Implementation of a novel linkage of primary care electronic medical record data with hospital data in South Eastern Ontario
Notice bibliographique
Résumé
<h3>Context:</h3> Currently, primary care data, community data, and hospital data are not linked in Ontario, resulting in a disconnect in continuity of care. Combining these datasets in a consolidated data repository could result in an improved understanding of the care journey, support the healthcare needs of Ontario Health Team priority populations, promote continuity of care improvements across sectors, and decrease burden on emergency departments (EDs) and primary care providers. <h3>Objectives:</h3> To link primary care electronic medical record (EMR) data with community and hospital data and to test the utility of the merged dataset through a targeted quality improvement (QI) intervention among high-risk patients with chronic obstructive pulmonary disease (COPD). <h3>Datasets:</h3> Primary care EMR data from the Eastern Ontario Network and acute care, post-acute, and community mental health and addictions data from the Shared Health Integrated Information Portal. <h3>Population and Intervention:</h3> Patients attending an academic family health team who were at risk of COPD-related ED visits were identified and targeted for a QI intervention in which patients saw a COPD Specialist for pulmonary function testing, action plan development, medication review, and education. <h3>Results:</h3> Robust legal, privacy, and technical processes were developed and applied to securely link and merge datasets. Privacy risks were mitigated through a privacy impact assessment and execution of data use agreements between stakeholders. 1072 patients with COPD were identified within the merged dataset, 50% of whom visited the ED within two years. Risk factors (i.e., comorbid disease, smoking status) were determined to predict those at highest risk for future ED visits. Following patient review by clinician, 77 patients were deemed eligible. A total of 25 patients (32%) were booked for the intervention highlighting a simple pathway for patient care improvements in line with best practice guidelines. <h3>Conclusions:</h3> Despite privacy, legal, and technical considerations when combining datasets from different sources, we were able to successfully and safely bridge the gap between primary care EMR and hospital data. We demonstrated the capacity to implement data-drive QI approaches to support patient care across health care sectors using the novel merged datasets. Overall, this project highlights a robust linkage process which can be scaled and spread across primary care clinics and health conditions.
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 | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 | 0,001 |
| 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 ».