Teaching clinicians shared decision making and risk communication online: an evaluation study

Objectives: To describe the development and initial evaluation of a brief e-learning course as a means of teaching shared decision making and risk communication skills to clinicians of all specialties. Design: Comparison pre-course and post-course of scores in subjective confidence and objective knowledge about shared decision making and risk communication. Setting: Online and open to all specialties and levels of clinical experience, including students. Participants: The course is freely available online and all who started the course from September 2018 to May 2020 were invited to participate in the evaluation study. Intervention: The self-guided e-learning course is made up of four modules and takes approximately 2 hours to complete. It is hosted on the website of the Winton Centre for Risk Communication and the UK’s National Health Service e-learning platform. Main outcome measures: Pre-course and post-course confidence in performing shared decision making (as measured by a 10-item scale adapted from the OPTION tool; total score range 10–50), and objective knowledge about basic principles of shared decision making and risk communication, as measured by performance on four knowledge questions and three calculations. At course commencement, a single item from the Berlin Numeracy Test, and the eight-item Subjective Numeracy Test were also asked. Results: Of 366 unique participants who consented and commenced the course, 210 completed all modules and the final post-course test. Participants’ mean age was 38.1 years, 69% were in current clinical practice and had a mean of 10.5 years of clinical practice. Numeracy was relatively low, with 50.7% correctly answering the Berlin Numeracy Test item pre-course. Participants who completed the course showed a significant improvement in their confidence by a mean summed score of 3.7 units (95% CI 2.9 to 4.6, p<0.0001) from a mean pre-course of 37.4 (SD 6.1) to post-course of 41.1 (SD 6.9). There was an increase in the proportion of correct answers for most knowledge questions (p<0.0001, p=0.013 for two directly compared), although no improvement in most skill questions that involved numbers (eg, calculating relative risks). Participants with higher numeracy appeared to show higher skill and confidence on most questions. Conclusions: This online, free e-learning course was successful in increasing participants’ confidence in, and some aspects of knowledge about, shared decision making and risk communication. It also highlighted the need for improvements in clinicians’ numerical skills as a vital part of training. We suggest that the course is used in combination with practical face-to-face experience and more intensive numerical skills training

Perioperative shared decision making – How do we train clinicians?

Objective: Decision making about high-risk surgery can be complex, particularly when outcomes may be uncertain. Clinicians have a legal and ethical responsibility to support decision making which fits with patients' values and preferences. In the UK, preoperative assessment and optimisation is led by Anaesthetists in clinic several weeks prior to planned surgery. Training in supporting shared decision making (SDM) has been identified as an area of need among UK anaesthetists with leadership roles in perioperative care. Methods: We describe adaptation of a generic SDM workshop to perioperative care, in particular to decisions on high-risk surgery, and its delivery to UK healthcare professionals over a two-year period. Feedback from workshops were thematically analysed. We explored further improvements to the workshop and ideas for development and dissemination. Results: The workshops were well received, with high satisfaction for techniques used, including video demonstrations, role-play and discussions. Thematic analysis identified a desire for multidisciplinary training and training in using patient aids. Conclusion: Qualitative findings suggest workshops were considered useful with perceived improvement in SDM awareness, skills and reflective practice. Innovation: This pilot introduces a new modality of training in the perioperative setting providing physicians, particularly Anaesthetists, with previously unavailable training needed to facilitate complex discussions

Predicting hospital resource use during COVID-19 surges: a simple but flexible discretely integrated condition event simulation of individual patient-hospital trajectories

Objectives: To assist with planning hospital resources, including critical care (CC) beds, for managing patients with COVID-19. Methods: An individual simulation was implemented in Microsoft Excel using a discretely integrated condition event simulation. Expected daily cases presented to the emergency department were modeled in terms of transitions to and from ward and CC and to discharge or death. The duration of stay in each location was selected from trajectory-specific distributions. Daily ward and CC bed occupancy and the number of discharges according to care needs were forecast for the period of interest. Face validity was ascertained by local experts and, for the case study, by comparing forecasts with actual data. Results: To illustrate the use of the model, a case study was developed for Guy's and St Thomas’ Trust. They provided inputs for January 2020 to early April 2020, and local observed case numbers were fit to provide estimates of emergency department arrivals. A peak demand of 467 ward and 135 CC beds was forecast, with diminishing numbers through July. The model tended to predict higher occupancy in Level 1 than what was eventually observed, but the timing of peaks was quite close, especially for CC, where the model predicted at least 120 beds would be occupied from April 9, 2020, to April 17, 2020, compared with April 7, 2020, to April 19, 2020, in reality. The care needs on discharge varied greatly from day to day. Conclusions: The DICE simulation of hospital trajectories of patients with COVID-19 provides forecasts of resources needed with only a few local inputs. This should help planners understand their expected resource needs