Acceptability of Guided Symptom Entry and Asynchronous Clinical Communication Software Among Primary Care Staff: Qualitative Study
Notice bibliographique
Résumé
Background: Patients often communicate with primary care centers remotely (eg, by telephone or email) before seeking in-person care. A comparatively novel addition might be patient-facing symptom entry websites, where subsequent questions are automatically guided by previous responses. However, the acceptability of such systems to health care staff remains unclear, particularly in terms of what features staff perceive as useful. Objective: This study aimed to investigate a patient-facing algorithm-guided symptom-entry software (developed by Certific OÜ, Estonia), which also supports subsequent asynchronous communication, for its acceptability and perceived utility to primary health care providers. Methods: In-depth and open-ended interviews were conducted in 8 primary care centers in Estonia, including 8 nurses and 6 doctors, 3-6 months after the implementation of a novel patient-facing website. Transcripts were coded inductively, using grounded theory and phenomenological approaches to uncover themes most salient to providers. Two family doctors provided feedback on the final analysis. Results: Staff perceived unstructured communication (via email and phone calls) as a burden that increased their cognitive load. Sometimes, this arises out of the perceived mismatch between needing to identify and document critical symptom information and being unable to standardize the supply of such information, due to a heterogeneous and unpredictable communication processes whose duration, quality, and risk of miscommunication are hard to predict and control. All interviewees expressed the desire that more patients initiate their remote query via the algorithm-guided symptom-entry software. The software was reported to satisfy perceived feature needs for patient verification, privacy and data security, editable plain-language symptom summaries of symptoms, and integration with prewritten response templates (particularly for staff who were nonnative speakers). Safety of the new software was perceived as high, on account of integration alongside traditional telephone requests. Staff reported the challenge that great effort was needed to persuade patients to use the website. Among perceived challenges, some providers reported difficulty in onboarding patients, digital literacy gaps, and limited time savings. While previous research has criticized poorly designed multiple-choice systems, our findings suggest that an appropriately designed and personalized multiple-choice system can be preferable to health care staff, as they may lower cognitive demands and enhance well-being. Conclusions: Interviewed primary health care staff felt that this symptom entry software was acceptable and desirable. They valued a perceived reduction in cognitive demands. This holds promise for increasing staff well-being and increasing efficiency, which needs to be quantified in future studies.
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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 | 0,016 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».