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Enregistrement W4386565035 · doi:10.1097/01.eem.0000696076.63401.b3

News

2020· article· en· W4386565035 sur OpenAlexaff
Joe Nemeth

Notice bibliographique

RevueEmergency Medicine News · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueIntensive Care Unit Cognitive Disorders
Établissements canadiensMcGill University Health CentreMontreal General Hospital
Organismes subventionnairesnon disponible
Mots-clésPolitical science

Résumé

récupéré en direct d'OpenAlex

FigureFigureThe daily struggle with efficiently managing information is not new for physicians. It is a well-accepted observation that excessive information (novel or established) can become a distraction and even negatively affect physician psychological well-being and consequently clinical efficiency and performance. (J Biosci Med. 2015;3[11]:124.) An important insight from behavioral theory is that careful consideration in information analysis and subsequent decision-making is mentally taxing, leading people to shy away from engaging in cognitively demanding reasoning when tired, which can clearly affect clinical work. Information overload can influence physicians to fall inappropriately into cognitive biases leading to clinical error, most commonly availability bias (believing that ideas that come readily to mind are more representative than is the case) and confirmation bias (focusing on information that reinforces preconceived notions at the expense of contradictory information). Information overload occurs when a person encounters a significant amount of seemingly relevant information needing to be processed, resulting in difficulty in distinguishing which data are reliable and helpful. This process can culminate in feelings of frustration and cause anxiety, fatigue, decision fatigue, and paralysis of action. Behavioral psychology studies in the 1970s revealed an inverted U-shaped relationship between information load and decision quality, which increases as information load increases and then it begins to decrease, especially under time pressure. Complex Definition Defining information overload, however, is much more complex than just being overburdened with a large amount of information. Many other terms have been used, such as information flood, information smog, information explosion, information glut, knowledge overload, information fatigue syndrome, data overload, and data explosion, but there is no universal definition. (Int J Res Engineer Tech. 2014;3[17]:27.) Health care providers, particularly physicians, can relate to feelings of exasperation from information overload, especially now during the COVID-19 era. Experience from the 2003 severe acute respiratory syndrome (SARS) outbreak and early reports of COVID-19 show that they experience considerable psychological distress for myriad reasons—providing direct patient care, vicarious psychological trauma, quarantine, and self-isolation. Physicians disproportionately bear the additional physical and psychological burdens associated with pandemics. (JAMA Netw Open. 2020;3[3]:e203976; JAMA Netw Open. 2020;3[6]:e2010705.) This distress is likely potentiated by the frequent and significant amount of information and misinformation on the internet and social media. Email Barometer My daily email load during the pandemic underscores the scope of the phenomenon. This is purely anecdotal and may under- or overestimate the scope of the information overload in relation to the average practicing physician. It is possible that my conclusions underestimate the scope of the information overload because only emails were analyzed, no social media were used, and the emails were not equal in their complexity. Overestimation may have occurred because of my specialty, practice in a university academic hospital, and leadership position in education. Before COVID-19, I received an average of 20 emails a day. For the six weeks after the pandemic started, from March 25 to May 6, I received an extra 15 emails related to COVID-19 every day. Fifty percent of those emails were departmental, 30 percent were institutional, 15 percent were academic and related to research and teaching, and five percent were personal. Seventy percent of the emails had similar or repetitive content, and 15 percent had contradictory content. Thirty-five percent of that content was related to logistics, 30 percent was general information, 25 percent was about diagnostic and therapeutic processes, and 10 percent was related to education and questionnaires (five percent each). Ninety-five percent of the emails were complicated with multiple concepts or ideas, and five percent were simple. Half of the emails required action in less than 48 hours, and I spent an hour each day reading them. An analysis of the raw data highlights the burden of information overload and how it clearly adds to an already full intrinsic cognitive load—the cognitive weight of the information determined by the complexity of the material being processed. A few striking trends with potential detrimental consequences to cognitive load and time management were obvious: the 75 percent increase in the volume of emails, the significant overlap of similar and repetitive information, and the significant proportion of the information that contained at least one contradictory statement. Quality and Conclusions Much of the COVID-19 medical literature pushed was of poor quality and made few tangible conclusions and links between evidence and recommendations, which was not dissimilar to the findings of at least one study during this time. (BMJ. 2020;369:m1936.) The overwhelming majority of the information was complex. Having so much information for decision-making can result in the frustrating paradox that there is actually a lack of information within that flood of information. Many industries other than health care are trying to mitigate the effects of information overload (accounting, organization science, marketing and management information systems), and we should emulate these efforts. Human-computer interaction and artificial intelligence hold promise. Using the concepts of data display and information visualization, data can be transformed into a visual form to make use of humans' natural visual capabilities. (Hospitalist. 2006;2006[3]; https://bit.ly/306HvjT.) Machine learning natural language processing might help pull relevant information and provide alerts to action from sources to physicians. Another major source of cognitive load is sifting through myriad sources of information, many with similar and contradictory content. Prioritizing, delegating, and significantly limiting outsourcing privilege would ease the information overload burden. (Otolaryngol Head Neck Surg. 2020;163[1]:60.) Self-reflection on many levels may also help. Figure out your learning style; there are many ways to access information. Knowing how one likes to consume information is vital (reading, listening, visually) and will help with retention. Prioritize your sources by seeking out curated content from limited places, and optimize the timing of information processing. Not only is the time of day important when processing information (decision fatigue is more likely later in the day) but so is individualizing the quantity of information processed in one sitting. The medical information explosion is here to stay and can overwhelm physicians. We need to explore and identify individual processing of information and use technology developed in different industries to manage information overload.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,073
Score d'incertitude au seuil0,000

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,003
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0020,000
Communication savante0,0060,004
Science ouverte0,0010,002
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,9270,848

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,057
Tête enseignante GPT0,331
Écart entre enseignants0,274 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2020
Routes d'admission1
Résumé présentoui

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