Health Care Professionals’ Experiences With a Mobile Self-Care Solution for Low Complex Orthopedic Injuries: Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: To cope with the rising number of patients with trauma in an already constrained Dutch health care system, Direct Discharge (DD) has been introduced in over 25 hospitals in the Netherlands since 2019. With DD, no routine follow-up appointments are scheduled after the emergency department (ED) visit, and patients are supported through information leaflets, a smartphone app, and a telephone helpline. DD reduces secondary health care use, with comparable patient satisfaction and primary health care use. Currently, little is known about the experiences of in-hospital health care professionals with DD. OBJECTIVE: The aim of this study was to explore the experiences of health care professionals with the DD protocol to enhance durable adoption and improve the protocol. METHODS: We conducted a mixed methods study parallel to the implementation of DD in 3 hospitals. Data were collected through a preimplementation survey, a postimplementation survey, and semistructured interviews. Quantitative data were reported descriptively, and qualitative data were reported using thematic analysis. Outcomes included the Bowen feasibility parameters: implementation, acceptability, preliminary efficacy, demand, and applicability. Preimplementation expectations were compared with postimplementation experiences. Health care professionals involved in the daily clinical care of patients with low-complex, stable injuries were eligible for this study. RESULTS: Of the 217 eligible health care professionals, 128 started the primary survey, 37 completed both surveys (response rate of 17%), and 15 participated in semistructured interviews. Health care professionals expressed satisfaction with the DD protocol (median 7.8, IQR 6.8-8.9) on a 10-point scale, with 82% (30/37) of participants noting improved information quality and uniformity and 73% (27/37) of patients perceiving reduced outpatient follow-up and imaging. DD was perceived as safe by 79% (28/37) of participants in its current form, but a feedback system to reassure health care professionals that patients had recovered adequately was suggested to improve DD. The introduction of DD had varying effects on workload and job satisfaction among different occupations. Health care professionals expressed intentions to continue using DD due to increased efficiency, patient empowerment, and self-management. CONCLUSIONS: Health care professionals perceive DD as an acceptable, applicable, safe, and efficacious alternative to traditional treatment. A numerical in-app feedback system (eg, in-app communication tools or recovery scores) could alleviate health care professionals' concerns about adequate recovery and further improve DD protocols. DD can reduce health care use, which is important in times of constrained resources. Nonetheless, both advantages and disadvantages should be considered while evaluating this type of treatment. In the future, clinicians and policy makers can use these insights to further optimize and implement DD in clinical practice and guidelines.
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 | 0,017 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».