Improving Abstraction Quality Through Registry Metric Deconstruction

Structured metric analysis reveals actionable abstraction errors, improves clinical documentation, and enhances data integrity. Integrating artificial intelligence-assisted tools in the future could further optimize and scale this quality improvement approach.

Authors

J.C. Norman (ORCID: https://orcid.org/0000-0003-0672-0271)
B Castillo

Institutions

College of San Mateo (US)

Publication Details

Journal: JACC Case Reports
Published: 2025-10-07
DOI: https://doi.org/10.1016/j.jaccas.2025.105851
Primary Topic: Intelligent Tutoring Systems and Adaptive Learning
Type: article
Field-Weighted Citation Impact: 0.00

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article

Improving Abstraction Quality Through Registry Metric Deconstruction

J.C. Norman, B Castillo

Oct 7, 2025

JACC Case Reports

Intelligent Tutoring Systems and Adaptive Learning

article

Improving Abstraction Quality Through Registry Metric Deconstruction

J.C. Norman, B Castillo

October 7, 2025article en

Abstract

JACC Case ReportsVol. 30(36)

College of San Mateo (US)

Openalex Percentile: Top 85%

Intelligent Tutoring Systems and Adaptive Learning

DOI Open Access PDF OpenAlex

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Improving Abstraction Quality Through Registry Metric Deconstruction — J.C. Norman, B Castillo · JACC Case Reports (2025) | TGRS Research Map | TGRS