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Related Concept Videos

Quality Assurance01:19

Quality Assurance

4.0K
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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Data Validation01:03

Data Validation

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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.
Nursing assessment guides are generally based on holistic models rather than medical...
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Data Validation01:15

Data Validation

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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
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Quality Control01:05

Quality Control

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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
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Quality of Water01:19

Quality of Water

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In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
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Data Reporting and Recording01:24

Data Reporting and Recording

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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
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A comprehensive framework for data quality assessment in CER.

Erin Holve1, Michael Kahn, Meredith Nahm

  • 1AcademyHealth, Washington, DC;

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|December 5, 2013
PubMed
Summary
This summary is machine-generated.

Ensuring high-quality data is crucial for comparative effectiveness research (CER). This study proposes a unified framework for data quality assessment (DQA) in electronic health records to improve research reliability.

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Area of Science:

  • Health Informatics
  • Clinical Data Management
  • Comparative Effectiveness Research

Background:

  • Comparative Effectiveness Research (CER) relies on diverse data sources, necessitating robust data quality.
  • Current data quality assessment (DQA) methods are often project-specific and lack transparency.
  • Electronic clinical data is increasingly used for CER, patient-centered outcomes research (PCOR), and quality improvement (QI).

Purpose of the Study:

  • To address the need for credible, high-quality data in CER from distributed sources.
  • To establish consistent, comprehensive, and accessible methods for assessing and reporting data quality.
  • To transition DQA from an behind-the-scenes process to an integrated framework.

Main Methods:

  • Harmonizing existing models for describing and measuring clinical data quality.
  • Developing a comprehensive integrated framework for DQA.
  • Leveraging collaborative efforts supported by the Electronic Data Methods (EDM) Forum and funded by the Agency for Healthcare Research and Quality (AHRQ).

Main Results:

  • A proposed integrated framework for assessing and reporting data quality findings.
  • A harmonized approach to describing and measuring clinical data quality.
  • Facilitation of learning and collaboration across CER, PCOR, and QI projects.

Conclusions:

  • A standardized DQA framework is essential for reliable CER using electronic health data.
  • Integrated DQA enhances transparency and comparability across research projects.
  • Collaboration through initiatives like the EDM Forum can advance data quality methods.