Are Patient Self-reported Healthcare Utilization Data Reliable in Persons With IBD?
Notice bibliographique
Résumé
In its 2018 impact report, Crohn’s and Colitis Canada (CCC), the significant health and economic burden of inflammatory bowel disease (IBD) was highlighted.1 Over the last 2 decades, all stakeholders have been working together to improve quality of care provided to persons with IBD and reduce the negative impacts of this serious disease. Several quality improvement programs have been successfully created with very promising results.2,3 One great example of quality improvement initiative is the ImproveCareNow (ICN) pediatric network that includes over 100 pediatric IBD centers mainly in the United States but also internationally.4 Another collaborative work to improve learning health systems (LHS) is the Crohn’s and Colitis Foundations’ IBD Qorus LHS.5 This is a network of over 50 academic and private IBD practices that work together toward improving healthcare outcomes in adults with IBD and have already lead to reductions in emergency room visits, hospitalizations, and opioid use in adults with IBD.6 In any of these networks, and to allow clinicians and administrators to improve the healthcare system, collecting accurate data on the provided services, quality measures, and healthcare outcomes is prudent for the success of LHS. Several data sources exist and include administrative databases, electronic health records (EHRs), and patient self-reported data with pros and cons in each option. In this issue of Crohn’s & Colitis 360, Van Deen et al7 compared patient self-reported data on medication utilization, vaccinations, and some other aspects of healthcare use such as emergency department visits and hospitalization, to EHR within 4 sites of the IBD Qorus LHS. They analyzed data from 328 IBD patients who were surveyed about utilization of some healthcare services, mainly IBD-related emergency department visits, hospitalizations, and computerized tomography scans that they received in 6-month period before the survey. The agreement between patient-reported and EHR-obtained data was reassuring and ranged from 89% to 96%. At the time of patients’ visit, the agreement between self-reported medication use and the EHR was 92% for both corticosteroid and opioid use. On the other hand, the agreement between self-reported and EHR-reported vaccination status was 61% agreement for influenza vaccinations in the previous year and 68% agreement for pneumococcal vaccinations that were ever received. The study results are important and concordant with other studies that showed that, despite a relatively long recall period, patient self-reported data on hospital utilization and medication are usually accurate and reliable.8,9 However, and as mentioned by the authors, the study conclusions are limited by the lack of the exact survey completion rate and hence the probability of selection bias. Overall, this work is a confirming step on the reliability of patient-reported data and clearly signifies the importance of partnership between patients and healthcare personnel toward improving healthcare systems. None. None. NA as this is an editorial article.
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 | 0,037 | 0,141 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,006 | 0,003 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,008 | 0,006 |
| Science ouverte | 0,005 | 0,002 |
| Intégrité de la recherche | 0,019 | 0,025 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,004 |
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 ».