Connecting Classrooms, Clinicians, and Community Clinics Through Technology (C4Tech) for Active and Collaborative Learning
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Connecting Classrooms, Clinicians, and Community Clinics Through Technology (C4Tech) for Active and Collaborative Learning
Objective: This pilot study examined levels of cognition physician assistant (PA) students achieved through the application of innovative blended learning models that connect classroom, physicians and community clinics through (e-learning) electronic-learning technology (C4Tech) used in the course of the PA. Educational interventions aimed to facilitate collaboration between students PA authentic learning and practicing physicians that will result in high levels of cognition associated with the manifestations of the social determinants of health and health inequalities.
Methods: A case study approach was adopted to assess the level of cognition and changes in those levels resulting from the application of innovative blended learning models. content analysis using Bloom’s taxonomy of cognitive domain facilitated the determination of the level of cognition and changes in those levels. Samples of the 8 groups comprising 70 students of PA and 8 clinical instructor of community clinics with patient populations underrepresented.
Results: Analysis of 2 course the task revealed that the application of the model C4Tech produce high levels of cognition. At the end of this course, all 8 groups achieved at least “evaluate” the level of cognition and half of the group reached the highest levels of cognition, which “creates” level. A wide variation in the level of cognition was shown between the first and second tasks in each group and between groups.
Conclusions: Our findings suggest that e-learning technologies can be effective in blending classroom and work environments for authentic and collaborative learning. The application of the model C4Tech generate high levels of cognition are related to the course content.
Connecting Classrooms, Clinicians, and Community Clinics Through Technology (C4Tech) for Active and Collaborative Learning
Real-World Integration of Learning Technologies Sepsis Deep Into Routine Clinical Care: Implementation Study
Background: The successful integration of machine learning into routine clinical care are very rare, and barriers to adoption are poorly marked in the literature.
Objective: The aim of this study to report on quality improvement efforts to integrate learning in sepsis detection and management platform, Sepsis Watch, in routine clinical care.
Methods: In 2016, a multidisciplinary team consisting of a statistician, a data scientist, engineer data, and doctors assembled by the leadership of academic health system to radically improve the detection and treatment of sepsis. This report follows on improving the quality of learning health system framework to describe the problem assessment, design, development, implementation, and evaluation plan Sepsis Watch.
Results: Sepsis Watch successfully integrated into routine clinical care and reshaped how machine learning projects locally run. front-line clinical staff are heavily involved in the design and development workflow, machine learning models, and applications. new machine learning method developed to detect early sepsis, and application of models required a robust infrastructure. significant investment required for stakeholders to align, develop trusting relationships, defining roles and responsibilities, and to train front-line staff, which leads to the formation of three partnerships with internal and external research groups to evaluate Sepsis Watch.
Description: CRK, also known as p38, is a protein that in humans is encoded by the CRK gene. This gene is a member of an adapter protein family that binds to several tyrosine-phosphorylated proteins. It is mapped to 17p13.3. The protein participates in the Reelin signaling cascade downstream of DAB1. The product of this gene has several SH2 and SH3 domains (src-homology domains) and is involved in several signaling pathways, recruiting cytoplasmic proteins in the vicinity of tyrosine kinase through SH2-phosphotyrosine interaction. The N-terminal SH2 domain of Crk functions as a positive regulator of transformation whereas the C-terminal SH3 domain functions as a negative regulator of transformation. Two alternative transcripts encoding different isoforms with distinct biological activity have been described.
Description: Purified recombinant Human P38 delta protein
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Conclusion: Machine learning common model was developed to improve clinical decision-making, but the successful integration of machine learning into routine clinical care is rare. Although there are no guidelines for integrating learning deep into clinical care, Sepsis Watch the lessons of integration can inform efforts to develop the technology of machine learning in other health care systems.
