Point‐of‐care Cognitive Support Technology in Emergency Departments: A Scoping Review of Technology Acceptance by Clinicians
Notice bibliographique
Résumé
OBJECTIVE: Cognitive support technologies that support clinical decisions and practices in the emergency department (ED) have the potential to optimize patient care. However, limited uptake by clinicians can prevent successful implementation. A better understanding of acceptance of these technologies from the clinician perspective is needed. We conducted a scoping review to synthesize diverse, emerging evidence on clinicians' acceptance of point-of-care (POC) cognitive support technology in the ED. METHOD: We systematically searched 10 electronic databases and gray literature published from January 2006 to December 2016. Studies of any design assessing an ED-based POC cognitive support technology were considered eligible for inclusion. Studies were required to report outcome data for technology acceptance. Two reviewers independently screened studies for relevance and quality. Study quality was assessed using the Mixed-Methods Appraisal Tool. A descriptive analysis of the features of POC cognitive support technology for each study is presented, illustrating trends in technology development and evaluation. A thematic analysis of clinician, technical, patient, and organizational factors associated with technology acceptance is also presented. RESULTS: Of the 1,563 references screened for eligibility, 24 met the inclusion criteria and were included in the review. Most studies were published from 2011 onward (88%), scored high for methodologic quality (79%), and examined POC technologies that were novel and newly introduced into the study setting (63%). Physician use of POC technology was the most commonly studied (67%). Technology acceptance was frequently conceptualized and measured by factors related to clinician attitudes and beliefs. Experience with the technology, intention to use, and actual use were also more common outcome measures of technology acceptance. Across studies, perceived usefulness was the most noteworthy factor impacting technology acceptance, and clinicians generally had positive perceptions of the use of POC cognitive support technology in the ED. However, the actual use of POC cognitive support technology reported by clinicians was low-use, by proportion of patient cases, ranged from 30% to 59%. Of the 24 studies, only two studies investigated acceptance of POC cognitive support technology currently implemented in the ED, offering "real-world" clinical practice data. All other studies focused on acceptance of novel technologies. Technical aspects such as an unfriendly user interface, presentation of redundant or ambiguous information, and required user effort had a negative impact on acceptance. Patient expectations were also found to have a negative impact, while patient safety implications had a positive impact. Institutional support was also reported to impact technology acceptance. CONCLUSIONS: Findings from this scoping review suggest that while ED clinicians acknowledge the utility and value of using POC cognitive support technology, actual use of such technology can be low. Further, few studies have evaluated the acceptance and use of POC technologies in routine care. Prospective studies that evaluate how ED clinicians appraise and consider POC technology use in clinical practice are now needed with diverse clinician samples. While this review identified multiple factors contributing to technology acceptance, determining how clinician, technical, patient, and organizational factors mediate or moderate acceptance should also be a priority.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,044 | 0,191 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,006 | 0,007 |
| Bibliométrie | 0,025 | 0,022 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».