the world's population being active users of social media platforms. Contemporary health-care systems have tried to find ways to engage more actively with SoMe as Internet users are increasingly searching for health information on social media platforms. As a result, new demand-side levers arise in the health-care sector along with new opportunities and risks for the stakeholders. Our study looked into the responses of 173 health-care professionals in Greece. SoMe are here to stay and the majority of health-care professionals embrace them in their professional lives. Quality in health information and the work context of Greek health-care professionals in our cohort contribute to attitudes and perceptions of social media use in health care.

With the increased use of patient portals in acute and chronic care settings as a strategy to support patient care and improve patient-centric care, there is still little known about the impact of patient portals in mental health contexts. The purposes of this review were to: 1) identify the critical success factors for successful patient portal implementation and adoption among end-users that could be utilized in a mental health setting; 2) uncover what we know about existing mental health portals and their effectiveness for end-users; and 3) determine what indicators are being used to evaluate existing patient portals for end-users that may be applied in a mental health context. This scoping review was conducted through a search of six electronic databases including Medline, EMBASE, PsycINFO, and CINAHL for articles published between 2007 and 2021. A total of 31 articles were included in the review. Critical success factors of patient portal implementation included those related to education, usefulness, usability, culture, and resources. Only two patient portals had articles published related to their effectiveness for end-users (one in Canada and the other in the United States). More than 100 measures of process (n = 73) and outcome (n = 59) indicators were extracted from the studies and mapped to the Benefits Evaluation Framework. Patient portals carry great potential to improve patient care, but more attention needs to be given to ensure they are being evaluated through the development and implementation phases with the end-users in mind. Further understanding of process indicators relating to use are essential for long-term patient adoption of portals to obtain their potential benefits.

In neurosurgical or orthopedic clinics, the differential diagnosis of lower back pain is often time-consuming and costly. This is especially true when there are several candidate diagnoses with similar symptoms that might confuse clinic physicians. Therefore, methods for the efficient differential diagnosis can help physicians to implement the most appropriate treatment and achieve the goal of pain reduction for their patients.In this study, we applied data-mining techniques from artificial intelligence technologies, in order to implement a computer-aided auxiliary differential diagnosis for a herniated intervertebral disc, spondylolithesis, and spinal stenosis. We collected questionnaires from 361 patients and analyzed the resulting data by using a linear discriminant analysis, clustering, and artificial neural network techniques to construct a related classification model and to compare the accuracy and implementation efficiency of the different methods.Our results indicate that a linear discriminant analysis has obvious advantages for classification and diagnosis, in terms of accuracy.We concluded that the judgment results from artificial intelligence can be used as a reference for medical personnel in their clinical diagnoses. Our method is expected to facilitate the early detection of symptoms and early treatment, so as to reduce the social resource costs and the huge burden of medical expenses, and to increase the quality of medical care.

Objective: The objective of this paper is to provide empirical guidance by comparing the performance of six different area-level SDoH measurement approaches in predicting patient referral to a social worker and hospital admission after a primary care visit.

Methods: We compared the performance of six area-level SDoH measurement approaches in predicting patient referral to a social worker and hospital admission after a primary care visit using random forest classification algorithm. Data came from 209,605 patient encounters at a federally qualified health center. Models with each area-based measurement approach were compared against the patient-level data only model using area under the curve, sensitivity, specificity, and precision.

Results: Addition of area-level features to patient-level data improved the overall performance of models predicting need for a social worker referral. Entering area-level measures as individual features resulted in highest model performance.

Conclusion: Researchers seeking to include area-level SDoH measures in risk prediction may be able to forego more complex measurement approaches.

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.