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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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Related Experiment Video

Updated: Apr 15, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Evaluating the Reliability of EHR-Generated Clinical Outcomes Reports: A Case Study.

Chatrian Kanger¹, Lisanne Brown¹, Snigdha Mukherjee¹

¹Louisiana Public Health Institute.

EGEMS (Washington, DC)

|April 8, 2015

Summary

Improving electronic health record (EHR) data reliability is crucial for quality reporting. A standardized, collaborative process enhanced data accuracy and trust among safety net providers.

Keywords:

data use and quality health information technology standardized data collection

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

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Published on: September 20, 2018

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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

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Published on: May 15, 2020

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Area of Science:

Health Informatics
Clinical Quality Measurement
Healthcare Data Management

Background:

Quality incentive programs assume reliable clinical quality measure extraction from Electronic Health Records (EHRs).
Safety Net providers, including Federally Qualified Health Centers (FQHCs), show high EHR adoption but low meaningful use rates.
Distrust in EHR data, often due to lack of standardization, necessitates manual chart audits for quality reporting.

Purpose of the Study:

To describe a step-by-step process for enhancing the reliability of data extracted from EHRs.
To increase the accuracy of quality measure reports for improved decision-making and national reporting alignment.
To guide practices and communities in standardizing EHR data capture and reporting.

Main Methods:

A case study detailing a five-step process to harmonize measures and reduce data errors in a community of Safety Net providers using a common EHR.
Focus on reducing reporting errors and assessing their impact on quality measure percentages.
Documentation of activities and resource requirements for the data standardization project.

Main Results:

A nine-month community-wide project resulted in harmonized measures, reduced reporting burden, and fewer errors in EHR-generated reports.
Increased accuracy of clinical outcomes reports empowered physicians and care teams with better data for quality improvement planning.
Participating clinics experienced enhanced confidence in their EHR-generated quality measure reports.

Conclusions:

Achieving reliable population-level quality reporting from EHRs involves challenges at practice, vendor, and community levels.
Close collaboration between clinics and EHR vendors is essential for improving report reliability over time.
Collaborative user groups and technical assistance can build trust in EHR-generated reports, validating data locally before external reporting.