Metrics that matter: Performance tools to improve prescribing and reporting at Princess Margaret (PM) Cancer Centre.
Notice bibliographique
Résumé
614 Background: Ordering anti-cancer systemic therapy (ST) with Systemic Treatment Computerized Prescriber Order Entry (STCPOE) is critical for safe prescribing. STCPOE enables secondary data capture for organizational reporting, reimbursement, and insights into prescribing patterns. In 2022, PM implemented a new Electronic Health Record (EHR) with STCPOE system. Post Go-Live, reduced clinician use of STCPOE for take home cancer drug (THCD) prescribing and 19% decrease in provincial quality-based procedure (QBP) funding volumes for THCD was noted compared to the prior fiscal year (FY). A quality improvement project was launched to promote appropriate STCPOE use for prescribing, improve data quality, and recover funding. Methods: A working group with data scientists, clinicians, executives & EHR specialists, conducted a root-cause analysis (RCA) using 5 Whys methodology to understand key gaps. Over one year, education and change management initiatives were developed, guided by Plan-Do-Study-Act methodology. Clinician education focused on STCPOE for safe prescribing, use of real-time data for reporting, and funding impact of inaccurate clinician-entered data. Executive and clinician buy-in was prioritized to develop individual and disease-site level performance report cards with data insights on prescribing practices outside STCPOE and issues with data fields affecting funding. Performance report cards were co-designed with end-users, for leaders and clinicians. These were shared monthly with leadership, along with individual feedback loop on ideas to improve STCPOE use. Key evaluation metrics included rates of ordering outside of ST-CPOE and monthly QBP funded volumes. Results: RCA revealed high volumes of THCD ordered outside STCPOE and while intravenous (IV) ST was ordered using STCPOE, there were inaccuracies in clinician-entered data fields required to trigger funding affecting data quality & volume capture, leading to funding loss. The following results were seen since the launch of interventions in September 2024 to March 2025: 6% decrease in ordering THCD prescriptions outside of STCPOE; 8% decrease in clinicians selecting inappropriate data elements in IV ST; 9% increase in reported funding volume for THCD prescriptions compared to prior FY (adjusted 3% natural patient volume growth); and 2 medium-scale informatics projects to improve STCPOE usability & efficient prescribing completed. Conclusions: The use of a performance improvement approach improved STCPOE adherence for THCD, safe medication prescribing, and accuracy of secondary data capture for funding. Drivers for success included early executive sponsorship, clinician-engagement, & education with data-driven performance report cards and feedback loops to sustain behavior change. This initiative underlines the importance of funding models & safe prescribing as motivators for change.
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 | 0,004 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».