A Review of Measuring the Cognitive Workload of Electronic Health Records

Wilbanks, Bryan A; McMullan, Susan P.

doi:10.1097/cin.0000000000000469

Cited by 16 publications

(44 citation statements)

References 31 publications

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“…Wilbanks and McMullen reviewed cognitive workload associated with EHRs and generally found that it is hard to measure with validity. Specifically regarding CDS, they proposed that the related concepts of alarm fatigue and desensitization were the most pressing for patient safety and they suggested minimizing within-patient identical alerts to ameliorate the negative effects on cognitive load 19 . Finally, Tolley et al systematically reviewed CDS literature from 2007-2014 to identify areas of improvement.…”

Section: Resultsmentioning

confidence: 99%

Clinical Decision Support and Implications for the Clinician Burnout Crisis

Janković

Chen

2020

Yearb Med Inform

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Objectives: This survey aimed to review aspects of clinical decision support (CDS) that contribute to burnout and identify key themes for improving the acceptability of CDS to clinicians, with the goal of decreasing said burnout. Methods: We performed a survey of relevant articles from 2018-2019 addressing CDS and aspects of clinician burnout from PubMed and Web of Science™. Themes were manually extracted from publications that met inclusion criteria. Results: Eighty-nine articles met inclusion criteria, including 12 review articles. Review articles were either prescriptive, describing how CDS should work, or analytic, describing how current CDS tools are deployed. The non-review articles largely demonstrated poor relevance and acceptability of current tools, and few studies showed benefits in terms of efficiency or patient outcomes from implemented CDS. Encouragingly, multiple studies highlighted steps that succeeded in improving both acceptability and relevance of CDS. Conclusions: CDS can contribute to clinician frustration and burnout. Using the techniques of improving relevance, soliciting feedback, customization, measurement of outcomes and metrics, and iteration, the effects of CDS on burnout can be ameliorated.

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Section: Resultsmentioning

confidence: 99%

Clinical Decision Support and Implications for the Clinician Burnout Crisis

Janković

Chen

2020

Yearb Med Inform

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“…Information overload inhibits clinical medical staff from making effective decisions based on electronic medical records [13] and may contribute to errors or clinician burnout [14]. Other literature has found that overly informative medical data interfaces can increase clinical medical staff members' cognitive workload, ultimately impairing patient outcomes [15]. Simplified electronic interfaces have been used to improve outcomes in intensive care units, which motivated our approach to design as we aimed to determine whether these results generalize to a less-urgent setting [16].…”

Section: Interface Developmentmentioning

confidence: 99%

Evaluating Simplified Web Interfaces of Risk Models for Clinical Use: Pilot Survey Study

et al. 2021

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Background In this pilot study, we investigated sociotechnical factors that affect intention to use a simplified web model to support clinical decision making. Objective We investigated factors that are known to affect technology adoption using the unified theory of acceptance and use of technology (UTAUT2) model. The goal was to pilot and test a tool to better support complex clinical assessments. Methods Based on the results of a previously published work, we developed a web-based mobile user interface, WebModel, to allow users to work with regression equations and their predictions to evaluate the impact of various characteristics or treatments on key outcomes (eg, survival time) for chronic obstructive pulmonary disease. The WebModel provides a way to combat information overload and more easily compare treatment options. It limits the number of web forms presented to a user to between 1 and 20, rather than the dozens of detailed calculations typically required. The WebModel uses responsive design and can be used on multiple devices. To test the WebModel, we designed a questionnaire to probe the efficacy of the WebModel and assess the usability and usefulness of the system. The study was live for one month, and participants had access to it over that time. The questionnaire was administered online, and data from 674 clinical users who had access to the WebModel were captured. SPSS and R were used for statistical analysis. Results The regression model developed from UTAUT2 constructs was a fit. Specifically, five of the seven factors were significant positive coefficients in the regression: performance expectancy (β=.2730; t=7.994; P<.001), effort expectancy (β=.1473; t=3.870; P=.001), facilitating conditions (β=.1644; t=3.849; P<.001), hedonic motivation (β=.2321; t=3.991; P<.001), and habit (β=.2943; t=12.732). Social influence was not a significant factor, while price value had a significant negative influence on intention to use the WebModel. Conclusions Our results indicate that multiple influences impact positive response to the system, many of which relate to the efficiency of the interface to provide clear information. Although we found that the price value was a negative factor, it is possible this was due to the removal of health workers from purchasing decisions. Given that this was a pilot test, and that the system was not used in a clinical setting, we could not examine factors related to actual workflow, patient safety, or social influence. This study shows that the concept of a simplified WebModel could be effective and efficient in reducing information overload in complex clinical decision making. We recommend further study to test this in a clinical setting and gather qualitative data from users regarding the value of the tool in practice.

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“…The mental load involves the psychological effort to perform a specific task. In the literature, the terms cognitive workload (CW) and mental workload are synonymously used [ 2 ]. An increase in CW in medical practice can lead to undesirable consequences such as information overload, mental fatigue, decrease in situational awareness, and errors in decision-making.…”

Section: Introductionmentioning

confidence: 99%

“…A wide number of strategies have been used to measure CW. These can be broadly divided into three groups [ 2 ]. The first group includes psychometric instruments , with examples such as surveys, self-reports and questionnaires, which subjectively measure the perceived CW of a subject.…”

Section: Introductionmentioning

confidence: 99%

“…Eye-tracking devices have advanced in recent years and provide adequate temporal resolution and precision to detect relatively small changes in the pupil diameter. Eye-tracking for continuous assessment of objective CW by measurement of pupil diameter changes is therefore possible [ 2 ]. Specifically, remote non-contact eye trackers have been increasingly used for pupillometric measurement of CW [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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Machine learning-based analysis of operator pupillary response to assess cognitive workload in clinical ultrasound imaging

Sharma

Drukker

Papageorghiou

et al. 2021

Computers in Biology and Medicine

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Introduction Pupillometry, the measurement of eye pupil diameter, is a well-established and objective modality correlated with cognitive workload. In this paper, we analyse the pupillary response of ultrasound imaging operators to assess their cognitive workload, captured while they undertake routine fetal ultrasound examinations. Our experiments and analysis are performed on real-world datasets obtained using remote eye-tracking under natural clinical environmental conditions. Methods Our analysis pipeline involves careful temporal sequence (time-series) extraction by retrospectively matching the pupil diameter data with tasks captured in the corresponding ultrasound scan video in a multi-modal data acquisition setup. This is followed by the pupil diameter pre-processing and the calculation of pupillary response sequences. Exploratory statistical analysis of the operator pupillary responses and comparisons of the distributions between ultrasonographic tasks (fetal heart versus fetal brain) and operator expertise (newly-qualified versus experienced operators) are performed. Machine learning is explored to automatically classify the temporal sequences into the corresponding ultrasonographic tasks and operator experience using temporal, spectral, and time-frequency features with classical (shallow) models, and convolutional neural networks as deep learning models. Results Preliminary statistical analysis of the extracted pupillary response shows a significant variation for different ultrasonographic tasks and operator expertise, suggesting different extents of cognitive workload in each case, as measured by pupillometry. The best-performing machine learning models achieve receiver operating characteristic (ROC) area under curve (AUC) values of 0.98 and 0.80, for ultrasonographic task classification and operator experience classification, respectively. Conclusion We conclude that we can successfully assess cognitive workload from pupil diameter changes measured while ultrasound operators perform routine scans. The machine learning allows the discrimination of the undertaken ultrasonographic tasks and scanning expertise using the pupillary response sequences as an index of the operators’ cognitive workload. A high cognitive workload can reduce operator efficiency and constrain their decision-making, hence, the ability to objectively assess cognitive workload is a first step towards understanding these effects on operator performance in biomedical applications such as medical imaging.

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A Review of Measuring the Cognitive Workload of Electronic Health Records

Cited by 16 publications

References 31 publications

Clinical Decision Support and Implications for the Clinician Burnout Crisis

Clinical Decision Support and Implications for the Clinician Burnout Crisis

Evaluating Simplified Web Interfaces of Risk Models for Clinical Use: Pilot Survey Study

Machine learning-based analysis of operator pupillary response to assess cognitive workload in clinical ultrasound imaging

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