Linking Patient-Reported and Clinician-Assessed Wound Status via Chatbot-Based Digital Surveillance for Wound Infection: Retrospective Observational Study
Notice bibliographique
Résumé
BACKGROUND: Digital wound monitoring has become increasingly feasible with the widespread use of smartphones and mobile messaging platforms. Although most previous studies have focused on chronic wounds and demonstrated the clinical benefits of remote monitoring, little is known about how patients with acute wounds perceive and report wound-related changes after discharge; these factors may affect the accuracy and reliability of patient-facing digital health systems. OBJECTIVE: This study aimed to evaluate the diagnostic performance of patient-reported infection symptoms in predicting clinician-initiated callbacks within a chatbot-based wound monitoring platform. A secondary objective was to identify wound features most strongly associated with patient-reported infection and examine differences between acute and chronic wound populations. METHODS: This retrospective observational study was conducted at a tertiary medical center in Taipei, Taiwan, between June 30, 2022, and March 1, 2023, as part of an institutional digital health initiative. Within this program, adults with acute or chronic wounds voluntarily joined a chatbot-based monitoring system deployed through the Line messaging app using a bring-your-own-device model. Participants submitted daily symptom reports and wound photographs through the chatbot interface. For each submission, patient self-report of infection served as the primary predictor variable, while an independent review by a senior plastic surgeon determined the reference standard (callback vs no callback). Logistic regression and generalized estimating equation models were applied to account for within-subject correlation, with covariates including age, sex, and wound type. Analyses were performed separately for acute and chronic wounds. RESULTS: This study included 159 patients; 88 (55.3%) had acute wounds and 71 (44.7%) had chronic wounds. Across the study period, 4764 wound photographs were submitted, with a median of 5 (IQR 2-18) photographs per patient. Diagnostic performance differed by wound type. For acute wounds, the area under the receiver operating characteristic curve was 0.702, with 52.6% sensitivity (95% CI 31.7-72.7) and 87.8% specificity (95% CI 84.7-90.3). For chronic wounds, the area under the receiver operating characteristic curve was 0.907, with 94.9% sensitivity (95% CI 93.3-96.2) and 86.4% specificity (95% CI 85.2-87.5). In symptom correlation analyses, redness was significantly associated with patient-reported infection in the acute wound subgroup (odds ratio [OR] 3.94, 95% CI 1.97-7.90; P<.001), whereas in the chronic wound subgroup, both redness (OR 86.35, 95% CI 57.11-130.56; P<.001) and skin darkening (OR 358.55, 95% CI 244.79-525.16; P<.001) showed significant associations (all P<.001). CONCLUSIONS: This study highlights the differences in how patients perceive and report infection-related symptoms between populations with acute and chronic wounds. Lower diagnostic accuracy for acute wounds underscores the influence of limited experience and contextual constraints on patient self-assessment. These findings suggest that patient-facing digital wound monitoring systems should be tailored according to wound chronicity and patient experience, incorporating adaptive feedback and artificial intelligence-assisted screening to enhance patient-reported symptom interpretation.
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) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 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 ».