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Related Concept Videos

Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Pilot and Numeric Relaying01:21

Pilot and Numeric Relaying

Pilot relaying is a type of differential protection used in power systems. It compares electrical quantities at the terminals of equipment via a communication channel instead of direct relay interconnection. This method is essential for transmission lines where the terminals are far apart, typically up to 80 km for lines with 69 to 115 kV ratings. Four types of communication channels are used for pilot relaying:
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Data Reporting and Recording01:24

Data Reporting and Recording

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...
Contaminants and Errors01:16

Contaminants and Errors

Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...

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Related Experiment Video

Updated: Jul 10, 2026

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
07:48

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Published on: April 4, 2025

The good catch pilot program: increasing potential error reporting.

JoAnn M Mick1, Geri L Wood, Robert L Massey

  • 1Department of Nursing Research and Evidence Based Practice, The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA. jmick@mdanderson.org

The Journal of Nursing Administration
|November 3, 2007
PubMed
Summary

The Good Catch Program boosted reporting of potential medical errors by changing "close call" to "good catch" terminology and adding safety reports and incentives. This initiative significantly increased error identification and reporting.

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Area of Science:

  • Healthcare safety
  • Medical error reporting systems

Background:

  • Low reporting rates in existing close call systems (175 reports over 2.5 years) indicated a need for intervention.
  • Existing systems failed to capture a significant number of potential errors.

Purpose of the Study:

  • To evaluate the effectiveness of the Good Catch Program in increasing the reporting of potential errors.
  • To assess the impact of specific program strategies on reporting culture.

Main Methods:

  • Implementation of the Good Catch Program with three key strategies: terminology change, end-of-shift safety reports, and executive-sponsored incentives.
  • Monitoring and analysis of reporting data before and after program implementation.

Main Results:

  • Significant increase in the number of reported potential errors following program implementation.
  • Positive outcomes attributed to the combined effect of terminology change, new reporting tools, and leadership support.

Conclusions:

  • The Good Catch Program successfully enhanced the reporting of potential medical errors.
  • A multi-faceted approach including terminology, reporting mechanisms, and incentives is effective in improving healthcare safety culture.