Patient Perspectives on Digital Technology and Experiences of Computerized History-Taking for Chest Pain Management in the Emergency Department: CLEOS-CPDS Prospective Cohort Study
Notice bibliographique
Résumé
Background: Automated, self-reported medical history-taking has the potential to provide comprehensive patient-reported data across a wide range of clinical issues. In the Clinical Expert Operating System-Chest Pain Danderyd Study (CLEOS-CPDS), medical history data were entered by patients using tablets in an emergency department (ED). Since successful implementation of this technology depends on understanding patients' views and willingness to use it, we have studied these factors following patients' use of the CLEOS program. Objective: This study aimed to develop and use a questionnaire to investigate patients' attitudes, perceptions and skills related to using digital technology in health care in general, and specifically their experiences with the CLEOS program during their visit to an ED with a chief complaint of chest pain. Methods: The study included the development of a questionnaire, followed by a cross-sectional study. Questionnaire design and the technology acceptance model underpinned the development of the questionnaire. The think-aloud method was used to test the questionnaire. Adults who participated in the CLEOS-CPDS were invited consecutively to respond to the questionnaire. Descriptive and correlational analyses were performed. Results: The refinement of the questionnaire included language revision, removal of similar items, and replacement of some response formats. The final questionnaire consisted of 16 items and one free text comment that assessed attitudes, perceptions, and skills related to the use of digital technology in health care in general and the specific experience of using self-reported history-taking by CLEOS. The majority of the 129 patients (mean age 56, SD=17.3 y) who answered the questionnaire found it easy to use digital technology in general (118/129, 91%), that digital technology has a role when seeking health care (115/129, 91%), and that patient-reported symptoms are helpful in making a diagnosis (83/129, 65%). There were some concerns that the patient-physician interaction would be disrupted when using digital technology (48/129, 38%). The overall experience of using CLEOS was positive and most felt confident in answering the questions on a tablet (118/129, 91%). Older age was associated with less ease (P<.001), confidence (P<.001), and trust (P=.002) when using digital technology, as well as less confidence in answering the questions in CLEOS (P=.019). Moreover, older age was associated with more worry about the potential disruption of the patient-physician personal contact when using digital technology (P<.001). Conclusions: This study suggests strong approval of usefulness and trust in digital technology among patients with chest pain visiting a cardiology ED, but the concern for lack of personal contact should be acknowledged. End users found the CLEOS program to perform well but recommend some adjustments for future studies. The questionnaire responses provided some new insights on perceived usability of digital technology for health care delivery, and it appears relevant for future evaluations of CLEOS in other contexts.
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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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| 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 ».