Development of Quality Metrics to Evaluate Pediatric Hematologic Oncology Care in the Outpatient Setting
Notice bibliographique
Résumé
Abstract Introduction: Systems to quantify and incentivize quality of care (QoC) have been developed in multiple healthcare settings. In pediatric oncology, lists of QoC metrics or recommendations have been procured through consensus methodologies such as the Delphi process. To date, no QoC metrics have been developed for outpatient pediatric oncology. Objectives: The aim of this study was to develop a list of QoC metrics for the leukeumia-lymphoma (LL) clinic at the Hospital for Sick Children in Toronto, using a consensus process that could be adapted to other clinic settings. Methods: A modified Delphi process following the American Society of Clinical Oncology (ASCO) guidelines was used to generate consensus on a list of QoC metrics (Loblaw et al., 2012). A Medline-Ovid search was conducted for quality indicators, metrics and recommendations relevant to pediatric oncology. Results were screened for (a) system-level metrics that could be translated to a clinic level and (b) clinic-level recommendations that could be converted to measurable quantities. Additional metrics outside the literature search were considered. A provisional list was compiled and circulated electronically to local stakeholders, including medical and nursing staff (n=10). Stakeholders ranked each metric on a 5-point Likert scale based on importance and feasibility of measurement (round 1). Stakeholders provided feedback on the metrics and suggested additional metrics. Median, interquartile range and full ranges were calculated for each metric. A metric was considered to reach consensus if the percent of respondents ranking within two consecutive scores was ≥70%. Results and comments from round 1 were re-circulated to stakeholders in personalized reports. This allowed each stakeholder to compare his or her previous scores with overall scores for each metric. Stakeholders were asked to re-rank each metric (round 2). Results: The literature search yielded 2 relevant publications from which a provisional list of 27 metrics was generated. Metrics were grouped into 7 categories (Table 1). In round 1, 19/27 (70%) metrics reached consensus. Stakeholders’ comments resulted in 4 new metrics and edits to 8 original metrics. All metrics were included in round 2 for a total of 31. Twenty-four of 31 (77%) metrics reached consensus after round 2 (Table 1). Thirteen were chosen for the final list based on highest consensus scores, highest interquartile and full ranges, and minimizing redundancy. Conclusion: This study demonstrates the feasibility of using a modified Delphi process to generate QoC metrics for a pediatric hematology oncology clinic, and provides a model other clinics may employ for local use. The final metrics will be used to evaluate the quality of care in the LL clinic, and to identify areas for improvement in clinic function. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.
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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 | 0,011 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 ».