Informatics for Health & Social Care最新文献

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.

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.