Adapting A Unified Electronic Health Record Usability Framework for Evaluation of Connected Health Care Technologies Linking Mobile Data
Notice bibliographique
Résumé
Background: Evidence-based, objective, and systematic usability evaluation is key to successful connected health care technologies.  The increases in patient-facing mobile health technologies not only offer convenience for patients in managing their own health and/or chronic conditions, but also offer the opportunity for health care providers to access patient behaviors and patient-centered outcomes at ease. Thus, it is of significance to link and present patient-facing mobile device data to their health care providers in a secure and uninterrupted way that will facilitate workflow and promote patient provider communication, rather than drawing providers away from patients. This prompts increasing efforts developing connected health care technologies linking mobile data to electronic health record systems guided by user-centered design and redesign principles. However, lack of scientific, objective, and systematic usability evaluation put connected health care technologies at risk for low adoption and eventual failure. Objective: Learning from lessons in electronic health record usability evaluation, we propose to adapt an existing unified framework, TURF, for electronic health record usability evaluation to guide the design, redesign, and usability evaluation of connected health care technologies linking mobile data to electronic health record systems or other provider-facing Web-based evaluation tools. Methods: TURF, a unified framework of electronic health record usability, involves three dimensions: useful, usable, and satisfying; and four key components: task, user, representation, and function. Each dimension and component is described with theoretical underpinnings along with examples of how usability can be measured. Results: Specific adaptation of TURF that’s unique for connected health technologies include (1) user analysis for “satisfying” dimension will need to include both users using and mobile health users who’s feeding data into the system; (2) function analysis for “useful” dimension will need to consider functions/data wanted by the providers, functions actually used in real activities, functions/data available from mobile devices and with agreement from patients, functions/data available in interfaces within connected health care technologies ; (3) representation analysis for “usable” dimension need to consider correct representation of data from mobile devices in connected interface; (4) task analysis for “usable” dimension will highlight learnability, efficiency (time on task, steps on task, task success, mental effort), and error prevention and recovery (occurrence rate, error recovery rate). Real world interruptions, team dynamics, and multitasking should also be considered during evaluation of connected health care technologies. Conclusions: An adapted framework is proposed to offer objective, evidence-based, and systematic usability evaluation to guide the design and redesign of interfaces connecting mobile data with electronic health record systems and Web-based evaluation tools.
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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 | 0,013 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 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 ».