Evidence map›Paper›PMID 34255674›Full record

Observational studyJournal of medical Internet research2021

Frustration With Technology and its Relation to Emotional Exhaustion Among Health Care Workers: Cross-sectional Observational Study.

Daniel S Tawfik, Amrita Sinha, Mohsen Bayati, Kathryn C Adair, Tait D Shanafelt, J Bryan Sexton, Jochen Profit

Abstract readObservational Study
In one paragraph

Observational study in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 3 pooled it
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Daniel S TawfikDepartment of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, United States.ORCID 0000-0003-2901-2737
Amrita SinhaDepartment of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, United States.ORCID 0000-0001-6171-6932
Mohsen BayatiOperations, Information, and Technology, Stanford University Graduate School of Business, Stanford, CA, United States.ORCID 0000-0002-7280-912X
Kathryn C AdairDuke Center for Healthcare Safety and Quality, Duke University Health System, Durham, NC, United States.ORCID 0000-0003-4886-0002
Tait D ShanafeltDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, United States.ORCID 0000-0002-7106-5202
J Bryan SextonDuke Center for Healthcare Safety and Quality, Duke University Health System, Durham, NC, United States.ORCID 0000-0002-0578-2924
Jochen ProfitDepartment of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, United States.ORCID 0000-0002-3782-9248

Funding

The WISER StudyR01HD084679 · NICHD · STANFORD UNIVERSITY · PI PROFIT, JOCHEN, SEXTON, JOHN BRYAN · 2015 to 2018
$2.2M
Development and Validation of a Prediction Model to Address Physician BurnoutK08HS027837 · AHRQ · STANFORD UNIVERSITY · PI TAWFIK, DANIEL · 2020 to 2024
$762k
AHRQ HHS K08 HS027837NICHD NIH HHS R01 HD084679
6 · The paper itself

Abstract

backgroundNew technology adoption is common in health care, but it may elicit frustration if end users are not sufficiently considered in their design or trained in their use. These frustrations may contribute to burnout.

objectiveThis study aimed to evaluate and quantify health care workers' frustration with technology and its relationship with emotional exhaustion, after controlling for measures of work-life integration that may indicate excessive job demands.

methodsThis was a cross-sectional, observational study of health care workers across 31 Michigan hospitals. We used the Safety, Communication, Operational Reliability, and Engagement (SCORE) survey to measure work-life integration and emotional exhaustion among the survey respondents. We used mixed-effects hierarchical linear regression to evaluate the relationship among frustration with technology, other components of work-life integration, and emotional exhaustion, with adjustment for unit and health care worker characteristics.

resultsOf 15,505 respondents, 5065 (32.7%) reported that they experienced frustration with technology on at least 3-5 days per week. Frustration with technology was associated with higher scores for the composite Emotional Exhaustion scale (r=0.35, P<.001) and each individual item on the Emotional Exhaustion scale (r=0.29-0.36, P<.001 for all). Each 10-point increase in the frustration with technology score was associated with a 1.2-point increase (95% CI 1.1-1.4) in emotional exhaustion (both measured on 100-point scales), after adjustment for other work-life integration items and unit and health care worker characteristics.

conclusionsThis study found that frustration with technology and several other markers of work-life integration are independently associated with emotional exhaustion among health care workers. Frustration with technology is common but not ubiquitous among health care workers, and it is one of several work-life integration factors associated with emotional exhaustion. Minimizing frustration with health care technology may be an effective approach in reducing burnout among health care workers.

Indexed as

Burnout, ProfessionalFrustrationCross-Sectional StudiesHealth PersonnelHumansJob SatisfactionReproducibility of ResultsSurveys and QuestionnairesTechnologybiomedical technologyelectronic health recordsemotional exhaustionfrustration with technologyhealth information systemsmedical informatics applicationsprofessional burnoutuser-centered designwork-life balancework-life integration

Identifiers

PMID34255674
PMCPMC8292941

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.