Impact of Electronic Health Record Use on Cognitive Load and Burnout Among Clinicians: Narrative Review.

burnout clinician cognitive load electronic health record technology

Journal

JMIR medical informatics
ISSN: 2291-9694
Titre abrégé: JMIR Med Inform
Pays: Canada
ID NLM: 101645109

Informations de publication

Date de publication:
12 Apr 2024
Historique:
received: 14 12 2023
accepted: 11 03 2024
revised: 15 02 2024
medline: 12 4 2024
pubmed: 12 4 2024
entrez: 12 4 2024
Statut: epublish

Résumé

The cognitive load theory suggests that completing a task relies on the interplay between sensory input, working memory, and long-term memory. Cognitive overload occurs when the working memory's limited capacity is exceeded due to excessive information processing. In health care, clinicians face increasing cognitive load as the complexity of patient care has risen, leading to potential burnout. Electronic health records (EHRs) have become a common feature in modern health care, offering improved access to data and the ability to provide better patient care. They have been added to the electronic ecosystem alongside emails and other resources, such as guidelines and literature searches. Concerns have arisen in recent years that despite many benefits, the use of EHRs may lead to cognitive overload, which can impact the performance and well-being of clinicians. We aimed to review the impact of EHR use on cognitive load and how it correlates with physician burnout. Additionally, we wanted to identify potential strategies recommended in the literature that could be implemented to decrease the cognitive burden associated with the use of EHRs, with the goal of reducing clinician burnout. Using a comprehensive literature review on the topic, we have explored the link between EHR use, cognitive load, and burnout among health care professionals. We have also noted key factors that can help reduce EHR-related cognitive load, which may help reduce clinician burnout. The research findings suggest that inadequate efforts to present large amounts of clinical data to users in a manner that allows the user to control the cognitive burden in the EHR and the complexity of the user interfaces, thus adding more "work" to tasks, can lead to cognitive overload and burnout; this calls for strategies to mitigate these effects. Several factors, such as the presentation of information in the EHR, the specialty, the health care setting, and the time spent completing documentation and navigating systems, can contribute to this excess cognitive load and result in burnout. Potential strategies to mitigate this might include improving user interfaces, streamlining information, and reducing documentation burden requirements for clinicians. New technologies may facilitate these strategies. The review highlights the importance of addressing cognitive overload as one of the unintended consequences of EHR adoption and potential strategies for mitigation, identifying gaps in the current literature that require further exploration.

Identifiants

pubmed: 38607672
pii: v12i1e55499
doi: 10.2196/55499
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e55499

Informations de copyright

©Elham Asgari, Japsimar Kaur, Gani Nuredini, Jasmine Balloch, Andrew M Taylor, Neil Sebire, Robert Robinson, Catherine Peters, Shankar Sridharan, Dominic Pimenta. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 12.04.2024.

Auteurs

Elham Asgari (E)

Guy's and St Thomas' NHS Trust, London, United Kingdom.
Tortus AI, London, United Kingdom.

Japsimar Kaur (J)

Manchester University NHS Foundation Trust, Manchester, United Kingdom.

Gani Nuredini (G)

Barts Health NHS Trust, London, United Kingdom.

Jasmine Balloch (J)

Tortus AI, London, United Kingdom.

Andrew M Taylor (AM)

Great Ormond Street Hospital, London, United Kingdom.

Neil Sebire (N)

Great Ormond Street Hospital, London, United Kingdom.

Robert Robinson (R)

Great Ormond Street Hospital, London, United Kingdom.

Catherine Peters (C)

Great Ormond Street Hospital, London, United Kingdom.

Shankar Sridharan (S)

Great Ormond Street Hospital, London, United Kingdom.

Dominic Pimenta (D)

Tortus AI, London, United Kingdom.

Classifications MeSH