Lessons identified from applications of the Risk Analysis Quality Test Release 1.0.

The Risk Analysis Quality Test Release 1.0 (RAQT1.0) was developed as a framework to encourage mutual understanding between technical risk analysts and risk management decision makers of risk assessment quality indicators. The initial version (release 1.0) was published by the Society for Risk Analysis (SRA) in 2020 with the intent of learning from early test applications whether the approach was useful and whether changes in approach or contents would be helpful. The results of applications across three diverse fields are reported here. The applications include both retrospective evaluations of past risk assessments and prospective guidance on the design of future risk assessment projects or systems. The fields represented include Quantitative Microbial Risk Assessment, Cultural Property Risk Analysis, and Software Development Cyber Risk Analysis. The RAQT1.0 proved helpful for identifying shortcomings in all applications. Ways in which the RAQT1.0 might be improved are also identified.

Shared leadership in tertiary care: design of a simulation for patient safety decision-making in healthcare management teams.

Introduction: Simulation-based training (SBT) on shared leadership (SL) and group decision-making (GDM) can contribute to the safe and efficient functioning of a healthcare system, yet it is rarely incorporated into healthcare management training. The aim of this study was design, develop and validate a robust and evidence-based SBT to explore and train SL and GDM. Method: Using a two-stage iterative simulation design approach, 103 clinical and non-clinical managerial students and healthcare professionals took part in an SBT that contained real-world problems and opportunities to improve patient safety set within a fictional context. Self-report data were gathered, and a focus group was conducted to address the simulation's degree of realism, content, relevance, as well as areas for improvement. Results: Participants experienced the simulation scenario, the material and the role assignment as realistic and representative of real-world tasks and decision contexts, and as a good opportunity to identify and enact relevant tasks, behaviours and knowledge related to SL and GDM. Areas for improvement were highlighted with regard to involving an actor who challenges SL and GDM; more preparatory time to allow for an enhanced familiarisation of the content; and, video debriefs to reflect on relevant behaviours and team processes. Conclusions: Our simulation was perceived as an effective method to develop SL and GDM within the context of patient safety and healthcare management. Future studies could extend this scenario method to other areas of healthcare service and delivery, and to different sectors that require diverse groups to make complex decisions.

Understanding health management and safety decisions using signal processing and machine learning.

BACKGROUND: Small group research in healthcare is important because it deals with interaction and decision-making processes that can help to identify and improve safer patient treatment and care. However, the number of studies is limited due to time- and resource-intensive data processing. The aim of this study was to examine the feasibility of using signal processing and machine learning techniques to understand teamwork and behaviour related to healthcare management and patient safety, and to contribute to literature and research of teamwork in healthcare. METHODS: Clinical and non-clinical healthcare professionals organised into 28 teams took part in a video- and audio-recorded role-play exercise that represented a fictional healthcare system, and included the opportunity to discuss and improve healthcare management and patient safety. Group interactions were analysed using the recurrence quantification analysis (RQA; Knight et al., 2016), a signal processing method that examines stability, determinism, and complexity of group interactions. Data were benchmarked against self-reported quality of team participation and social support. Transcripts of group conversations were explored using the topic modelling approach (Blei et al., 2003), a machine learning method that helps to identify emerging themes within large corpora of qualitative data. RESULTS: Groups exhibited stable group interactions that were positively correlated with perceived social support, and negatively correlated with predictive behaviour. Data processing of the qualitative data revealed conversations focused on: (1) the management of patient incidents; (2) the responsibilities among team members; (3) the importance of a good internal team environment; and (4) the hospital culture. CONCLUSIONS: This study has shed new light on small group research using signal processing and machine learning methods. Future studies are encouraged to use these methods in the healthcare context, and to conduct further research on how the nature of group interaction and communication processes contribute to the quality of team and task decision-making.

Engaging with Comparative Risk Appraisals: Public Views on Policy Priorities for Environmental Risk Governance.

Communicating the rationale for allocating resources to manage policy priorities and their risks is challenging. Here, we demonstrate that environmental risks have diverse attributes and locales in their effects that may drive disproportionate responses among citizens. When 2,065 survey participants deployed summary information and their own understanding to assess 12 policy-level environmental risks singularly, their assessment differed from a prior expert assessment. However, participants provided rankings similar to those of experts when these same 12 risks were considered as a group, allowing comparison between the different risks. Following this, when individuals were shown the prior expert assessment of this portfolio, they expressed a moderate level of confidence with the combined expert analysis. These are important findings for the comprehension of policy risks that may be subject to augmentation by climate change, their representation alongside other threats within national risk assessments, and interpretations of agency for public risk management by citizens and others.

Confluence and Contours: Reflexive Management of Environmental Risk.

Government institutions have responsibilities to distribute risk management funds meaningfully and to be accountable for their choices. We took a macro-level sociological approach to understanding the role of government in managing environmental risks, and insights from micro-level psychology to examine individual-level risk-related perceptions and beliefs. Survey data from 2,068 U.K. citizens showed that lay people's funding preferences were associated positively with beliefs about responsibility and trust, yet associations with perception varied depending on risk type. Moreover, there were risk-specific differences in the funding preferences of the lay sample and 29 policymakers. A laboratory-based study of 109 participants examined funding allocation in more detail through iterative presentation of expert information. Quantitative and qualitative data revealed a meso-level framework comprising three types of decisionmakers who varied in their willingness to change funding allocation preferences following expert information: adaptors, responders, and resistors. This research highlights the relevance of integrated theoretical approaches to understanding the policy process, and the benefits of reflexive dialogue to managing environmental risks.

The role of teamworking in error reduction during vascular procedures.

BACKGROUND: To examine the associations between teamworking processes and error rates during vascular surgical procedures and then make informed recommendations for future studies and practices in this area. METHODS: This is a single-center observational pilot study. Twelve procedures were observed over a 3-week period by a trained observer. Errors were categorized using a standardized error capture tool. Leadership and teamworking processes were categorized based on the Malakis et al. (2010) framework. Data are expressed as frequencies, means, standard deviations and percentages. RESULTS: Errors rates (per hour) were likely to be reduced when there were effective prebriefing measures to ensure that members were aware of their roles and responsibilities (4.50 vs. 5.39 errors/hr), communications were kept to a practical and effective minimum (4.64 vs. 5.56 errors/hr), when the progress of surgery was communicated throughout (3.14 vs. 8.33 errors/hr), and when team roles changed during the procedure (3.17 vs. 5.97 errors/hr). CONCLUSIONS: Reduction of error rates is a critical goal for surgical teams. The present study of teamworking processes in this environment shows that there is a variation that should be further examined. More effective teamworking could prevent or mitigate a range of errors. The development of vascular surgical team members should incorporate principles of teamworking and appropriate communication.

A role for human reliability analysis (HRA) in preventing drinking water incidents and securing safe drinking water.

The prevalence of water quality incidents and disease outbreaks suggests an imperative to analyse and understand the roles of operators and organisations in the water supply system. One means considered in this paper is through human reliability analysis (HRA). We classify the human errors contributing to 62 drinking water accidents occurring in affluent countries from 1974 to 2001; define the lifecycle of these incidents; and adapt Reason's 'Swiss cheese' model for drinking water safety. We discuss the role of HRA in human error reduction and drinking water safety and propose a future research agenda for human error reduction in the water sector.