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Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...

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Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

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Published on: September 27, 2024

Process for Quality Management of Electronic Medical Records-Based Data: Case Study Using Real Colorectal Cancer

NaYoung Park^1,2, Kyungmin Na³, Woongsang Sunwoo⁴

¹Department of Health Administration, Kongju National University, Gongju-Si, Chungcheongnam-do, Gongju, 32588, Republic of Korea, 82 41-850-0328.

JMIR Medical Informatics

|November 13, 2025

Summary

A new quality management process (QMP) significantly reduces missing data in colorectal cancer (CRC) research. This improves the reliability of clinical data and AI models for better decision-making.

Keywords:

colorectal cancer data quality medical data quality management real-world data

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E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy

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Published on: August 1, 2019

Area of Science:

Medical Informatics
Clinical Data Management
Cancer Research

Background:

Advancing data-driven medical research generates vast datasets.
Data heterogeneity, complexity, and incompleteness hinder practical use.
Errors and missing data compromise AI predictive models and clinical decision-making.

Purpose of the Study:

Develop a rules-based quality management process (QMP).
Address data errors and impute missing values in real-world clinical data.
Establish high-quality datasets for clinical research.

Main Methods:

Utilized colorectal cancer (CRC) clinical data from 6491 patients (2010-2022).
Leveraged the Korea Clinical Data Use Network for Research Excellence library.
Implemented a literature review, labeling process, and automatic staging library with rule-based analysis.

Main Results:

Reduced missing data for TNM stage from 75.3% to 35.7%.
Reduced missing data for SEER from 24.3% to 18.5%.
Feature selection included previously unselected variables like TNM stage in the improved model.

Conclusions:

Developed and validated a rules-based QMP for clinical data.
Demonstrated effectiveness in reducing errors and imputing missing values.
Highlighted potential for broader application in clinical studies and cancer research.