Lynch Carrillo (woodbutane9)

ing room. With added open-ended questions, the SAQ-NL appears to be an assessment tool that allows for developing strategies that are instrumental in improving quality of care. ©Alexander D Hilt, Ad A Kaptein, Martin J Schalij, Jan van Schaik. Originally published in JMIR Human Factors (http//humanfactors.jmir.org), 08.04.2020.BACKGROUND Bone marrow aspiration and biopsy remain the gold standard for the diagnosis of hematological diseases despite the development of flow cytometry (FCM) and molecular and gene analyses. However, the interpretation of the results is laborious and operator dependent. Furthermore, the obtained results exhibit inter- and intravariations among specialists. Therefore, it is important to develop a more objective and automated analysis system. Several deep learning models have been developed and applied in medical image analysis but not in the field of hematological histology, especially for bone marrow smear applications. OBJECTIVE The aim of this study was to develop a deep learning model (BMSNet) for assisting hematologists in the interpretation of bone marrow smears for faster diagnosis and disease monitoring. METHODS From January 1, 2016, to December 31, 2018, 122 bone marrow smears were photographed and divided into a development cohort (N=42), a validation cohort (N=70), and a competition cohort (N=10ally published in JMIR Medical Informatics (http//medinform.jmir.org), 08.04.2020.BACKGROUND In long-term residential care (LTRC), caregivers' attempts to provide person-centered care can be challenging when assisting residents living with a communication disorder (eg, aphasia) and/or a language-cultural barrier. Mobile communication technology, which includes smartphones and tablets and their software apps, offers an innovative solution for preventing and overcoming communication breakdowns during activities of daily living. There is a need to better understand the availability, relevance, and stability of commercially available communication apps (cApps) that could support person-centered care in the LTRC setting. OBJECTIVE This study aimed to (1) systematically identify and evaluate commercially available cApps that could support person-centered communication (PCC) in LTRC and (2) examine the stability of cApps over 2 years. METHODS We conducted systematic searches of the Canadian App Store (iPhone Operating System platform) in 2015 and 2017 using predefined search terms. cApps that metcial participation and person-centered care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.2196/10.2196/17136. ©Rozanne Wilson, Diana Cochrane, Alex Mihailidis, Jeff Small. Originally published in JMIR Aging (http//aging.jmir.org), 08.04.2020.BACKGROUND Interprofessional team training is needed to improve nurse-physician communication skills that are lacking in clinical practice. Using simulations has proven to be an effective learning approach for team training. Yet, it has logistical constraints that call for the exploration of virtual environments in delivering team training. OBJECTIVE This study aimed to evaluate a team training program using virtual reality vs conventional live simulations on medical and nursing students' communication skill performances and teamwork attitudes. METHODS In June 2018, the authors implemented nurse-physician communication team training using communication tools. A randomized controlled trial study was conducted with 120 undergraduate medical and nursing students who were randomly assigned to undertake team training using virtual reality or live simulations. The participants from both groups were tested on their communication performances through team-based simulation assessments. Selleckchem Ulixertinib Their teamwork attitudes were evion. TRIAL REGISTRATION ClinicalTrials.gov NCT04330924; https//clinicaltrials.gov/ct2/show/NCT04330924. ©Sok Ying Liaw, Sim Win Ooi, Khairul Dzakirin Bin Rusli, Tang Ching Lau, Wilson Wai San Tam, Wei Ling Chua. Originally published i