Use of regional clinical data to identify veterans for a multi-center osteoporosis electronic consult quality improvement intervention
Notice bibliographique
Résumé
Background: Electronic medical record systems can rapidly identify fracture patients so that healthcare systems can target osteoporosis treatment programs. However, it is not clear what proportion of such patients are actually eligible for treatment. Method: In 3 Veterans Affairs Medical Centers, a secondary fracture prevention electronic screening protocol was developed and proceeded in 3 stages. First, all patients with a fracture-related ICD-9 or CPT code for fracture over the preceding 6 months were identified using a SQL server report run regularly on regional clinical data. Additional data was obtained automatically at this stage, and patients were excluded if they were already on bisphosphonate, their fracture was facial or digital, they did not have a primary care provider, they were under age 50 years, or had died. In a second stage, chart abstraction was completed by the project director. Patients were excluded if their fracture occurred after high-impact trauma, the coded fracture was not confirmed on radiograph, the fracture occurred more than 10 years previously, bone density screening had already been obtained, the fracture was pathologic, the patient was receiving palliative care, or the patient had been offered and declined therapy. In the final stage, remaining patients were referred to a bone specialist who reviewed the medical record and generated an electronic consult to the primary provider that gave recommendations for further evaluation and management consistent with current guidelines. Results: Among 986 screened veterans with ICD9 fracture code within the study period, 841 (85%) were ultimately excluded from further intervention. A majority (n=574, 68%) were excluded in the first, automated screening stage [no primary provider (22%), age under 50 years (38%), already on a bisphosphonate (12%), fracture facial or digital (25%), patient had died (3%)]. Chart abstraction was required to exclude 267 (32%) prior to physician review [high trauma (37%), remote injury or no evidence of fracture (36%), palliative care (9%), other reasons (18%)] One hundred three consults were completed, with 80 (78%) recommending osteoporosis treatment or BMD testing. Conclusion: An electronic screening tool was effective at a regional level in identifying recent fracture patients for secondary osteoporosis intervention, but many (85%) are ultimately not eligible for additional interventions. Most exclusions (68%) can be made without additional chart abstraction.
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 | 0,012 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».