This study aims to evaluate the effectiveness of a computer-based teaching program in supporting and enhancing traditional teaching methods. The program covers the pharmacology of inflammation and has been evaluated with a group of second-year medical students at a UK university. The study assessed subject-specific knowledge using a pre-and post-test and surveyed, by questionnaire, students' perceptions of the usefulness of the program to support learning before and after use. The use of computers for learning amongst this cohort of students was widespread. The results demonstrated an increase in students ' knowledge of the pharmacology of inflammation, coupled with a positive attitude towards the CBL program they had used and the advantages that this mode of study may provide in enabling students to manage their own learning. However, students did not feel that the program could substitute for traditional teaching (lectures).
BackgroundThe field of medicine is rapidly becoming digitised, and in the process passively amassing large volumes of healthcare data. Machine learning and data analytics are advancing rapidly, but these have been slow to be taken up in the day-to-day delivery of healthcare. We present an application of machine learning to optimise a laboratory testing programme as an example of benefiting from these tools.MethodsCanterbury District Health Board has recently implemented a system for urgent lab sample processing in the community, reducing unnecessary emergency presentations to hospital. Samples are transported from primary care facilities to a central laboratory. To improve the efficiency of this service, our team built a prototype transport scheduling platform using machine learning techniques and simulated the efficiency and cost impact of the platform using historical data.ResultsOur simulation demonstrated procedural efficiency and potential for annual savings between 5% and 14% from implementing a real-time lab sample transport scheduling platform. Advantages included providing a forward job list to the laboratory, an expected time to result and a streamlined transport request process.ConclusionThere are a range of opportunities in healthcare to use large datasets for improved delivery of care. We have described an applied example of using machine learning techniques to improve the efficiency of community patient lab sample processing at scale. This is with a view to demonstrating practical avenues for collaboration between clinicians and machine learning engineers.
An interactive computer program written for IBM-compatible microcomputers, which simulates the physiological response to graded exercise in healthy individuals, is described. The program presents high-resolution graphic data (heart rate, pulmonary ventilation, oxygen consumption, and blood lactate concentration) in a form comparable to that of a chart recorder display. Data are derived from an empirical model that allows users to select certain parameters of the subject they wish to investigate, including sex, age, height, weight, and level of training. Measurements may be taken directly from the monitor screen by use of the cross-hair cursor facility provided. The program has been positively evaluated in use by undergraduate students and shown to be an effective teaching aid. The potential use of the software in light of these findings is discussed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.