Situation Background Assessment Recommendation (SBAR): Undergraduate Perspectives

Situation Background Assessment and Recommendation (SBAR): Undergraduate Perspectives C Morgan, L Adams, J Murray, R Dunlop, IK Walsh. Ian K Walsh, Centre for Medical Education, Queen’s University Belfast, Mulhouse Building, Royal Victoria Hospital, Grosvenor Road, Belfast BT12 6DP Background and Purpose: Structured communication tools are used to improve team communication quality.1,2 The Situation Background Assessment and Recommendation (SBAR) tool is widely adopted within patient safety.3 SBAR effectiveness is reportedly equivocal, suggesting use is not sustained beyond initial training.4-6 Understanding perspectives of those using SBAR may further improve clinical communication. We investigated senior medical undergraduate perspectives on SBAR, particularly when communicating with senior colleagues. Methodology: Mixed methods data collection was used. A previously piloted questionnaire with 12 five point Lickert scale questions and 3 open questions was given to all final year medical students. A subgroup also participated in 10 focus groups, deploying strictly structured audio-recorded questions. Selection was by convenience sampling, data gathered by open text questions and comments transcribed verbatim. In-vivo coding (iterative, towards data saturation) preceded thematic analysis. Results: 233 of 255 students (91%) completed the survey. 1. There were clearly contradictory viewpoints on SBAR usage. A recurrent theme was a desire for formal feedback and a relative lack of practice/experience with SBAR. 2. Students reported SBAR as having variable interpretation between individuals; limiting use as a shared mental model. 3. Brief training sessions are insufficient to embed the tool. 4. Most students reported SBAR helping effective communication, especially by providing structure in stressful situations. 5. Only 18.5% of students felt an alternative resource might be needed. Sub analysis of the themes highlighted: A. Lack of clarity regarding what information to include and information placement within the acronym, B. Senior colleague negative response to SBAR C. Lack of conciseness with the tool. Discussion and Conclusions: Despite a wide range of contradictory interpretation of SBAR utility, most students wish to retain the resource. More practice opportunities/feedback may enhance user confidence and understanding. References: (1) Leonard M, Graham S, Bonacum D. The human factor: the critical importance of effective teamwork and communication in providing safe care. Quality & Safety in Health Care 2004 Oct;13(Suppl 1):85-90. (2) d'Agincourt-Canning LG, Kissoon N, Singal M, Pitfield AF. Culture, communication and safety: lessons from the airline industry. Indian J Pediatr 2011 Jun;78(6):703-708. (3) Dunsford J. Structured communication: improving patient safety with SBAR. Nurs Womens Health 2009 Oct;13(5):384-390. (4) Compton J, Copeland K, Flanders S, Cassity C, Spetman M, Xiao Y, et al. Implementing SBAR across a large multihospital health system. Jt Comm J Qual Patient Saf 2012 Jun;38(6):261-268. (5) Ludikhuize J, de Jonge E, Goossens A. Measuring adherence among nurses one year after training in applying the Modified Early Warning Score and Situation-Background-Assessment-Recommendation instruments. Resuscitation 2011 Nov;82(11):1428-1433. (6) Cunningham NJ, Weiland TJ, van Dijk J, Paddle P, Shilkofski N, Cunningham NY. Telephone referrals by junior doctors: a randomised controlled trial assessing the impact of SBAR in a simulated setting. Postgrad Med J 2012 Nov;88(1045):619-626.

A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NO_x, SO₂ etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.