A Nurse-Led Care Delivery App and Telehealth System for Patients Requiring Wound Care: Mixed Methods Implementation and Evaluation Study
Notice bibliographique
Résumé
BACKGROUND: Innovative solutions to nursing care are needed to address nurse, health system, patient, and caregiver concerns related to nursing wellness, work flexibility and control, workforce retention and pipeline, and access to patient care. One innovative approach includes a novel health care delivery model enabling nurse-led, off-hours wound care (PocketRN) to triage emergent concerns and provide additional patient health education via telehealth. OBJECTIVE: This pilot study aimed to evaluate the implementation of PocketRN from the perspective of nurses and patients. METHODS: Patients and part-time or per-diem, wound care-certified and generalist nurses were recruited through the Stanford Medicine Advanced Wound Care Center in 2021 and 2022. Qualitative data included semistructured interviews with nurses and patients and clinical documentation review. Quantitative data included app use and brief end-of-interaction in-app satisfaction surveys. RESULTS: This pilot study suggests that an app-based nursing care delivery model is acceptable, clinically appropriate, and feasible. Low technology literacy had a modest effect on initial patient adoption; this barrier was addressed with built-in outreach and by simplifying the patient experience (eg, via phone instead of video calls). This approach was acceptable for users, despite total patient enrollment and use numbers being lower than anticipated (N=49; 17/49, 35% of patients used the app at least once beyond the orientation call). We interviewed 10 patients: 7 who had used the app were satisfied with it and reported that real-time advice after hours reduced anxiety, and 3 who had not used the app after enrollment reported having other resources for health care advice and noted their perception that this tool was meant for urgent issues, which did not occur for them. Interviewed nurses (n=10) appreciated working from home, and they reported comfort with the scope of practice and added quality of care facilitated by video capabilities; there was interest in additional wound care-specific training for nonspecialized nurses. Nurses were able to provide direct patient care over the web, including the few participating nurses who were unable to perform in-person care (n=2). CONCLUSIONS: This evaluation provides insights into the integration of technology into standard health care services, such as in-clinic wound care. Using in-system nurses with access to electronic medical records and specialized knowledge facilitated app integration and continuity of care. This care delivery model satisfied nurse desires for flexible and remote work and reduced patient anxiety, potentially reducing postoperative wound care complications. Feasibility was negatively impacted by patients' technology literacy and few language options; additional patient training, education, and language support are needed to support equitable access. Adoption was impacted by a lack of perceived need for additional care; lower-touch or higher-acuity settings with a longer wait between visits could be a better fit for this type of nurse-led care.
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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 | 0,033 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 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 ».