Using Standardized Electronic Pathology and Surgery Data to Inform Clinical Quality Improvement and Health System Planning
Notice bibliographique
Résumé
Background: Practice variation in diagnosis and treatment exists between clinicians and jurisdictions across Canada. This variation can impact the quality of care that patients receive and patient outcomes. Knowledge of the scale and type of variation is the first step to developing action plans to improve consistency and enhance patient care. Aim: We aimed to establish a method by which to examine the magnitude of practice variation between clinicians and interjurisdictionally within the cancer system. We leveraged and derived evidence from discrete pathology data collected by five Canadian jurisdictions at the point of care to identify areas to improve quality of cancer care services and to direct patient care. Methods: Fifty pathologists, surgeons, and medical oncologists from 10 jurisdictions conferred to leverage literature and data standards (developed by the College of American Pathologists (CAP)) to create 48 descriptive and outcome indicators related to five cancers: breast, lung, colorectal, endometrial, and prostate cancer. Five jurisdictions collected and used data to generate the indicators. This baseline data were reviewed by 65 clinicians. Results: Interjurisdictional comparative baseline data analyses on 48 indicators showed clinical validity and relevance for use to direct downstream patient care. Data characterizing cancer type, stage, and grade distribution were consistently reported across geography and aligned with the evidence noted in the literature. The data also noted practice and performance variation across multiple cancer sites. For example, although the recommended guideline is to examine at least 12 lymph nodes in 90% of colorectal cancer patients, only one province met this target. Another example is Lynch syndrome testing, which may be important for patients with a diagnosis of colorectal or endometrial cancer depending on the age at diagnosis and family history. The data showed that 0%–70% of patients diagnosed with colorectal cancer prior to age 70 received testing for Lynch syndrome, and only 10%–40% of endometrial cancer cases were tested for markers of Lynch syndrome across the country. The value of these indicators is enormous to inform potential training opportunities and set standards of care at the local or broader clinical governance level so that consistent, high-quality care is delivered in accordance with evidence-based guidelines. Conclusion: Practice variation exists between clinicians and jurisdictions, and comparative pathology data can be used to create a cancer learning system. Four jurisdictions are now embarking on leveraging indicator data analysis to generate physician-level feedback reports and convening communities of practice with the goal of facilitating peer-to-peer conversations, and establishing benchmarks and targets to improve the quality of care, refine or develop clinical guidelines, and inform health system planning in Canada. These lessons can be applied in other cancer systems.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,019 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».