Mobile devices are increasingly becoming integral communication and clinical tools. Monitoring the prevalence and utilization characteristics of surgeons and trainees is critical to understanding how these new technologies can be best used in practice. The authors conducted a prospective Internet-based survey over 7 time points from August 2010 to August 2014 at all nationwide American Council for Graduate Medical Education-accredited orthopedic programs. The survey questionnaire was designed to evaluate the use of devices and mobile applications (apps) among trainees and physicians in the clinical setting. Results were analyzed and summarized for orthopedic surgeons and trainees. During the 48-month period, there were 7 time points with 467, 622, 329, 223, 237, 111, and 134 responses. Mobile device use in the clinical setting increased across all fields and levels of training during the study period. Orthopedic trainees increased their use of Smartphone apps in the clinical setting from 60% to 84%, whereas attending use increased from 41% to 61%. During this time frame, use of Apple/Android platforms increased from 45%/13% to 85%/15%, respectively. At all time points, 70% of orthopedic surgeons believed their institution/hospital should support mobile device use. As measured over a 48-month period, mobile devices have become an ubiquitous tool in the clinical setting among orthopedic surgeons and trainees. The authors expect these trends to continue and encourage providers and trainees to be aware of the limitations and risks inherent with new technology.
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http://dx.doi.org/10.3928/01477447-20151228-01 | DOI Listing |
NPJ Prim Care Respir Med
December 2024
ResMed Science Center, San Diego, CA, USA.
Digital health platforms for asthma self-management have demonstrated promise in improving clinical and quality of life outcomes. However, few studies have examined such an approach in a real-world, fully remote setting. As such, we evaluated the benefit of an evidence-based digital self-management platform for asthma-both on its own and when integrated into an established virtual clinical service.
View Article and Find Full Text PDFTrials
December 2024
Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
Background: Vancomycin, an antibiotic with activity against methicillin-resistant Staphylococcus aureus (MRSA), is frequently included in empiric treatment for community-acquired pneumonia (CAP) despite the fact that MRSA is rarely implicated in CAP. Conducting polymerase chain reaction (PCR) testing on nasal swabs to identify the presence of MRSA colonization has been proposed as an antimicrobial stewardship intervention to reduce the use of vancomycin. Observational studies have shown reductions in vancomycin use after implementation of MRSA colonization testing, and this approach has been adopted by CAP guidelines.
View Article and Find Full Text PDFBMC Infect Dis
December 2024
Lab Services and Infection Control; Chief, Education and Research, Artemis Hospitals, Sector-51, Gurugram, Haryana, India.
Klebsiella pneumoniae, a pathogen of concern worldwide can be classified as classical K. pneumoniae (cKp) and Hypervirulent K. pneumoniae (HvKp).
View Article and Find Full Text PDFBMC Pulm Med
December 2024
School of Nursing, Jinan University, Guangzhou, China.
Objectives: Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide, particularly among the elderly, resulting in high rates of intensive care unit (ICU) admissions. Malnutrition is common in elderly patients and has been associated with poor prognosis in patients with COPD. However, its impact in the ICU setting remains incompletely defined.
View Article and Find Full Text PDFBMC Public Health
December 2024
Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada.
Background: Machine learning (ML) is increasingly used in population and public health to support epidemiological studies, surveillance, and evaluation. Our objective was to conduct a scoping review to identify studies that use ML in population health, with a focus on its use in non-communicable diseases (NCDs). We also examine potential algorithmic biases in model design, training, and implementation, as well as efforts to mitigate these biases.
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