Checklists improve experts' diagnostic decisions

Context Checklists are commonly proposed tools to reduce error. However, when applied by experts, checklists have the potential to increase cognitive load and result in expertise reversal'. One potential solution is to use checklists in the verification stage, rather than in the initial interpretation stage of diagnostic decisions. This may avoid expertise reversal by preserving the experts' initial approach. Whether checklist use during the verification stage of diagnostic decision making improves experts' diagnostic decisions is unknown. Methods Fifteen experts interpreted 18 electrocardiograms (ECGs) in four different conditions: undirected interpretation; verification without a checklist; verification with a checklist, and interpretation combined with verification with a checklist. Outcomes included the number of errors, cognitive load, interpretation time and interpretation length. Outcomes were compared in two analyses: (i) a comparison of verification conditions with and without a checklist, and (ii) a comparison of all four conditions. Standardised scores for each outcome were used to calculate the efficiency of a checklist and to weigh its relative benefit against its relative cost in terms of cognitive load imposed, interpretation time and interpretation length. Results In both analyses, checklist use was found to reduce error (more errors were corrected in verification conditions with checklists [0.29 +/- 0.77 versus 0.03 +/- 0.61 errors per ECG], and fewer net errors occurred in all conditions with checklists [0.39 +/- 1.14 versus 1.04 +/- 1.49 errors per ECG];

Do you have to re-examine to reconsider your diagnosis? Checklists and cardiac exam

Background Few studies have investigated whether clinicians can use checklists to verify their diagnostic decisions. Checklists may improve accuracy by prompting clinicians to reconsider or recollect information but might impair decision making by adding to clinicians' cognitive load. This study assessed whether checklists improve cardiac exam diagnostic accuracy, and whether this benefit is dependent on collecting additional information. Methods 191 internal medicine residents examined a cardiopulmonary simulator. They provided a diagnosis, subjective rating of certainty, and key findings before and after using a checklist. Residents were randomised; half were allowed access to the simulator and half were prohibited access to the simulator while using the checklist. Residents rated their cognitive load in each step: prechecklist diagnosis, checklist use and postchecklist diagnosis. Result Verifying with a checklist resulted in improved diagnostic accuracy; 88 residents (46%) made the correct diagnosis before using the checklist compared with 97 (51%) afterwards, p=0.04. The benefit of checklist use was restricted to residents allowed to re-examine the simulator (10 changed to correct diagnosis and one to an incorrect diagnosis) whereas no net benefit was seen among residents unable to re-examine the simulator (two changed to a correct diagnosis and two to an incorrect diagnosis, p=0.03). Those able to re-examine the simulator were slightly more confident after checklist use, whereas those unable to re-examine were slightly less confident after checklist use (p=0.01). The opportunity to re-examine the simulator had no effect on the accuracy of key findings reported. Of the three steps, checklist use was associated with the lowest cognitive load (F-1,F-189=68

Некоторые робастные решения в условиях риска и неопределенности

Обсуждаются проблемы построения робастных решений в условиях риска и неопределенности. Рассматриваются две модели распределения средств для минимизации потенциальных рисков. Проблемы поиска их робастных решений сведены к соответствующим задачам линейного программирования.Обговорюються проблеми побудови робастних рішень в умовах ризику та невизначеності. Розглядаються дві моделі розподілу коштів для мінімізації потенційних ризиків. Проблеми пошуку їх робастних рішень зведено до відповідних задач лінійного програмування.Problems of constructing robust decisions in conditions of risk and uncertainty are discussed. Two fund distribution models for minimization of potential risks are considered. Problems of searching their robust decisions are reduced to appropriate linear programming problems