Prompted Text-Based Vital Sign Recording Versus Unprompted Electronic Medical Record Entries in Patients With Advanced Heart Failure: Observational Study (Preprint)
Notice bibliographique
Résumé
Abstract Background Long-term remote patient monitoring of weight, pulse, and blood pressure has been shown to significantly reduce mortality and hospitalization rates among patients with heart failure. Despite its proven effectiveness, maintaining patient engagement in remote monitoring programs remains challenging. Objective This study aimed to evaluate the impact of 2-way text-based communication on prompting patients to record key vital signs and to compare it with unprompted patient reporting through electronic medical records in terms of engagement and clinical outcomes. Methods We analyzed data from patients participating in the University of Michigan Advanced Heart Failure Program who reported daily weight, blood pressure, and pulse using either the MiChart Patient Outreach Texting Application (MPOTA) or patient enrolled flowsheets (PEFs). The study’s primary metric was the consistency of patient-reported vital signs, with secondary descriptive metrics including variations in hospitalization and emergency room visits pre-enrollment and postenrollment in the programs. Results A total of 890 patients were included, with 301 enrolled in the MPOTA group and 589 in the PEF group. The engagement rate for the PEF group had a median of 2.29% (IQR 0%‐23.93%). In contrast, the MPOTA group showed a significantly higher median engagement rate of 66.67% (IQR 30.67%‐88.24%). There were no significant differences in hospitalization or emergency room visit rates across engagement categories (none, low, medium, and high) or between programs. Mean hospitalizations declined by 21% in the MPOTA group (from mean 0.53, SD 0.90 to mean 0.42, SD 0.88; P =.06) and 18% in the PEF group (from mean 0.50, SD 0.86 to mean 0.41, SD 0.80; P =.03) after the initiation of each program. This reduction was small and statistically significant only for the PEF group. Mean emergency room visits did not significantly change in either group. Regression analyses showed no significant association between engagement level and hospitalization or emergency room utilization, although medium engagement was associated with a nonsignificant trend toward fewer events. Despite improved engagement with MPOTA, this did not translate into significant reductions in hospitalizations or emergency room visits. All analyses of clinical outcomes were exploratory and underpowered, and no significant associations were found between engagement level and utilization. Conclusions Although MPOTA was associated with substantially higher patient engagement levels compared to unprompted patient reporting, neither demonstrated significant differences in hospitalization or emergency room visits across engagement levels or between programs. Small reductions in hospitalizations were observed, but these were significant only in PEF, not in MPOTA. Observed changes in utilization were exploratory, small in magnitude, and underpowered to detect clinically meaningful effects. These findings suggest that mobile text-based communication may be a useful tool for improving engagement in remote monitoring programs for patients with advanced heart failure; however, further research is needed to assess its impact on clinical outcomes.
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 | 0,003 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».