Remote Symptom Monitoring Using Patient-Reported Outcomes in Patients With Chronic Kidney Disease: Process Evaluation of a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: In Denmark, outpatient follow-up for patients with chronic kidney disease (CKD) is changing from in-hospital visits toward more remote health care delivery. The nonuse of remote patient-reported outcomes (PROs) is a well-known challenge, and it can be difficult to explain which mechanisms of interventions influence the outcome. Process evaluation may, therefore, be used to answer important questions on how and why interventions work, aiming to enhance the implications for clinical practice. OBJECTIVE: This study aimed to provide insight into the intervention process by evaluating (1) the representativity of the study population, (2) patient and physician use patterns, (3) patient adherence to the intervention, and (4) clinical engagement. METHODS: A process evaluation determining the reach, dose, fidelity, and clinical engagement was carried out, alongside a multicenter randomized controlled trial (RCT). We developed and implemented an intervention using PRO measures to monitor outpatients remotely. Data were collected for the PRO intervention arms in the RCT from 4 sources: (1) PRO data from the participants to determine personal factors, (2) the web-based PRO system to identify key usage intervention patterns, (3) medical records to identify clinical factors relating to the use of the intervention, and (4) semistructured interviews conducted with involved physicians. RESULTS: Of the 320 patients invited, 152 (47.5%) accepted to participate. The study population reflected the target population. The mean adherence rate to the PRO intervention arms was 82% (95% CI 76-87). The questionnaire response rate was 539/544 (99.1%). A minority of 13 (12.9%) of 101 patients needed assistance to complete study procedures. Physicians assessed 477/539 (88.5%) of the questionnaires. Contact was established in 417/539 (77.4%) of the cases, and 122/539 (22.6%) of the patients did not have contact. Physicians initiated 288/417 (69.1%) and patients requested 129/417 (30.9%) of all the contacts. The primary causes of contact were clinical data (242/417, 58%), PRO data (92/417, 22.1%), and medication concerns and precautionary reasons (83/417, 19.9%). Physicians found the use of PRO measures in remote follow-up beneficial for assessing the patient's health. The inclusion of self-reported clinical data in the questionnaire motivated physicians to assess patient responses. However, some barriers were emphasized, such as loss of a personal relationship with the patient and the risk of missing important symptoms in the absence of a face-to-face assessment. CONCLUSIONS: This study demonstrates the importance and practical use of remote monitoring among patients with CKD. Overall, the intervention was implemented as intended. We observed high patient adherence rates, and the physicians managed most questionnaires. Some physicians worried that distance from the patients made it unfeasible to use their "clinical glance," posing a potential risk of overlooking crucial patients' symptoms. These findings underscore key considerations for the implementation of remote follow-up. Introducing a hybrid approach combining remote and face-to-face consultations may address these concerns. TRIAL REGISTRATION: ClinicalTrials.gov NCT03847766; https://clinicaltrials.gov/study/NCT03847766.
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 | 0,071 | 0,091 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,008 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».