RESUMO
An efficient chemical comparator, a computer application facilitating searching and comparing chemical libraries, is useful in drug discovery and other relevant areas. The need for an efficient and user-friendly chemical comparator prompted us to develop ChemCom (Chemical Comparator) based on Java Web Start (JavaWS) technology. ChemCom provides a user-friendly graphical interface to a number of fast algorithms including a novel algorithm termed UnionBit Tree Algorithm. It utilizes an intuitive stepwise mechanism for selecting chemical comparison parameters before starting the comparison process. UnionBit has shown approximately an 165% speedup on average compared to its closest competitive algorithm implemented in ChemCom over real data. It is approximately 11 times faster than the Open Babel FastSearch algorithm in our tests. ChemCom can be accessed free-of-charge via a user-friendly website at http://bioinformatics.org/chemcom/.
Assuntos
Mineração de Dados/métodos , Bibliotecas de Moléculas Pequenas , Software , Algoritmos , Gráficos por Computador , Descoberta de Drogas , Fatores de Tempo , Interface Usuário-ComputadorRESUMO
Landmark publications, such as To Err is Human, confronted the healthcare community with the egregious toll medical errors played in both patient safety and overall healthcare costs. This heralded a paradigm shift and a call for action by professional organizations to enact methods to ensure physician competency and quality assurance. The American College of Radiology similarly convened a task force to discuss these concerns and how best to address quality assurance in radiology practice, leading to the development of RADPEER, a score-based peer review system. However, critics were quick to point out the deficiencies of this model, highlighting it as punitive and a poor evaluator of physician performance. The recognized deficiencies in score-based peer review prompted the pursuit of an alternate model that would instead emphasize learning and improvement. Peer learning was proposed and highlighted the necessity of an inclusive and collaborative environment where colleagues could discuss case errors as learning opportunities without fear of punitive consequence. This paper explores peer learning, its benefits and challenges, as well as how to identify specific learning opportunities by utilizing case examples.
Assuntos
Garantia da Qualidade dos Cuidados de Saúde , Radiologia , Humanos , Revisão por Pares , Radiologia/educação , Erros Médicos , Competência ClínicaRESUMO
A significant number of patients are reported to not receive timely completion of their recommended follow-up intervention following the interpretation of their imaging studies, contributing to patient deaths resulting from inaccurate or delayed diagnosis. Though automated critical test notification systems and computerized communication mechanisms currently exist, many institutions are discovering that there continue to be gaps in the completion of follow-up recommendations. Herein, we describe how we developed and implemented a closed-loop program dedicated to identifying such gaps and ensuring patients were aware of and received appropriate follow-up.