Digital Outpatient Services for Adults: Development of an Intervention and Protocol for a Multicenter Non–Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Health care services are being challenged by an increasing number of patients and limited resources. Hence, research investigating options to reduce costs and increase effectiveness is warranted. Digital outpatient services can provide flexible and tailored follow-up, improve patients' health literacy, and facilitate the identification of adverse courses of disease. However, previous research largely focused on disease-specific contexts and outcomes. Therefore, research on digital services investigating generic outcomes such as health literacy is warranted. OBJECTIVE: This article aims to describe the "digital outpatient service" intervention and present the protocol for an ongoing multicenter, nonrandomized trial evaluating this intervention. METHODS: Based on previous experiences and evidence-based knowledge, we developed this intervention through patient-journey maps in collaboration with each clinical specialty. The patients gain access to a mobile app for self-monitoring and patient-reported outcomes and a chat for contact between the patients and health care workers. The health care workers' dashboard includes a traffic light system to draw attention to the most urgent patient reports. In this multicenter, non-randomized controlled trial, patients are allocated to the control group receiving standard care or the 6-month intervention. Eligible patients are aged 18 years or older who receive outpatient care at the neurology, lung, pain, or cancer departments at 2 university hospitals in Norway. Our evaluation will include patient-reported outcomes, qualitative interviews, and clinical measures. The primary outcome will be health literacy using the Health Literacy Questionnaire. A sample size of 165 participants is split into a 1:2 ratio in favor of the intervention. We will analyze quantitative data in SPSS (IBM Corp) using descriptive statistics and logistic regression, and qualitative data using thematic analysis. RESULTS: This trial started in September 2021, and the intervention started in January 2022. Recruitment has ended, with 55 patients in the control group and 107 patients in the intervention group. Follow-up is expected to end in July 2023, with results expected to be obtained in December 2023. CONCLUSIONS: This study will evaluate an intervention facilitated by an already certified digital multicomponent solution, with intervention content based on patient-reported outcomes, health literacy, and self-monitoring. The intervention is specifically tailored to each participating center and the needs of their patients using patient journey maps. The comprehensive and generic evaluation of this digital outpatient service intervention is a strength as it targets a heterogeneous sample of patients. Thus, this study will provide important knowledge about the applicability and effects of digital health care services. As a result, patients and health care workers will gain a new, evidence-based understanding of whether and how digital tools may be used in clinical care. TRIAL REGISTRATION: ClinicalTrials.gov NCT05068869; https://clinicaltrials.gov/ct2/show/NCT05068869. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46649.
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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 | 0,079 | 0,074 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,003 |
| Méta-épidémiologie (sens large) | 0,011 | 0,008 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,009 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,044 | 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 ».