Debiasing Judgements Using a Distributed Cognition Approach: A Scoping Review of Technological Strategies.

OBJECTIVE: To review and synthesise research on technological debiasing strategies across domains, present a novel distributed cognition-based classification system, and discuss theoretical implications for the field. BACKGROUND: Distributed cognition theory is valuable for understanding and mitigating cognitive biases in high-stakes settings where sensemaking and problem-solving are contingent upon information representations and flows in the decision environment. Shifting the focus of debiasing from individuals to systems, technological debiasing strategies involve designing system components to minimise the negative impacts of cognitive bias on performance. To integrate these strategies into real-world practices effectively, it is imperative to clarify the current state of evidence and types of strategies utilised. METHODS: We conducted systematic searches across six databases. Following screening and data charting, identified strategies were classified into (i) group composition and structure, (ii) information design and (iii) procedural debiasing, based on distributed cognition principles, and cognitive biases, classified into eight categories. RESULTS: Eighty articles met the inclusion criteria, addressing 100 debiasing investigations and 91 cognitive biases. A majority (80%) of the identified debiasing strategies were reportedly effective, whereas fourteen were ineffective and six were partially effective. Information design strategies were studied most, followed by procedural debiasing, and group structure and composition. Gaps and directions for future work are discussed. CONCLUSION: Through the lens of distributed cognition theory, technological debiasing represents a reconceptualisation of cognitive bias mitigation, showing promise for real-world application. APPLICATION: The study results and debiasing classification presented can inform the design of high-stakes work systems to support cognition and minimise judgement errors.

Not all MDTs are created equal: international survey of HPB MDT practices.

BACKGROUND: In order to identify opportunities to streamline hepatopancreaticobiliary (HPB) multidisciplinary teams (MDT) for cancer care, it is important to first document variability in MDT team practices worldwide. We aimed to develop a comprehensive checklist of parameters to evaluate existing practices and guide the development of MDTs for new cancer services. METHODS: Participants were recruited via the International Hepato-Pancreato-Biliary Association (IHPBA) and European-African HPB Association (E-AHPBA) and emailed an anonymised online survey. The survey comprised 29 questions, including a combination of closed-ended and open-ended questions. Responses were analysed using descriptive statistics and inductive content analysis. RESULTS: Analysing 72 responses from 31 countries, we found substantial variations in HPB MDT practices across regions. Notable variability was found in core team composition, chairing practices, caseload planning, information practices and MDT audit practices. Issues impacting efficiency were common to many MDTs. DISCUSSION: MDT care is understood and applied differently across the world. There is a lack of standardisation of practice, and an apparent need for better case preparation, effective specialist contribution, improved audit frequency and metrics to improve performance. It may be valuable to consider human factors while designing MDTs to support team decision processes, minimise errors, and enhance efficiency.

Mapping cognitive biases in multidisciplinary team (MDT) decision-making for cancer care in Scotland: a cognitive ethnography study protocol.

INTRODUCTION: The efficiency of multidisciplinary teams (MDTs) in cancer care hinges on facilitating clinicians' cognitive processes as they navigate complex and uncertain judgements during treatment planning. When systems and workflows are not designed to adequately support human judgement and decision-making, even experts are prone to fallible reasoning due to cognitive biases. Incomplete integration of information or biased interpretations of patient data can lead to clinical errors and delays in the implementation of treatment recommendations. Though their impact is intuitively recognised, there is currently a paucity of empirical work on cognitive biases in MDT decision-making. Our study aims to explicate the impact of such biases on treatment planning and establish a foundation for targeted investigations and interventions to mitigate their negative effects. METHODS AND ANALYSIS: This is a qualitative, observational study. We employ cognitive ethnography, informed by the Distributed Cognition for Teamwork framework to assess and evaluate MDT decision-making processes. The study involves in-person and virtual field observations of hepatopancreaticobiliary and upper gastrointestinal MDTs and interviews with their members over several months. The data generated will be analysed in a hybrid inductive/deductive fashion to develop a comprehensive map of potential cognitive biases in MDT decision processes identifying antecedents and risk factors of suboptimal treatment planning processes. Further, we will identify components of the MDT environment that can be redesigned to support decision-making via development of an MDT workspace evaluation tool. ETHICS AND DISSEMINATION: This project has received management and ethical approvals from NHS Lothian Research and Development (2023/0245) and the University of Edinburgh Medical School ethical review committee (23-EMREC-049). Findings will be shared with participating MDTs and disseminated via a PhD thesis, international conference presentations and relevant scientific journals.

Cognitive Load Management: An Invaluable Tool for Safe and Effective Surgical Training.

This article highlights the importance of considering Cognitive Load (CL) and Cognitive Load Theory (CLT) during surgical training, focusing on the acquisition of intra-operative skills. It describes the basis of CLT with the overarching aim of describing CLT-based techniques to enhance current training strategies and surgical performance, many of which are instinctively already employed in surgical practice. Currently, methods of feedback and assessment are imperfect - typically subjective, unsystematic, opportunistic, or retrospective, and at risk of human bias. Surgical Sabermetrics, the advanced analytics of surgical and audio-visual data, aims to enhance this feedback by providing objective, real-time, digital-based feedback. This article introduces the benefit of real-time measurement of CL to enhance feedback and its applications to surgical performance that follow the ethos of Surgical Sabermetrics.1 The 2022 theme for ICOSET was "Making it Better." Cognitive Load and Surgical Sabermetrics principles provide tools to make Surgical training better, with the goal of higher quality care for patients.