Background: There are few published studies of the use of portable or handheld computers in health care, but these devices have the potential to transform multiple aspects of clinical teaching and practice.
Objective: This article assesses resident physicians' perceptions and experiences with tablet computers before and after the introduction of these devices.
Methods: We surveyed 49 resident physicians from 8 neurology, surgery, and internal medicine clinical services before and after the introduction of tablet computers at a 415-bed Boston teaching hospital. The surveys queried respondents about their assessment of tablet computers, including the perceived impact of tablets on clinical tasks, job satisfaction, time spent at work, and quality of patient care.
Results: Respondents reported that it was easier (73%) and faster (70%) to use a tablet computer than to search for an available desktop. Tablets were useful for reviewing data, writing notes, and entering orders. Respondents indicated that tablet computers increased their job satisfaction (84%), reduced the amount of time spent in the hospital (51%), and improved the quality of care (65%).
Conclusion: The introduction of tablet computers enhanced resident physicians' perceptions of efficiency, effectiveness, and job satisfaction. Investments in this technology are warranted.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6082660 | PMC |
http://dx.doi.org/10.1055/s-0038-1667121 | DOI Listing |
Sensors (Basel)
January 2025
Department of Computer Engineering, Dongseo University, Busan 47011, Republic of Korea.
Choosing nutritious foods is essential for daily health, but finding recipes that match available ingredients and dietary preferences can be challenging. Traditional recommendation methods often lack personalization and accurate ingredient recognition. Personalized systems address this by integrating user preferences, dietary needs, and ingredient availability.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Artifcial Intelligence, Chung-Ang University, Heukseok-dong, Dongjak-gu, Seoul 06974, Republic of Korea.
Sensor-based gesture recognition on mobile devices is critical to human-computer interaction, enabling intuitive user input for various applications. However, current approaches often rely on server-based retraining whenever new gestures are introduced, incurring substantial energy consumption and latency due to frequent data transmission. To address these limitations, we present the first on-device continual learning framework for gesture recognition.
View Article and Find Full Text PDFSci Rep
January 2025
Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran.
Corneal ulcer is one of the most important ophthalmic emergencies. A portable, recordable, and smartphone-attachable slit-lamp device called the "Smart Eye Camera" (SEC) is introduced to compare evaluating corneal ulcers between the SEC and the conventional slit-lamp. A total of 110 participants were included in the study, consisting of 55 patients with corneal ulcers and 55 age- and gender-matched healthy volunteers as controls.
View Article and Find Full Text PDFAppl Psychol Health Well Being
February 2025
Department of Education and Psychology, Division of Health Psychology, Freie Universität Berlin, Berlin, Germany.
Background: Interventions targeting social media use show mixed results in improving well-being outcomes, particularly for persons with problematic forms of smartphone use. This study assesses the effectiveness of an intervention app in enhancing well-being outcomes and the moderating role of persons' perceptions about problematic smartphone use (PSU).
Methods: In a randomized controlled trial, N = 70 participants, allocated to the intervention (n = 35) or control condition (n = 35), completed weekly online surveys at baseline, post-intervention, and follow-up.
Biosensors (Basel)
January 2025
Department of Computer and Information Sciences, University of Houston-Victoria, Victoria, TX 77904, USA.
Lateral flow assay has been extensively used for at-home testing and point-of-care diagnostics in rural areas. Despite its advantages as convenient and low-cost testing, it suffers from poor quantification capacity where only yes/no or positive/negative diagnostics are achieved. In this study, machine learning and deep learning models were developed to quantify the analyte load from smartphone-captured images of the lateral flow assay test.
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