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Data Validation01:03

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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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Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
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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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The validity of validation: A practical assessment.

Eric C Stone1, Vickie Miller1, Heidi J Shedenhelm2

  • 1Division of Infectious Diseases, Mayo Clinic, Rochester, Minnesota.

Infection Control and Hospital Epidemiology
|January 25, 2020
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Summary
This summary is machine-generated.

Electronic health record reporting is more reliable than manual counts within 30 days. A shorter validation period for electronic data capture can save costs while maintaining accuracy.

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Area of Science:

  • Healthcare Informatics
  • Clinical Data Management
  • Infection Prevention

Background:

  • Manual data collection for healthcare surveillance is time-consuming and prone to errors.
  • Electronic health record (EHR) systems offer potential for more efficient and accurate data reporting.
  • Validation of EHR reporting tools is crucial for reliable infection prevention surveillance.

Purpose of the Study:

  • To compare the time required for reliable data reporting between EHR systems and manual methods.
  • To assess the accuracy and reliability of EHR data for central venous catheter and urinary catheter surveillance.
  • To determine optimal validation periods for EHR reporting tools.

Main Methods:

  • Simultaneous collection of electronic and manual counts for device data (central venous catheters, urinary catheters).
  • Discrepancy analysis to identify sources of error in electronic and manual reporting.
  • Statistical comparison of data reliability between EHR and manual methods.

Main Results:

  • Electronic reporting achieved durable reliability within 30 days, outperforming manual counts.
  • EHR data proved more reliable than manual counts for tracking device utilization.
  • Validation of EHR data for catheter days was achieved significantly faster than traditional methods.

Conclusions:

  • Shorter validation periods for EHR data capture are feasible and effective.
  • Implementing EHR reporting can lead to substantial cost savings compared to longer validation mandates.
  • Reliable electronic data reporting can be achieved more rapidly, enhancing infection prevention efforts.