Notice bibliographique
Résumé
Time required for paperwork has been increasing to the detriment of other aspects of physicians’ work.1 Physicians’ satisfaction is inversely related to this burden.2 In this era of electronic medical records (EMRs), paperwork is being supplanted by electronic “document handling.” We have been unable to find articles in the literature that quantify this aspect of Canadian family doctors’ workload and, therefore, we analyzed document handling in our practice. We began implementing an office EMR system for our rural family practice in 2002. All correspondence and laboratory, imaging, and consultant reports are entered into patients’ electronic records. Prescriptions are generated electronically. Electronic documents handled by physicians in our office during a 3-year period (2003-2005, inclusive) were identified and counted. During this time clinic progress notes were still handwritten; we estimated 1 for each office visit. There were 30 213 visits in the EMR appointment book over the 3 years. We electronically signed 28 304 pages of received correspondence and 21 774 pages of laboratory results (each with an average of 11 test results). The physicians wrote 17 874 prescriptions, with an average of 2 medications per prescription. The practice generated and sent 6109 pages of correspondence. One handwritten note per visit adds 30 213 progress note entries. Every week, on average, each physician saw 97 patients and handled 335 pages of documents (including 91 pages of received correspondence, 70 pages of laboratory results, 57 prescriptions, 20 pages of outgoing correspondence, and 97 progress notes). These numbers underestimate our overall document handling workload and paperwork burden. We have not included estimates for handwritten requisitions (laboratory and imaging), notes to patients, immunization cards, additional progress note entries related to patient phone calls, or follow-up of results. Much of the document workload generated at the local nursing home is not included. We have also not included a count of the many third-party or government forms (eg, drug plan limited-use forms, travel grant applications) that are not entered into the EMR. Paperwork related to billing, office administration, practice audit, quality assurance, continuing education, teaching, research, or coroner duties are not included in this analysis. We believe that our experience of each handling well over 17 000 pages yearly will be similar for other family doctors who provide a range of clinical services. Our numbers for laboratory results are similar to those reported for American primary care physicians by Poon and colleagues in 2003.3 The document burden, however, could be much greater in practices with higher rates of patient referral to specialists or private health insurance coverage. While an EMR might not reduce physician time needed for document handling, it does allow for quantification of this component of physicians’ work. Canadian physicians were already spending 5.4 hours weekly on “indirect patient care” in 2002.4 Increasing time needed for this will exacerbate physician shortages and contribute to longer wait times. Modern clinical practice demands high-quality documentation. Electronic medical records are powerful tools to improve quality of care; however, increased need for documentation will require increased physician manpower. Medical software vendors must strive to find ways to streamline EMR interfaces. Administrators, government agencies, and third parties must be encouraged to prioritize, simplify, and reduce documentation requests from physicians. Efforts to reduce the burdens of paperwork and document handling must become a priority to help reduce physician burnout and frustration and to contribute to solving the problems of doctor shortages and long waiting lists.
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 | 0,001 | 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 ».