Applying Learning Object Principles To Simulation: The Anesthesia Machine Pre-Use Check

Abstract

Introduction. Recent preliminary survey results indicate up to 80% non-compliance worldwide with the general recommendations to check an anesthesia machine before every case [1]. Lack of knowledge and inadequate instruction were among the most cited causes for non-compliance [1] suggesting a worldwide need for education and training, given the reported impact of the pre-use check and its documentation on patient safety [2]. Objectives: With Anesthesia Patient Safety Foundation funding, we set out to develop a free, transparent reality, web-disseminated simulation of the current US Food & Drug Administration (FDA) checklist (ca. 1993) that is reusable for (a) other national/regional checklists, (b) forthcoming new FDA recommendations, (c) different audiences (anesthesiologists, CRNAs, techs, vets) and (d) different machine designs, thus facilitating collaborative, efficient and fast deployment of e-learning. Methods. To facilitate reuse, we applied learning object (LO) principles to simulation to create reusable simulation learning objects (SLO). A learning object (a) is reusable, (b) contains content, practice and assessment components and (c) is meta-tagged so that it can be intelligently identified by search algorithms to promote its reuse. A LO can consist of any kind of instruction format such as text, graphics, audio, video, multiple choice questions and simulations. In our definition of SLOs, implemented with Director (Macromedia, San Francisco, CA), we used only simulations and further subdivided the content, practice and assessment components into stand-alone simulations, individually invoked via unique URLs. The initial state of each simulated step in the FDA checklist is defined via a corresponding XML file. In the content SLOs (“see one”), users are taught how to perform a given test while simultaneously learning how to use the simulation; a “Rationale” button explains why each test is performed. An intelligent tutor provides tiered levels of assistance during practice SLOs (“do one”). In the assessment SLOs (“test oneself”), learners have to perform a procedure correctly and in the right sequence and then judge whether a randomly configured machine passes or fails a given test. Results: The simulation of the US checklist at http://vam.anest.ufl.edu/learningobjects consists of 44 content, 45 practice, and 19 assessment SLOs. Casting the individual steps of the FDA checklist as SLOs and the text instructions as reconfigurable XML files more than doubled the time to implement the simulation. However, as a result, we were able to produce in an hour a simulation-enhanced version of the Australian and New Zealand College of Anaesthetists’ checklist (at the above URL) via reuse of the SLOs created for the US checklist. Similarly, a Chinese version of the simulation is already available. Discussion. Preliminary results are that our SLO approach has facilitated reusability and will provide finer granularity and control in reusability, sequencing and assessment. Our initial experience with display-based SLOs suggests that it may be worthwhile to investigate applying LO principles to physical simulator scenarios to facilitate sharing and consistency in providing content, practice and assessment via full-body simulation. Conflict of Interest: Authors indicated they have nothing to disclose.