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Context Patient handovers remain a significant patient safety challenge. Cognitive load theory (CLT) can be used to identify the cognitive mechanisms for handover errors. The ability to measure cognitive load types during handovers could drive the development of more effective curricula and protocols. No such measure currently exists. Methods The authors developed the Cognitive Load Inventory for Handoffs (CLIH) using a multi‐step process, including expert interviews to enhance content validity and talk‐alouds to optimise response process validity. The final version contained 28 items. From January to March 2019, we administered a cross‐sectional survey to 1807 residents and fellows from a large health care system in the USA. Participants completed the CLIH following a handover. Exploratory factor analysis of data from one‐third of respondents identified high‐performing items; confirmatory factor analysis of data from the remaining sample assessed model fit. Model fit was evaluated using the comparative fit index (CFI) (>0.90), Tucker‐Lewis index (TFI) (>0.80), standardised root mean square residual (SRMR) (<0.08) and root mean square of error of approximation (RMSEA) (<0.08). Results Participants included 693 trainees (38.4%) (231 in the exploratory study and 462 in the confirmatory study). Eleven items were removed during exploratory factor analysis. Confirmatory factor analysis of the 16 remaining items (five for intrinsic load, seven for extraneous load and four for germane load) supported a three‐factor model and met criteria for good model fit: the CFI was 0.95, TFI was 0.93, RMSEA was 0.074 and SRMR was 0.07. The factor structure was comparable for gender and role. Intrinsic, extraneous and germane load scales had high internal consistency. With one exception, scale scores were associated, as hypothesised, with postgraduate level and clinical setting. Conclusions The CLIH measures three types of cognitive load during patient handovers. Evidencefor validity is provided for the CLIH's content, response process, internal structure and association with other variables. This instrument can be used to determine the relative drivers of cognitive load during handovers in order to optimize handover instruction and protocols.

IntroductionUnder the Next Accreditation System, programs need to find ways to collect and assess meaningful reportable information on its residents to assist the program director regarding resident milestone progression. This paper discusses the process that one large Internal Medicine Residency Program used to provide both quantitative and qualitative data to its clinical competency committee (CCC) through the creation of a resident dashboard.MethodsProgram leadership at a large university-based program developed four new end of rotation evaluations based on the American Board of Internal Medicine (ABIM) and Accreditation Council of Graduated Medical Education's (ACGME) 22 reportable milestones. A resident dashboard was then created to pull together both milestone- and non-milestone-based quantitative data and qualitative data compiled from faculty, nurses, peers, staff, and patients.ResultsDashboards were distributed to the members of the CCC in preparation for the semiannual CCC meeting. CCC members adjudicated quantitative and qualitative data to present their cohort of residents at the CCC meeting. Based on the committee's response, evaluation scores remained the same or were adjusted. Final milestone scores were then entered into the accreditation data system (ADS) on the ACGME website.ConclusionsThe process of resident assessment is complex and should comprise both quantitative and qualitative data. The dashboard is a valuable tool for program leadership to use both when evaluating house staff on a semiannual basis at the CCC and to the resident in person.

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