Challenges With Continuous Pulse Oximetry Monitoring and Wireless Clinician Notification Systems After Surgery: Reactive Analysis of a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Research has shown that introducing electronic Health (eHealth) patient monitoring interventions can improve healthcare efficiency and clinical outcomes. The VIGILANCE (VItal siGns monItoring with continuous puLse oximetry And wireless cliNiCian notification aftEr surgery) study was a randomized controlled trial (n=2049) designed to assess the impact of continuous vital sign monitoring with alerts sent to nursing staff when respiratory resuscitations with naloxone, code blues, and intensive care unit transfers occurred in a cohort of postsurgical patients in a ward setting. This report identifies and evaluates key issues and challenges associated with introducing wireless monitoring systems into complex hospital infrastructure during the VIGILANCE eHealth intervention implementation. Potential solutions and suggestions for future implementation research are presented. OBJECTIVE: The goals of this study were to: (1) identify issues related to the deployment of the eHealth intervention system of the VIGILANCE study; and (2) evaluate the influence of these issues on intervention adoption. METHODS: During the VIGILANCE study, issues affecting the implementation of the eHealth intervention were documented on case report forms, alarm event forms, and a nursing user feedback questionnaire. These data were collated by the research and nursing personnel and submitted to the research coordinator. In this evaluation report, the clinical adoption framework was used as a guide to organize the identified issues and evaluate their impact. RESULTS: Using the clinical adoption framework, we identified issues within the framework dimensions of people, organization, and implementation at the meso level, as well as standards and funding issues at the macro level. Key issues included: nursing workflow changes with blank alarm forms (24/1030, 2.33%) and missing alarm forms (236/1030, 22.91%), patient withdrawal (110/1030, 10.68%), wireless network connectivity, false alarms (318/1030, 30.87%), monitor malfunction (36/1030, 3.49%), probe issues (16/1030, 1.55%), and wireless network standards. At the micro level, these issues affected the quality of the service in terms of support provided, the quality of the information yielded by the monitors, and the functionality, reliability, and performance of the monitoring system. As a result, these issues impacted access through the decreased ability of nurses to make complete use of the monitors, impacted care quality of the trial intervention through decreased effectiveness, and impacted productivity through interference in the coordination of care, thus decreasing clinical adoption of the monitoring system. CONCLUSIONS: Patient monitoring with eHealth technology in surgical wards has the potential to improve patient outcomes. However, proper planning that includes engagement of front-line nurses, installation of appropriate wireless network infrastructure, and use of comfortable cableless devices is required to maximize the potential of eHealth monitoring. TRIAL REGISTRATION: ClinicalTrials.gov NCT02907255; https://clinicaltrials.gov/ct2/show/NCT02907255.
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 | 0,182 | 0,246 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,020 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».