Safer Surgery part 40 pdf

Safer Surgery 364 ratings were not signicantly different from trainee surgeons’ self-ratings (t(37) = 0.88, p > 0.05), thus indicating agreement in the assessment of skill. In the second series of simulations, decision-making was rated signicantly lower than all other skills (all ps < 0.05). Moreover, there was a signicant pre-post training improvement in the ratings of decision-making for the surgical trainees (M Pre-training = 2.51 vs. M Post-training = 3.62; F(1, 15) = 6.59, p = 0.05). Taken together, these studies suggest that simulation-based team training is feasible and has great potential as a training tool. From the decision-making perspective, simulations offer a unique environment, in which the skill can be observed and assessed ‘in action’ – in other words, in a dynamic, uid, potentially stressful, but safe training environment. (v) Concluding Remarks Surgical decision-making is an important but under-researched eld. The aim of the present chapter was to discuss the characteristics of surgical decision-making, highlight how some behavioural and naturalistic decision research approaches can be applied to surgery and, nally, present empirical work that has been carried out by our research team to date to assess decision-making processes and decision- making as a skill in surgery. We presented empirical applications of three different approaches to surgical decision-making – namely, knowledge elicitation from experts, experimentation and modelling using Judgement Analysis (JA), and, nally, simulation-based assessment of and training in decision-making (among other non-technical skills). All approaches yielded promising ndings. Knowledge elicitation resulted in the representation of the care pathway for patients presenting with symptomatic gallstone disease – both acute and non-acute. The approach was shown to be reliable. Its contribution is that it can be used to articulate key clinical decisions that need to be made in the process of care of such patients and, importantly, what are the cues (information or considerations) that feed into these decisions. Experimentation and modelling using JA is a different approach, especially useful where cognitive processes that are not easy to articulate consciously are involved. In the presence of gold standard models of the type that we used in our study, JA can be used to assess surgeons’ risk estimation and to provide individualized Mean NOTECHS subscale rating (standard deviation) Communication Vigilance Teamworking Leadership Decision- making Trainer 4.00 (0.97) 4.11 (1.17) 3.96 (1.15) 3.78 (1.01) 3.95 (1.10) Trainee 3.69 (1.03) 3.67 (1.15) 3.76 (0.91) 3.73 (0.90) 3.74 (0.94) Table 21.1 Non-technical skills in the rst simulation series Surgical Decision-Making 365 feedback regarding (in)appropriate cue utilization. Although accurate judgement of the likelihood of conversion of laparoscopic cholecystectomy to the open procedure is not a critical life-or-death issue, it is important in terms of informed consent. It may also be thought of as an ‘exemplar judgement’ – thus suggesting that ndings from this study may be generalizable across other similar surgical risk judgements. Importantly, the technique does not rely on self-report. Finally, simulation-based assessment allows the simultaneous assessment of decision-making and other skills, technical and non-technical. Simulation-based training that involves an entire operating theatre team recreates the working reality of the surgeon and can be used as a training environment for the whole surgical team. Existing observational tools, after appropriate revision, were shown to be reliable in the surgical context. Given the wide-spread use and face validity of surgical simulators, this approach can be used extensively both as a training tool, but also as a research environment. Currently, we are building on our initial experiences with each one of the three approaches presented here to expand the eld of their application. We have used interviews to assess in detail surgeons’ perceptions of the stressors that they face in the operating theatre (Arora et al. 2009, Wetzel et al. 2006). We found that technical issues (e.g., difcult anatomy; bleeding), malfunctioning/lack of availability of equipment, distractions/interruptions and poor teamwork/communication are the key stressors that surgeons have to cope with. We also found that surgeons recognize the impact of such stressors on their performance – including their intra-operative decision-making. Importantly, the interviews allowed us to capture a range of training needs that the surgeons articulated in relation to a stress management training module (Arora et al. 2009). We are now in the process of developing such a module, which will involve simulation-based team training. One of our key aims is to assess the impact of stress management training on intra-operative decision-making via observation (thus following up previous simulation-based assessment of decision-making and other non-technical skills). We are also piloting the use of JA as a training tool. We recently used JA-derived feedback to improve accuracy and reliability of surgical risk estimation (Jacklin et al. 2009). Pre-feedback, we assessed accuracy and reliability of trainee surgeons’ and medical students’ estimates of operative mortality for major surgery using a number of patient vignettes with varying risk factors. Vignette construction was guided by a published risk model as a gold standard. Post-feedback, participants were retested on a second, equivalent case set. We found that feedback improved reliability of risk estimates in both groups and also accuracy in students’ risk estimates: these estimates were signicantly worse than those of the surgical group pre-feedback, but matched them in accuracy post-feedback. Accuracy of the surgeons’ estimates did not improve (arguably, because of a ceiling effect). This is a novel application of JA, suggesting that the technique could be potentially useful for surgical training and assessment – at least in the eld of risk estimation. All approaches that we have used so far have limitations, both conceptual, but also practical. Over-reliance on self-report is an obvious limitation of knowledge Safer Surgery 366 elicitation/any interview-based technique. The usefulness of JA-type modelling will always be a function of the availability of robust epidemiological models to be used as gold standard comparisons, whilst the choice of modelling approach to be applied to the surgeons’ judgements can be debated. Simulation is a rather expensive tool: it requires facilities and trained trainers. In addition, robust transfer of learning from simulated to real operating theatres remains to be empirically demonstrated. These limitations, together with the variety of environments in which surgeons are required to make decisions and the range of potential applications of this line of research render a multimodal approach to surgical decision-making of paramount importance. First of all, surgical decision-making occurs in the operating theatre under time pressure and stress, but also in surgical wards and outpatient clinics, in which the surgeon has more opportunity to consider options and discuss them with the patient. Some of the decisions are grounded on highly technical knowledge and the required skill to execute them; others involve more reliance on patient’s preferences; all of them require systematic use of the available evidence base. It is rather hard, if not impossible, to assume that all the approaches that we have described in this chapter are equally applicable to all decision situations. Secondly, measuring surgeons’ decision-making implies that it is feasible and conceptually sound to treat decision-making as an observable skill, in which surgeons can be trained. In turn, training involves demonstration of tangible improvement and also assessment (formative, summative, or both). Although some initial attempts can be found in the relevant literature (e.g., Sarker et al. 2009), these are still few and rather heavily knowledge-based – which suggests that their generalizability across decision-making situations remains to be demonstrated. Systematic, replicable empirical work across a variety of decision- making situations needs to be done in order to arrive at robust, valid tools to assess decision-making skill comprehensively. Such tools are not unlikely to consist of a range of different modules – thus representing the richness and variety of decisions that a modern surgeon is faced with. The multimodal approach that we propose here appears to be well suited to capture the various elements of real-life surgical decisions, thereby rendering surgical decision-making less of a ‘black box’ among other surgical skills. Acknowledgements The work that is reported in this chapter has been funded by a research fellowship of the Royal College of Surgeons of England (RJ), the Rosetrees Foundation (RJ), the Grand Lodge 250th Anniversary Fund (RJ), a research fellowship of the Economic and Social Research Council Centre for Economic Learning and Social Evolution (NS) and the British Academy (NS). Surgical Decision-Making 367 Authors’ Note Papers outlining methods of assessing surgical decision-making as described in this chapter were rst presented at the 21st Society for Probability, Utility and Decision-Making (SPUDM) biennial research conference (Warsaw, 19–23 August, 2007) and at the 2nd Healthcare Systems, Ergonomics, and Patient Safety (HEPS) international conference (Strasbourg, 25–27 June, 2008). We would like to thank all our clinical and psychologist colleagues for their very useful feedback. References Aggarwal, R., Moorthy, K. and Darzi, A. (2004) Laparoscopic skills training and assessment. British Journal of Surgery 91, 1549–58. Arora, S., Sevdalis, N., Nestel, D., Tierney, T., Woloshynowych, M. and Kneebone, R. (2009) Managing intra-operative stress: What do surgeons want from a crisis training programme? American Journal of Surgery 197, 537–43. Calland J., Guerlain S., Adams R., et al. (2002) A systems approach to surgical safety. Surgical Endoscopy 16, 1005–14. Canter, R. and Kelly, A. (2007) A new curriculum for surgical training within the United Kingdom: The rst stages of implementation. Journal of Surgical Education 64, 20–6. Chapman, G.B. and Sonnenberg, F.A. (2003) Decision Making in Healthcare. Cambridge: Cambridge University Press. Clarke, M.G., Wilson, J.R.M., Kennedy, K.P. and MacDonald, R.P. (2007) Clinical judgment analysis of the parameters used by consultant urologists in the management of prostate cancer. Journal of Urology 178, 98–102. Coiera, E. and Tombs, V. (1998) Communication behaviours in a hospital setting: An observational study. British Medical Journal 316, 673–6. Cooksey, R.W. (1996a) Judgment Analysis: Theory, Methods, and Applications. San Diego, CA: Academic Press. Cooksey, R.W. (1996b) The methodology of social judgement theory. Thinking and Reasoning 2, 141–73. Dankelman, J. and Di Lorenzo, N. (2005) Surgical training and simulation. Minimally Invasive Therapy and Allied Technologies 14, 211–13. Denig, P. and Haaijer-Ruskamp, F.M. (1994) ‘Thinking aloud’ as a method of analyzing the treatment decision of physicians. European Journal of Public Health 4, 55–9. Ericsson, K.A. and Simon, H.A. (1984) Protocol Analysis: Verbal Reports as Data. Cambridge, MA: MIT Press. Flin, R., Martin, L., Goeters, K.M., Hörmann, H.J., Amalberti, R., Valot, C., Nijhuis, H. (2003). Development of the NOTECHS (non-technical skills) system for assessing pilots’ CRM skills. Human Factors and Aerospace Safety 3, 97–119. Safer Surgery 368 Flin, R., Youngson, G. and Yule S. (2007) How do surgeons make intraoperative decisions? Quality and Safety in Health Care 16, 235–9. Frank, J.R. (2005) The CanMEDS 2005 Physician Competency Framework. Better Standards. Better Physicians. Better Care. Ottawa: Royal College of Physicians and Surgeons of Canada. Frank, J.R, Jabbour, M. and Tugwell P., et al. (1996) Skills for the new millennium: Report of the societal needs working group, CanMEDS 2000 Project. Annals Royal College of Physicians and Surgeons of Canada 29, 206–16. Frank, J.R. and Langer, B. (2003) Collaboration, communication, management, and advocacy: Teaching surgeons new skills through the CanMEDS project. World Journal of Surgery 27, 972–8. Fried, G.M. and Feldman, L.S. (2008) Objective assessment of technical performance. World Journal of Surgery 32, 156–60. Harries, C. and Kostopoulou, O. (2005) Psychological approaches to measuring and modelling clinical decision-making. In A. Bowling and S. Ebrahim (eds) Handbook of Health Research Methods: Investigation, Measurement and Analysis (pp. 331–61). Maidenhead: Open University Press. Healey, A.N. and Vincent, C.A. (2007) The systems of surgery. Theoretical Issues in Ergonomic Science 8, 429–43. Healey, A.N., Sevdalis, N. and Vincent, C.A. (2006a) Measuring intra-operative interference from distraction and interruption observed in the operating theatre. Ergonomics 49, 589–604. Healey, A.N., Undre, S. and Vincent, C.A. (2006b) Dening the technical skills of teamwork in surgery. Quality and Safety in Health Care 15, 231–4. Helmreich, R.L., Merritt, A.C. and Wilhelm, J.A. (1999) The evolution of Crew Resource Management training in commercial aviation. International Journal of Aviation Psychology, 9, 19-32. Hertzer, N.R. 2006. Current status of endovascular repair of infrarenal abdominal aortic aneurysms in the context of 50 years of conventional repair. Ann N Y Acad Sci 1085, 175–86. Jacklin, R., Sevdalis, N., Darzi, A. and Vincent, C.A. (2008a) Mapping surgical practice decision making: An interview study to evaluate decisions in surgical care. American Journal of Surgery 195, 689–96. Jacklin, R., Sevdalis, N., Harries, C., Darzi, A. and Vincent, C.A. (2008b) Judgment analysis: A method for quantitative evaluation of trainee surgeons’ judgments of surgical risk. American Journal of Surgery 195, 193–8. Jacklin, R., Sevdalis, N., Darzi, A. and Vincent, C.A. (2009) Efcacy of cognitive feedback in improving operative risk estimation. American Journal of Surgery 197, 76–81. Kee, F., McDonald, P., Kirwan, .J.R. et al. (1998) Urgency and priority for cardiac surgery: A clinical judgment analysis. British Medical Journal 316, 925–9. Klampfer, R., Flin, R., Helmreich, R.L., et al. (2001) Enhancing Performance in High Risk Environments: Recommendations for the Use of Behavioural Surgical Decision-Making 369 Markers. Report from the Behavioural Markers Workshop, Zurich, June. Berlin: Damler Benz Foundation. Klein, G. (1998) Sources of Power: How People Make Decisions. London: MIT Press. Klein, G., Orasanu, J., Calderwood, R. and Zsambok, C.E. (1993) Decision Making in Action: Models and Methods. Norwood, NJ: Ablex Publishing Corporation. Koehler, D.J. and Harvey, N. (2004) Blackwell Handbook of Judgment and Decision Making. Oxford: Blackwell. Koele, P. and Hoogstraten, J. (1999) Determinants of dentists’ decisions to initiate dental implant treatment: A judgment analysis. Journal of Prosthetic Dentistry 81, 476–80. Koutantji, M., McCulloch, P. and Undre, S., et al. (2008) Is team training in briengs for surgical teams feasible in simulation? Cognition, Technology and Work 10, 275–85 Leong, J.J., Nicolaou, M., Emery, R.J., Darzi, A. and Yang, G.Z. (2007) Visual search behaviour in skeletal radiographs: A cross-specialty study. Clinical Radiology 62, 1069–77. Lipshitz, R., Klein, G., Orasanu, J. and Salas, E. (2001) Taking stock of naturalistic decision making. Journal of Behavioral Decision Making 14, 331–52. MacCormick, A.D. and Parry, B.R. (2006) Judgment analysis of surgeons’ prioritization of patients for elective general surgery. Medical Decision Making 26, 255–64. Moorthy, K., Munz, Y. and Adams S. et al. (2005) A human factors analysis of technical and team skills among surgical trainees during procedural simulations in a Simulated Operating Theatre (SOT). Annals of Surgery 242, 631–9. Moorthy, K., Munz, Y., Forrest, D., Pandey, V., Undre, S., Vincent, C. and Darzi, A. (2006) Surgical crisis management skills training and assessment. Annals of Surgery 244, 139–47. Nisbett, R.E. and Wilson, T.D. (1997) Telling more than we can know: Verbal reports on mental processes. Psychological Review 84, 231–57. Pster, M., Jakob, S., Frey, F.J., Niederer, U., Schmidt, M. and Marti, H.P. (1999) Judgment analysis in clinical nephrology. American Journal of Kidney Diseases 34, 569–75. Sarker, S., Chang, A., Albrani, T. and Vincent, C.A. (2008) Constructing hierarchical task analysis in surgery. Surgical Endoscopy 22, 107–11. Sarker, S., Rehman, S., Ladwa, M., Chang, A. and Vincent, C.A. (2009) A decision- making learning and assessment tool in laparoscopic cholecystectomy. Surgical Endoscopy 23(1), 197–203. Sevdalis, N. and McCulloch, P. (2006) Teaching evidence-based decision-making. Surgical Clinics of North America 86, 59–70. Sevdalis, N. and Jacklin, R. (2008) Opening the ‘black box’ of surgeons’ risk estimation: From intuition to quantitative modeling. World Journal of Surgery 32, 324–5. Safer Surgery 370 Sevdalis, N., Davis, R,. Koutantji, M., Undre, S., Darzi, A. and Vincent, C.A. (2008) Reliability of a revised NOTECHS scale for use in surgical teams. American Journal of Surgery 196, 184–90. Undre, S., Koutantji, M., Sevdalis, N., et al. (2007) Multi-disciplinary crisis simulations: The way forward for training surgical teams. World Journal of Surgery 31, 1843–53. van Avermaete J.A.G. and Kruijsen E. (1998) The Evaluation of Non-technical Skills of Multi-pilot Aircrew in Relation to the JAR-FCL Requirements (project rep. CR-98443). Amsterdam: NLR, Amsterdam. Vincent, C., Moorthy, K., Sarker, S.K., Chang, A. and Darzi, A. (2004) Systems approaches to surgical quality and safety: from concept to measurement. Annals of Surgery 239, 475–82. Wetzel, C., Kneebone, R., Woloshynowych, M., Nestel, D., Moorthy, K., Kidd, K. and Darzi, A. (2006) The effects of stress on surgical performance. American Journal of Surgery 191, 5–10. Yule, S., Flin, R., Paterson-Brown, S. and Maran, N. (2006) Non-technical skills for surgeons. A review of the literature. Surgery 139, 140–9. Chapter 22 Simulator-Based Evaluation of Clinical Guidelines in Acute Medicine Christoph Eich, Michael Müller, Andrea Nickut and Arnd Timmermann Clinical Background Guidelines in Acute Medicine Guidelines and related algorithms play a major role in acute medicine, in particular for the handling of time critical and high-risk situations in anaesthesiology, emergency and critical care medicine. Well-known examples are the guidelines on cardiopulmonary resuscitation (ASA 2006, Biarent et al. 2005, 2006). Furthermore, numerous national and local guidelines exist on time-critical procedures and interventions, for example, on rapid-sequence induction (RSI) of anaesthesia in children (Schmidt et al. 2007). The new technique (RSI controlled) makes the potentially hazardous procedure of anaesthesia induction in a non-fasted infant less time critical. It is thought to produce less stress and fewer unsafe actions and critical incidents and hence may be safer for the child. Characteristically, guidelines are based on a thoroughly performed consensus process on best scientic evidence, paired with expert knowledge and experience. Though guidelines aim to combine this with clinical feasibility, infrastructural conditions and educational aspects, evidence of their actual clinical superiority, and in particular of their whole entirety, is limited (Morley and Zaritsky 2005, Nolan 2005). A valid clinical evaluation frequently collides with ethical considerations, particularly when children are involved. Hence, a high proportion of guidelines in acute medicine may be evaluated by clinical observation, retrospective epidemiological analysis and expert appraisal only. The question is whether a high- delity simulator in an authentic clinical environment would be able to decrease this knowledge gap. Stress and Safety Stress is dened as a set of adaptive reactions of an individual undergoing aggression, stimulation, or effort. These stimuli provoke an increase in sympathetic activity that causes a raise of heart and respiratory rate, arterial blood pressure, and metabolic processes (Vrijkotte et al. 2000). Hence stress can generally be quantied by measuring cardiorespiratory and metabolic parameters. Safer Surgery 372 Physical and mental stress in operating room team members are both thought to have negative impact on patient safety as high stress levels are likely to be related with unsafe actions and critical events (Lazarus et al. 1952, Gaba and Howard 2002, Howard et al. 2003, Moorthy et al. 2003, Metz 2007). As a consequence, clinical guidelines in acute medicine aim to reduce stress to increase patient safety. On the other hand, optimum stress levels which allow best individual performance are subject to interpersonal variations within a wide range (Metz 2007, Nater et al. 2007a). As there seems to be no direct or even linear correlation between stress levels and safety such as unsafe actions and critical incidents, stress measurement alone would have only limited analytical power for the evaluation of clinical guidelines. In order to get more valid answers, we combine objective stress measurement with observational criteria and self-assessment (perception). The Model High delity infant simulators have only been commercially available for a few years. There now exists a sizable body of experience with their use for training and assessment purposes (Gaba et al. 1998, Howard et al. 2003, Eppich et al. 2006, Eich et al. 2007b, Overly et al. 2007). In our view, and according to our experience, they should also be suitable for the evaluation of guidelines in acute medicine when placed in sufciently realistic clinical environments. Our research group at the medical simulation centres of Göttingen and Dresden aims to establish a model for simulator-based evaluation of clinical guidelines in order to decrease the evidence gap prior to their implementation. As a rst pilot study of practical application, we compare a newly recommended controlled rapid-sequence-induction technique (RSI controlled) for non-fasted infants with the classic technique (RSI classic) using a high delity infant simulator (Eich et al. 2007a, Schmidt et al. 2007). Using an observational checklist we record critical events and unsafe actions. We simultaneously measure psycho-vegetative stress, based on ergospirometry (cardiorespiratory markers), saliva analyses (cortisol and alpha-amylase) and self-assessment (stress and safety perception). The Infant Simulator and its Environment For our current pilot study on RSI guidelines for infants, we work with a SimBaby ™ infant simulator (Laerdal Medical), using software version 1.4 and its customary touch screen vital signs monitor. The baby mannequin is placed on an operating table in an authentic theatre environment within our simulation centre (see Figure 22.1). SimBaby ™ is a high delity integrated infant simulator which features all essential clinical and monitored vital signs. It is controlled via computer interface. It has no physiologic model implemented but scenarios and trends can be pre- programmed. For our RSI study, we programmed a standardized scenario on Simulator-Based Evaluation of Clinical Guidelines in Acute Medicine 373 anaesthesia induction in a four-week-old baby boy with pyloric stenosis. Initial respiratory rate, oxygen saturation, heart rate, blood pressure , torso movement and vital signs monitor set-up were dened. Additionally we programmed three trends for respiratory rate, heart rate and oxygen saturation: ‘RSI classic’, ‘RSI controlled’ and ‘recovery from hypoxemia’ (oxygen desaturation). All trends are started manually on induction of anaesthesia or on appropriate ventilation respectively. The trends for oxygen saturation after breathing stops (apnoea) are based on oxygenation data derived from the Nottingham physiology simulator, as calculated for a one-month-old infant with no effective pre-oxygenation and open airway (Hardman and Wills 2006). The Scenario The techniques for RSI are standardized (see Figure 22.2). The same anaesthesia nurse assists all procedures in an identical manner strictly following the protocol. The induction drugs are drawn up in the predetermined dose and are administered by the anesthesia nurse when prompted by the candidate. The nurse also provides exact timing to allow the rst intubation attempt. In combination with programmed scenario and trends, this procedure ensures that all candidates are Figure 22.1 Study setting in theatres: infant simulator and anaesthesia work station, anaesthesia nurse (left) and candidate (right) with the mobile ergospirometry unit applied . surgeons’ risk estimation: From intuition to quantitative modeling. World Journal of Surgery 32, 324–5. Safer Surgery 370 Sevdalis, N., Davis, R,. Koutantji, M., Undre, S., Darzi, A. and Vincent,. applied to surgery and, nally, present empirical work that has been carried out by our research team to date to assess decision-making processes and decision- making as a skill in surgery. We. mortality for major surgery using a number of patient vignettes with varying risk factors. Vignette construction was guided by a published risk model as a gold standard. Post-feedback, participants