Strengths and Barriers to Coding Hospital Chart Information from Health Information Manager Perspectives: A Qualitative Study
Notice bibliographique
Résumé
IntroductionIt is essential that clinical documentation and data coding be of high quality for the production of healthcare data for research or administrative purposes. However, there is a limited understanding of the facilitators and barriers of coded data quality and strategies to improve it.
 Objectives and ApproachOur objective was to qualitatively assess what influences coded data quality from the perspective of health information managers who are responsible for the work of coding specialists. Nine health information managers and/or coding quality coordinators who oversee coding specialists were identified and recruited from nine provinces across Canada to participate in this study. Semi-structured interviews were conducted which asked questions on participant demographics, responsibilities, data quality, costs and budget of coding, continuing education for Health Information Management (HIM), suggestions for quality improvement, and barriers to quality improvement. Interviews were recorded and transcribed, and analyzed using Directed Content Analysis methodology.
 ResultsInterviewees were primarily responsible for managing staff, quality assurance, audits, reporting, budget, data collection, and transcription. Managers reported that the experienced coders under their employ strengthened coding quality. Common barriers to coding quality included incomplete and unorganized chart documentation, which led to undercoding, and lack of communication and access to physicians for clarification when needed. Further, coding quality suffered as a result of limited resources (e.g. staffing and budget) being available to HIM departments for an ever-expanding workload, that was commonly due to increasingly complex charts and additional project data. Managers unanimously reported that coding quality improvements can be made by 1) making interactive training programs available to coding specialists, and 2) streamlining sources of information from charts (i.e., transitioning to standardized electronic charting).
 Conclusion/ImplicationsAlthough coding quality is generally regarded as high across Canada, quality can be hampered by incomplete and inconsistent chart documentation, lack of resources (e.g. financial support, staff, education), and inconsistent coding standards across hospitals and provinces. This study presents novel evidence for coding quality improvement across Canada.
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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 | 0,006 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,003 | 0,000 |
| Communication savante | 0,000 | 0,012 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».