Effectiveness of an Electronic Communication Tool on Transitions in Care From the Intensive Care Unit: Protocol for a Cluster-Specific Pre-Post Trial (Preprint)
Notice bibliographique
Résumé
BACKGROUND Transitions in care are vulnerable periods in health care that can expose patients to preventable errors due to incomplete or delayed communication between health care providers. Transitioning critically ill patients from intensive care units (ICUs) to other patient care units (PCUs) is particularly risky, due to the high acuity of the patients and the diversity of health care providers involved in their care. Instituting structured documentation to standardize written communication between health care providers during transitions has been identified as a promising means to reduce communication breakdowns. We developed an evidence-informed, computer-enabled, ICU-specific structured tool—an electronic transfer (e-transfer) tool—to facilitate and standardize the composition of written transfer summaries in the ICUs of one Canadian city. The tool consisted of 10 primary sections with a user interface combination of structured, automated, and free-text fields. OBJECTIVE Our overarching goal is to evaluate whether implementation of our e-transfer tool will improve the completeness and timeliness of transfer summaries and streamline communications between health care providers during high-risk transitions. METHODS This study is a cluster-specific pre-post trial, with randomized and staggered implementation of the e-transfer tool in four hospitals in Calgary, Alberta. Hospitals (ie, clusters) were allocated randomly to cross over every 2 months from control (ie, dictation only) to intervention (ie, e-transfer tool). Implementation at each site was facilitated with user education, point-of-care support, and audit and feedback. We will compare transfer summaries randomly sampled over 6 months postimplementation to summaries randomly sampled over 6 months preimplementation. The primary outcome will be a binary composite measure of the timeliness and completeness of transfer summaries. Secondary measures will include overall completeness, timeliness, and provider ratings of transfer summaries; hospital and ICU lengths of stay; and post-ICU patient outcomes, including ICU readmission, adverse events, cardiac arrest, rapid response team activation, and mortality. We will use descriptive statistics (ie, medians and means) to describe demographic characteristics. The primary outcome will be compared within each hospital pre- and postimplementation using separate logistic regression models for each hospital, with adjustment for patient characteristics. RESULTS Participating hospitals were cluster randomized to the intervention between July 2018 and January 2019. Preliminary extraction of ICU patient admission lists was completed in September 2019. We anticipate that evaluation data collection will be completed by early 2021, with first results ready for publication in spring or summer 2021. CONCLUSIONS This study will report the impact of implementing an evidence-informed, computer-enabled, ICU-specific structured transfer tool on communication and preventable medical errors among patients transferred from the ICU to other hospital care units. CLINICALTRIAL ClinicalTrials.gov NCT03590002; https://www.clinicaltrials.gov/ct2/show/NCT03590002 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/18675
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 | 0,030 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,002 |
| Méta-épidémiologie (sens large) | 0,008 | 0,007 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,043 | 0,008 |
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 ».