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Quality Assurance01:19

Quality Assurance

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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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Health Information Technology and Healthcare Information System01:30

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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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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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Automating Electronic Health Record Data Quality Assessment.

Obinwa Ozonze1, Philip J Scott2, Adrian A Hopgood3

  • 1School of Computing, University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth, PO1 3HE, UK.

Journal of Medical Systems
|February 13, 2023
PubMed
Summary

Automated tools for assessing Electronic Health Record (EHR) data quality are emerging, but current systems are fragmented and lack theoretical grounding. Standardization is needed to ensure the reliability of EHR data quality assessment programs.

Keywords:
AutomationData qualityData quality assessmentElectronic health record (EHR)

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Area of Science:

  • Health Informatics
  • Data Science
  • Information Systems

Background:

  • Electronic Health Record (EHR) systems are crucial for healthcare but face significant data quality (DQ) challenges.
  • Ensuring EHR data is fit for use requires reliable Data Quality Assessment (DQA) tools, which are currently underdeveloped.
  • The increasing reliance on EHR data necessitates robust strategies for DQ management.

Purpose of the Study:

  • To review the current research on operationalizing EHR DQA, focusing on automated tooling.
  • To identify existing DQA programs and prototypes used in real-world settings.
  • To highlight key considerations for future EHR DQA implementations.

Main Methods:

  • A systematic literature review of 1841 articles from major databases (PubMed, Web of Science, Scopus) published between 2011 and 2021.
  • Identification and analysis of 23 DQA programs and prototypes assessing EHR data quality.
  • Categorization of DQA programs based on quality dimensions assessed, knowledge sources, and methodologies.

Main Results:

  • 14 DQA programs in real-world settings and 9 experimental prototypes were identified.
  • Completeness and value conformance were the most frequently assessed DQ dimensions.
  • While automation is increasing, current EHR DQA efforts are fragmented, lack theoretical backing, and vary widely in scope and methodology.

Conclusions:

  • The automation of EHR DQA is a growing trend, yet current tools and programs are not standardized.
  • Existing DQA programs often rely on expert knowledge and literature reviews, with limited use of data-driven techniques.
  • There is a critical need for standardized EHR DQA programs to ensure the unknown quality of EHR data is reliably assessed.