Continuous User Experience Monitoring of a Patient-Completed Preoperative Assessment System in the United Kingdom: Cross-sectional Study
Notice bibliographique
Résumé
BACKGROUND: Anesthetic preoperative assessment (POA) is now a common part of the surgical care pathway, and guidelines support its routine use. MyPreOp (Ultramed Ltd) is a web-based POA system that enables remote assessments. Usability is a key factor in the success of digital health solutions. OBJECTIVE: This study aims to assess the usability of the MyPreOp system through patient feedback, investigate the amount of time it took for patients to complete the POA questionnaire and the factors that influenced completion time, and explore the effect on completion times of implementing a validated eHealth usability scale, as compared to using a simple but unvalidated usability evaluation scale, and to test the feasibility of administering a more detailed usability evaluation scale in a staggered manner so as not to unduly increase completion times. METHODS: In this cross-sectional study, anonymized data sets were extracted from the MyPreOp system. The participants were adults (aged ≥18 years), scheduled for nonurgent surgical procedures performed in hospitals in the United Kingdom, who gave consent for their anonymized data to be analyzed. Data collected included age, gender, American Society of Anesthesiology (ASA) physical classification status, and completion time. Two user experience evaluations were used: in Phase 1, 2 questions asking about overall experience and ease of use, and in Phase 2, a previously validated usability questionnaire, with its 20 questions equally distributed among 5 succeeding patient cohorts. There were 2593 respondents in total (Phase 1: n=1193; Phase 2: n=1400). The median age of the participants was 46 years, and 1520 (58.62%) of the 2593 respondents were female. End points measured were the median completion times in Phase I and Phase II. The data were collected by extracting a subset of records from the database and exported to a spreadsheet for analysis (Excel, Microsoft Corporation). The data were analyzed for differences in completion times between Phase I and Phase II, as well as for differences between age groups, genders, and ASA classifications. RESULTS: MyPreOp scored well in usability in both phases. In Phase 1, 81.64% (974/1193) of respondents had a good or better experience, and 93.8% (1119/1193) found it easy to use. The usability rating in Phase 2 was 4.13 out of a maximum of 5, indicating high usability. The median completion time was 40.4 minutes. The implementation of the longer usability evaluation scale in Phase 2 did not negatively impact the completion times. Age and ASA physical status were found to be moderately associated with increased completion times. CONCLUSIONS: MyPreOp rates high in both user experience and usability. The method of dividing the questionnaire into 5 blocks is valid and does not negatively affect completion times. Further research into the factors affecting completion time is recommended.
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 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 | 0,003 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».