Clutter in Electronic Medical Records

Moacdieh, Nadine B.; Sarter, Nadine

doi:10.1177/0018720814564594

Cited by 37 publications

(10 citation statements)

References 34 publications

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“…Eye-tracking studies comparing novice and expert reviewers of radiographic images or ECGs suggest that a meticulous orderly search pattern is associated with less experience (van der Gijp et al, 2017;Wood et al, 2014). It may also be adopted in the setting of increased cognitive workload, as described in an eye-tracking study of EHR search tasks (Moacdieh & Sarter, 2015). Given these associations with performance and cognitive workload, we conclude that overcomplete searching behavior may be counterproductive and, thus, reflect alert fatigue in our simulations.…”

Section: Discussionmentioning

confidence: 84%

“…performance in both radiology and ECG interpretation (van der Gijp et al, 2017;Wood et al, 2014). Gaze fixations have also been directly associated with cognitive workload during EHR tasks (Moacdieh & Sarter, 2015). Therefore, we surmise that passive alerts make it easier to come to conclusions about laboratory data in general.…”

Section: Discussionmentioning

confidence: 98%

“…Similarly, experienced cardiologists require fewer gaze fixations to make a diagnosis from electrocardiograms (Wood, Batt, Appelboam, Harris, & Wilson, 2014). In contrast, inexperience has been associated with more gaze fixations, orderly search patterns, and visual neglect of important information, all of which have been observed to correlate with cognitive workload during EHR review (Moacdieh & Sarter, 2015). We have shown that gaze fixation data can predict a user's ability to recognize patient safety issues during simulated ICU rounds (Gold, Stephenson, Gorsuch, Parthasarathy, & Mohan, 2016).…”

Section: Introductionmentioning

confidence: 88%

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Impact of Passive Laboratory Alerts on Navigating Electronic Health Records in Intensive Care Simulations

et al. 2018

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Passive alerts highlighting abnormal results in the electronic health record (EHR) are abundant. However, their impact on EHR use and contribution to alert fatigue remains unknown. We examined the impact of passive laboratory alerts on EHR usability and recognition of patient safety issues during a simulated intensive care unit (ICU) rounding exercise. We performed a post hoc comparison of simulated ICU rounding exercises, comparing performance with and without passive laboratory alerts. Participants (physician trainees and advanced practice providers) reviewed EHRs depicting critically ill patients, which included multiple embedded patient safety issues. We employed eye tracking during chart review to determine the impact of alerts on EHR navigation. 92 simulations were reviewed, 46 with passive alerts activated. Passive alerts were associated with reduced gaze fixations on both normal and abnormal laboratory results. Activation of alerts was also associated with increased time to first fixation on abnormal results and increased saccadic eye movements. Alerts had no impact on either the duration spent reviewing laboratory results or identification of patient safety issues. Activating passive alerts in the EHR was associated with increase scanning behavior, which may explain why reductions in gaze fixations on lab results did not translate into improved efficiency or performance. This may be due to alert fatigue in the form of oversearching.

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Section: Discussionmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 88%

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Impact of Passive Laboratory Alerts on Navigating Electronic Health Records in Intensive Care Simulations

et al. 2018

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“…Furthermore, our pilot study characterized the validity of this new format based on subjective answers from our referral base. Future studies could look for objective evidence of validityby tracking time, tracking mouse clicks and/or using eye tracking programs [ 7 ]. Lastly, one of the limitations of our study was that we only received 34 responses.…”

Section: Discussionmentioning

confidence: 99%

Turning Electromyography Reports Upside Down: A Pilot Study Surveying Referring Providers

Shenoy

Baquis

2016

TOMINFOJ

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Providers are expressing a desire for more efficient ways to retrieve relevant clinical data from the Electronic Health Record. In an effort to improve our Electromyography and Nerve Conduction Study reports, we surveyed referring providers on the effects of having the IMPRESSION at the start of the report. Our survey respondents felt that using this format for an Electromyography and Nerve Conduction Study report significantly improved the quality of the report while saving them time and/or mouse clicks when interpreting the report. Electro diagnosticians might consider using this format for their Electromyography and Nerve Conduction Study reports to improve referring provider satisfaction.

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“…Since its introduction, the NNI has gained popularity in the field, and convergent evidence for its validity has been therefore provided (e.g. Dillar et al, 2014;Moacdieh and Sarter, 2015). The NNI has been also featured in relevant textbooks like Holmqvist et al's (2011) andWickens et al's (2013), thus making very likely its future use in the HF/E community.…”

Section: Introductionmentioning

confidence: 99%

A Simple(r) Tool For Examining Fixations

Nocera

Capobianco²,

Mastrangelo³

2016

JEMR

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This short paper describes an update of A Simple Tool For Examining Fixations (ASTEF) developed for facilitating the examination of eye-tracking data and for computing a spatial statistics algorithm that has been validated as a measure of mental workload (namely, the Nearest Neighbor Index: NNI). The code is based on Matlab® 2013a and is currently distributed on the web as an open-source project. This implementation of ASTEF got rid of many functionalities included in the previous version that are not needed anymore considering the large availability of commercial and open-source software solutions for eye-tracking. That makes it very easy to compute the NNI on eye-tracking data without the hassle of learning complicated tools. The software also features an export function for creating the time series of the NNI values computed on each minute of the recording. This feature is crucial given that the spatial distribution of fixations must be used to test hypotheses about the time course of mental load.

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Clutter in Electronic Medical Records

Abstract: Display designers need to be aware of the risks of clutter in EMRs and other complex displays and can use the identified eye tracking metrics to evaluate and/or adjust their display.

Cited by 37 publications

References 34 publications

Impact of Passive Laboratory Alerts on Navigating Electronic Health Records in Intensive Care Simulations

Impact of Passive Laboratory Alerts on Navigating Electronic Health Records in Intensive Care Simulations

Turning Electromyography Reports Upside Down: A Pilot Study Surveying Referring Providers

A Simple(r) Tool For Examining Fixations

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