Feasibility of a Web-Based and Mobile-Supported Follow-Up Treatment Pathway for Adult Patients With Orthopedic Trauma in the Netherlands: Concurrent Mixed Methods Study
Notice bibliographique
Résumé
Background: Orthopedic trauma care encounters challenges in follow-up treatment due to limited patient information provision, treatment variation, and the chaotic settings in which it is provided. Additionally, pressure on health care resources is rising worldwide. In response, digital follow-up treatment pathways were implemented for patients with orthopedic trauma, aiming to optimize health care resource use and enhance patient experiences. Objective: We aim to assess digital follow-up treatment pathway feasibility from the patient's perspective and its impact on health care resource use. Methods: A concurrent mixed methods study was conducted parallel to implementation of digital follow-up treatment pathways in an urban level-2 trauma center. Inclusion criteria were (1) minimum age of 18 years, (2) an active web-based patient portal account, (3) ability to read and write in Dutch, and (4) no cognitive or preexisting motor impairment. Data were collected via electronic patient records, and surveys at three time points: day 1-3, 4-6 weeks, and 10-12 weeks after an initial emergency department visit. Semistructured interviews were performed at 10-12 weeks post injury. Anonymous data from a pre-existing database were used to compare health care resource use between the digital treatment pathways and traditional treatment. Quantitative data were reported descriptively. A thematic analysis was used for qualitative data. All outcomes were categorized according to the Bowen feasibility parameters: acceptability, demand, implementation, integration, and limited efficacy. Results: Sixty-six patients were included for quantitative data collection. Survey response rates were 100% (66/66) at day 1-3, 92% (61/66) at 4-6 weeks, and 79% (52/66) at 10-12 weeks. For qualitative data collection, 15 semistructured interviews were performed. Patients reported median satisfaction scores of 7 (IQR 6-8) with digital treatment pathways and 8 (IQR 7-9) for overall treatment, reflecting positive experiences regarding functionality, actual and intended use, and treatment safety. Digital treatment pathways reduced secondary health care use, with fewer follow-up appointments by phone (median 0, IQR 0-0) versus the control group (median 1, IQR 0-1; P<.001). Consequently, fewer physicians were involved in follow-up treatment for the intervention group (median 2, IQR 1-2) than for the control group (median 2, IQR 1-3; P<.001). Fewer radiographs were performed for the intervention group (median 1, IQR 0-1) than for the control group (P=.01). Qualitative data highlighted positive experiences with functionalities, intended use, and safety, but also identified areas for improvement, including managing patient expectations, platform usability, and protocol adherence. Conclusions: Use of digital follow-up treatment pathways is feasible, yielding satisfactory patient experiences and reducing health care resource use. Recommendations for improvement include early stakeholder involvement, integration of specialized digital tools within electronic health record systems, and hands-on training for health care professionals. These insights can guide clinicians and policy makers in effectively integrating similar tools into clinical practice.
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 | 0,025 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».