Toward a Continuous Quality Improvement Paradigm for Hemodialysis Providers with Preliminary Suggestions for Clinical Practice Monitoring and Measurement
Notice bibliographique
Résumé
BACKGROUND: Consensus processes using the clinical literature as the primary source for information generally drive projects to draft clinical practice guidelines (CPGs). Most such literature citations describe special projects that are not part of an organized quality management initiative, and the publication/review/consensus process tends to be long. This project describes an initiative to develop and explore a flexible and dedicated data-driven paradigm for deciding new CPGs that could be rapidly responsive to changing medical knowledge and practice. METHODS: Candidate Clinical Practice Monitoring Measures (CPMM) were selected using a large, national database according to the natures and strengths of their associations with mortality risk among patients during 1994. Thresholds above or below which risk of death increased were evaluated for each CPMM using risk profile charts and spline functions. The fractions of patients outside of those thresholds in each dialysis unit (the %Var) were determined for the years 1993, 1994, and 1995. A standardized mortality ratio (SMR) was also determined for each year for each facility. The associations between the %Var and SMR were evaluated in several single-variable and multivariable statistical models. RESULTS: Eleven CPMM were selected and evaluated based on their associations with death risk. These included the urea clearance x dialysis time product (Kt); the concentrations of albumin, potassium, phosphate, bicarbonate, hemoglobin, neutrophils, and lymphocytes in the blood; the body weight/height ratio; diastolic blood pressure; and vascular access type. Even though the CPMM were strongly associated with death risk among patients, the %Var were weakly and inconsistently associated with SMR among facilities. CONCLUSIONS: The paradigm was flexible, easy to implement, quickly executed, and potentially able to accommodate evolving medical practice assuming the availability of large database systems such as this. The primary associates of death risk were easily identified and the thresholds easily adopted. The SMR and %Var from the CPMM were only weakly associated, however, suggesting that one cannot be reliably predicted from the other. As such, quality management programs should likely monitor both the processes and outcomes of care among dialysis facilities.
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 | 0,211 | 0,167 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,006 | 0,007 |
| Communication savante | 0,018 | 0,012 |
| Science ouverte | 0,006 | 0,011 |
| Intégrité de la recherche | 0,007 | 0,013 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».