Effects of implementing electronic medical records on primary care billings and payments: a before-after study
Notice bibliographique
Résumé
BACKGROUND: Several barriers to the adoption of electronic medical records (EMRs) by family physicians have been discussed, including the costs of implementation, impact on work flow and loss of productivity. We examined billings and payments received before and after implementation of EMRs among primary care physicians in the province of Ontario. We also examined billings and payments before and after switching from a fee-for-service to a capitation payment model, because EMR implementation coincided with primary care reform in the province. METHODS: We used information from the Electronic Medical Record Administrative Data Linked Database (EMRALD) to conduct a retrospective before-after study. The EMRALD database includes EMR data extracted from 183 community-based family physicians in Ontario. We included EMRALD physicians who were eligible to bill the Ontario Health Insurance Plan at least 18 months before and after the date they started using EMRs and had completed a full 18-month period before Mar. 31, 2011, when the study stopped. The main outcome measures were physicians' monthly billings and payments for office visits and total annual payments received from all government sources. Two index dates were examined: the date physicians started using EMRs and were in a stable payment model (n = 64) and the date physicians switched from a fee-for-service to a capitation payment model (n = 42). RESULTS: Monthly billings and payments for office visits did not decrease after the implementation of EMRs. The overall weighted mean annual payment from all government sources increased by 27.7% after the start of EMRs among EMRALD physicians; an increase was also observed among all other primary care physicians in Ontario, but it was not as great (14.4%). There was a decline in monthly billings and payments for office visits after physicians changed payment models, but an increase in their overall annual government payments. INTERPRETATION: Implementation of EMRs by primary care physicians did not result in decreased billings or government payments for office visits. Further economic analyses are needed to measure the effects of EMR implementation on productivity and the costs of implementing an EMR system, including the costs of nonclinical work by physicians and their staff.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».