Applying Human Factors Principles to Mitigate Usability Issues Related to Embedded Assumptions in Health Information Technology Design
Notice bibliographique
Résumé
BACKGROUND: There is growing recognition that design flaws in health information technology (HIT) lead to increased cognitive work, impact workflows, and produce other undesirable user experiences that contribute to usability issues and, in some cases, patient harm. These usability issues may in turn contribute to HIT utilization disparities and patient safety concerns, particularly among "non-typical" HIT users and their health care providers. Health care disparities are associated with poor health outcomes, premature death, and increased health care costs. HIT has the potential to reduce these disparate outcomes. In the computer science field, it has long been recognized that embedded cultural assumptions can reduce the usability, usefulness, and safety of HIT systems for populations whose characteristics differ from "stereotypical" users. Among these non-typical users, inappropriate embedded design assumptions may contribute to health care disparities. It is unclear how to address potentially inappropriate embedded HIT design assumptions once detected. OBJECTIVE: The objective of this paper is to explain HIT universal design principles derived from the human factors engineering literature that can help to overcome potential usability and/or patient safety issues that are associated with unrecognized, embedded assumptions about cultural groups when designing HIT systems. METHODS: Existing best practices, guidance, and standards in software usability and accessibility were subjected to a 5-step expert review process to identify and summarize those best practices, guidance, and standards that could help identify and/or address embedded design assumptions in HIT that could negatively impact patient safety, particularly for non-majority HIT user populations. An iterative consensus-based process was then used to derive evidence-based design principles from the data to address potentially inappropriate embedded cultural assumptions. RESULTS: Design principles that may help identify and address embedded HIT design assumptions are available in the existing literature. CONCLUSIONS: Evidence-based HIT design principles derived from existing human factors and informatics literature can help HIT developers identify and address embedded cultural assumptions that may underlie HIT-associated usability and patient safety concerns as well as health care disparities.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
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 tête enseignante, 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 ».