Identifying patients with asthma in primary care electronic medical record systems
Notice bibliographique
Résumé
Objective To develop and test a variety of electronic medical record (EMR) search algorithms to allow clinicians to accurately identify their patients with asthma in order to enable improved care. Design A retrospective chart analysis identified 5 relevant unique EMR information fields (electronic disease registry, cumulative patient profile, billing diagnostic code, medications, and chart notes); asthma-related search terms were designated for each field. The accuracy of each term was tested for its ability to identify the asthma patients among all patients whose charts were reviewed. Increasingly sophisticated search algorithms were then designed and evaluated by serially combining individual searches with Boolean operators. Setting Two large academic primary care clinics in Hamilton, Ont. Participants Charts for 600 randomly selected patients aged 16 years and older identified in an initial EMR search as likely having asthma (n = 150), chronic obstructive pulmonary disease (n = 150), other respiratory conditions (n = 150), or nonrespiratory conditions (n = 150) were reviewed until 100 patients per category were identified (or until all available names were exhausted). A total of 398 charts were reviewed in full and included. Main outcome measures Sensitivity and specificity of each search for asthma diagnosis (against the reference standard of a physician chart review–based diagnosis). Results Two physicians reviewed the charts identified in the initial EMR search using a standardized data collection form and ascribed the following diagnoses in 398 patients: 112 (28.1%) had asthma, 81 (20.4%) had chronic obstructive pulmonary disease, 104 (26.1%) had other respiratory conditions, and 101 (25.4%) had nonrespiratory conditions. Concordance between reviewers in chart abstraction diagnosis was high (κ = 0.89, 95% CI 0.80 to 0.97). Overall, the algorithm searching for patients who had asthma in their cumulative patient profiles or for whom an asthma billing code had been used was the most accurate (sensitivity of 90.2%, 95% CI 87.3% to 93.1%; specificity of 83.9%, 95% CI 80.3% to 87.5%). Conclusion Usable, practical search algorithms that accurately identify patients with asthma in existing EMRs are presented. Clinicians can apply 1 of these algorithms to generate asthma registries for targeted quality improvement initiatives and outcome measurements. This methodology can be emulated for other diseases.
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 | 0,001 | 0,000 |
| 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 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 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 ».