Informatics for Health & Social Care最新文献

Electronic clinical pathways (ECPs) strongly encourage the standardization of medical treatment and the sharing of information among medical staff. The goal of this study was to determine the influence of ECPs on information sharing among nurses in a university hospital. Four experienced nurses, selected based on ECP composing and operation experience, were recruited from the department with the most frequent users in the first-round interview, 132 nurses' questionnaire answers were analyzed, and eight nurses participated in the second-round interview. This study conducted a mixed-method (interview-questionnaire-interview) investigation to extract the behavioral signs of unintended errors in information sharing after the ethical approval was obtained. On the basis of ANOVA and t-test for the questionnaire and constant comparison for interview, this study found that the greater extent of user dependency on convenient ECPs in the frequent-use group led to mistakes under hectic conditions. This study also found evidence of poor management of ECPs when problems occurred. The immature design of ECPs provoked inappropriate behaviors among nurses even though they brought about some benefits such as mitigation of the burden of daily recording tasks. The findings empirically showed the ECP user's behavioral changes regarding the technology-induced error.

Data visualization tools have the potential to support decision-making for public health professionals. This review summarizes the science and evidence regarding data visualization and its impact on decision-making behavior as informed by cognitive processes such as understanding, attitude, or perception.An electronic literature search was conducted using six databases, including reference list reviews. Search terms were pre-defined based on research questions.Sixteen studies were included in the final analysis. Data visualization interventions in this review were found to impact attitude, perception, and decision-making compared to controls. These relationships between the interventions and outcomes appear to be explained by mediating factors such as perceived trustworthiness and quality, domain-specific knowledge, basic beliefs shared by social groups, and political beliefs.Visualization appears to bring advantages by increasing the amount of information delivered and decreasing the cognitive and intellectual burden to interpret information for decision-making. However, understanding data visualization interventions specific to public health leaders' decision-making is lacking, and there is little guidance for understanding a participant's characteristics and tasks. The evidence from this review suggests positive effects of data visualization can be identified, depending on the control of confounding factors on attitude, perception, and decision-making.

This study examined the association between caregivers' burdens and their individual characteristics and identified characteristics that are useful for predicting the level of caregiver burden. We successfully surveyed 387 family caregivers, having them complete the caregiver burden inventory scale (CBI) and an individual characteristic questionnaire. When we compared the average CBI scores between groups with a particular individual characteristic (including caring for older adult(s), educational level, employment status, place of birth, marital status, financial status, need for family support, need for friend support, and need for nonprofit organizational support), we found a significant difference in the average scores. From a logistic regression model, with burden level as the outcome, we found that caring for older adult(s), educational level, employment status, place of birth, financial situation, and need for nonprofit organizational support were significant predictors of the burden level of caregivers. The research findings suggest that certain individual characteristics can be adopted for identifying and quantifying caregivers who may have a higher level of burden. The findings are useful to uncover caregivers who may need prompt support and social care.

The Help2Care e-Health platform was developed in order to capacitate informal caregivers with digital, multimedia training materials. Health professionals select these materials according to the needs of the homebound patients under the supervision of these caregivers. In turn, caregiver can then use their smartphones to consult and apply the care procedures illustrated by these materials. In this paper, we present the results of performed usability tests for both web and mobile software applications of the Help2Care platform. These indicate an overall positive outcome, revealing less usable aspects such as the navigation flow in the web application and some design elements in the mobile application. Important written feedback was also collected, which we took into consideration to improve the software features of the platform.

Mental disorders are a critical public health challenge since they profoundly affected people lifestyle. Mental healthcare treatments aim to promote a higher quality of life of the patients. These procedures include interventions for prolonged mental illness which can be supported by telemedicine technologies. This paper presents a comprehensive analysis of mobile applications selected to address the most critical needs of people with mental problems. Needs include areas of the patient's life, such as basic activities, behavioral changes, and daily life tasks. This work has two main objectives; (1) identify critical needs for patients with mental disorders and (2) identify and analyze apps that can meet the identified critical needs. A Delphi methodology survey was carried with a group of thirteen volunteers, including nurses, assistants, and psychiatrists who are working in Zamora and Valladolid, Spain. This survey has recommended different needs for patients with mental disorders and address objective 1. Google Play and Apple Store have been assessed to select the most relevant mobile applications that were recommended in the Delphi study to address the essential needs of these patients according to objective 2. The results of the Delphi survey show 24 different needs for patients with mental disorders. This study has analyzed 62 mobile applications which address the essential needs recommended in the Delphi study. The selected mobile applications represent 31 applications with feedback (50%); 15 informative applications (24%), and 16 independent applications (26%). On the one hand, applications with feedback request can address 13 recommended needs (54%). On the other hand, informative applications can address 7 needs (29%). Finally, the independent applications are only able to respond to 4 of the 24 recommend needs (17%). Mobile health applications present effective technologies to support the needs of patients with mental disorders. However, this study suggests a critical limitation of mobile applications for mental health since the majority of the applications require user activity. Therefore, future research initiatives on the design and development of mobile apps for people who have mental disorders should focus on independent applications.

In this study, we developed a web-based emergency management system to provide timely treatments to patients in emergency conditions. With the integration of geospatial information and technologies, a global positioning system, and optimization technique, we designed a system consisting of two subsystems, emergency reporting and ambulance routing. The reporting subsystem helps in collecting emergency information in urban areas using geocoding and geolocation function, while the routing subsystem generates the optimal route for pick-up operation and selects the nearest hospital for patient delivery process. A committee of 10 experts comprising of seven medical experts and three GIS experts are invited to use the system. We performed system evaluation in terms of technology acceptance and usability issues. The technology acceptance's mean score ranged from 3.70 to 4.40, while the usability means score ranged from 4.00 to 4.50. The results indicated that the system provided user-friendliness features so that they are willing to use the system in the near future. The medical experts also perceived that the system was easy to operate and navigate. They stated that the two subsystems are helpful for clinical operators to understand a common situation in emergency handling. We used their feedback to further improve and refine the program.

One of the most commonly searched topics on the internet in the United States is cancer. Our study aims to provide a general overview of the predictors of trust for two health information sources, doctors and the internet, when seeking cancer-related information. The data were obtained from the 2018 HINTS 5 Cycle 2 survey, which was administered from January through May to a total of 3,504 respondents. We carried out next a series of ordinal logistic regression models to identify predictors of high trust in doctors and the internet separately for cancer-seeking information. Demographic predictor variables varied as predictors of high trust for cancer knowledge across both sources. Respondents who reported less confidence in their ability to seek cancer information had significantly higher odds of high trust in both doctors (OR = 8.43, CI: 5.58-12.73) and the internet (OR = 2.93, CI: 1.97-4.35) as compared to those who reported being "completely confident" in their ability to obtain cancer information. Understanding the key predictors of trust in doctors and the internet is crucial to the enhancement of health. The role of confidence as a predictor of trust in seeking cancer information has been shown to consistently influence the levels of trust attributed to each topic.

The objective of this study was to assess the feasibility of using a user-centered chatbotfor collecting linked data to study overweight and obesity causes ina target population. In total 980 people participated in the feasibility study organized in three studies: (1) within a group of university students (88 participants), (2) in a small town (422 participants), and (3) within a university community (470 participants). We gathered self-reported data through the Wakamola chatbot regarding participants diet, physical activity, social network, living area, obesity-associated diseases, and sociodemographic data. For each study, we calculated the mean Body Mass Index (BMI) and number of people in each BMI level. Also, we defined and calculated scores (1-100 scale) regarding global health, BMI, alimentation, physical activity and social network. Moreover, we graphically represented obesity risk for living areas and the social network with nodes colored by BMI. Students group results: Mean BMI 21.37 (SD 2.57) (normal weight), 8 people underweight, 5 overweight, 0 obesity, global health status 78.21, alimentation 63.64, physical activity 65.08 and social 26.54, 3 areas with mean BMI level of obesity, 17 with overweight level. Small town´s study results: Mean BMI 25.66 (SD 4.29) (overweight), 2 people underweight, 63 overweight, 26 obesity, global health status 69.42, alimentation 64.60, physical activity 60.61 and social 1.14, 1 area with mean BMI in normal weight; University´s study results: Mean BMI 23.63 (SD 3.7) (normal weight), 22 people underweight, 86 overweight, 28 obesity, global health status 81.03, alimentation 81.84, physical activity 70.01 and social 1.47, 3 areas in obesity level, 19 in overweight level. Wakamola is a health care chatbot useful to collect relevant data from populations in the risk of overweight and obesity. Besides, the chatbot provides individual self-assessment of BMI and general status regarding the style of living. Moreover, Wakamola connects users in a social network to help the study of O&O´s causes from an individual, social and socio-economic perspective.

Societal challenges associated with caring for the physical and mental health of older adults worldwide have grown at an unprecedented pace, increasing demand for health-care services and technologies Despite the development of several assistive systems tailored to older adults, the rate of adoption of health technologies is low. This review discusses the ethical and acceptability challenges resulting in low adoption of health technologies specifically focused on smart homes for older adults. The findings have been structured in two categories: Ethical Considerations (Privacy, Social Support, and Autonomy) and Technology Aspects (User Context, Usability, and Training). The findings conclude that older adults community is more likely to adopt assistive systems when four key criteria are met. The technology should: be personalized toward their needs, protect their dignity and independence, provide user control, and not be isolating. Finally, we recommend researchers and developers working on assistive systems to: (1) provide interfaces via smart devices to control and configure the monitoring system with feedback for the user, (2) include various sensors/devices to architect a smart home solution in a way that is easy to integrate in daily life, and (3) define policies about data ownership.