Evaluating the Population-Based Usage and Benefit of Digitally Collected Patient-Reported Outcomes and Experiences in Patients With Chronic Diseases: The PROMchronic Study Protocol
Notice bibliographique
Résumé
BACKGROUND: Chronic diseases are associated with a high disease burden. Under- and overprovision of care as well as quality variation between health care providers persists, while current quality indicators rarely capture the patients' perspective. Capturing patient-reported outcome measures (PROMs) as well as patient-reported experience measures (PREMs) is becoming more and more important to identify gaps in care provision, prioritize services most valuable to patients, and aid patients' self-management. OBJECTIVE: This study aims to measure the potential benefits and effectiveness of using electronic patient-reported outcome measures (ePROMs) and electronic patient-reported experience measures in a structured and population-based manner to enhance health care for chronic disease patients in Germany. METHODS: This prospective cohort study aims to evaluate the potential benefits of PROM usage in patients with chronic diseases. We evaluate whether (1) digitally collected PROMs and PREMs can be used for health system performance assessment by generating a representative response of chronically diseased individuals with asthma, chronic obstructive pulmonary disease, diabetes, and coronary artery disease across Germany, and (2) based on the PROMs and PREMs, low-value care can be identified. As patient-reported outcomes (PROs) are rarely presented back to patients, (3) this study also examines patients' reactions to their PROM scores in the form of digital PRO feedback. For these purposes, randomly selected patients from a nationwide German insurer are digitally surveyed with generic and disease-specific PROMs and PREMs, as well as additional questions on their health-related behavior, 4 times over 1 year. Individual PRO feedback is presented back to patients longitudinally and compared to a peer group after each survey period. Patient-reported data is linked with health insurance data. Response rates, changes in health and experience outcomes over time, self-reported changes in health behavior, and health care system usage will be analyzed. RESULTS: The PROMchronic study explores the usage of PROMs in patients with chronic diseases. Data collection began in October 2023, after the initial invitation letter. All the 200,000 potential patients have been invited to participate in the study. Data have not yet been analyzed. Publication of the interim results is planned for the autumn of 2024, and the results are planned to be published in 2025. CONCLUSIONS: We aim to fill the research gap on the population-based usage of PROMs and PREMs in patients with chronic diseases and add to the current understanding of PROM data-sharing with patients. The study's results can thereby inform whether a health care system-wide approach to collecting PROMs and PREMs can be used to identify low-value care, assess quality variation within and across chronic conditions, and determine whether PRO feedback is helpful and associated with any changes in patients' health behaviors. TRIAL REGISTRATION: German Clinical Trials Register DRKS00031656; https://drks.de/search/en/trial/DRKS00031656. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56487.
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 | 0,048 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,005 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,003 |
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 ».