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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...
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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...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
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Errors and mistakes in surveying refer to inaccuracies in measurements and data recording. The errors are deviations from the actual value caused by human sensory limitations, equipment flaws, or environmental effects. These errors are typically unintentional and can result from the inherent imperfections in the instruments used, atmospheric conditions, or the observer’s inability to perceive exact measurements. On the other hand, mistakes are caused by the surveyor's lack of attention,...

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Detection of Targetable Alterations in Non-small Cell Lung Cancer using Next-generation Sequencing
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A framework for diagnosing and identifying where technology-induced errors come from.

E M Borycki1, A W Kushniruk, L Keay

  • 1School of Health Information Science, University of Victoria, Victoria, British Columbia, Canada. emb@uvic.ca

Studies in Health Technology and Informatics
|September 12, 2009
PubMed
Summary
This summary is machine-generated.

Health information systems can reduce medical errors but also introduce new ones. A multi-organizational framework is needed to understand technology-induced errors originating from complex healthcare systems.

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Last Updated: Jun 20, 2026

Detection of Targetable Alterations in Non-small Cell Lung Cancer using Next-generation Sequencing
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Published on: October 10, 2025

Area of Science:

  • Health Informatics
  • Human Factors Engineering
  • Systems Science

Background:

  • Health information systems (HIS) offer potential to reduce medical errors.
  • However, HIS can also introduce novel types of technology-induced errors.
  • Complexities in organizational work structures are recognized as origins of high-profile accidents in various industries.

Purpose of the Study:

  • To propose a robust framework for diagnosing technology-induced errors in healthcare.
  • To understand the development, implementation, and policy influences on technology-induced errors.
  • To present a multi-organizational model for analyzing these errors.

Main Methods:

  • Development of a framework considering multiple organizational structures.
  • Application of the framework to analyze technology-induced errors in healthcare.
  • Review of cognitive and human factors literature on accidents in complex systems.

Main Results:

  • Technology-induced errors may originate from multiple organizational levels beyond the healthcare provider.
  • Key contributing organizational structures include governments, model organizations, software development organizations, and local healthcare organizations.
  • The proposed framework provides a structured approach to identifying error origins.

Conclusions:

  • A comprehensive understanding of technology-induced errors requires a multi-organizational perspective.
  • The framework aids in diagnosing errors by considering the interplay of various stakeholders and systems.
  • Further application and refinement of the framework are essential for improving healthcare safety.