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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...
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...
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...
Barriers to Effective Communication II01:21

Barriers to Effective Communication II

The barriers to effective communication also include cultural barriers, semantic barriers, gender barriers, and time constraints.
Cultural barriers:
Differences in values, beliefs, religion, knowledge, and tradition can significantly impact communication. Awareness of nonverbal cues is critical, especially when conversing with a patient from a different culture. What appears appropriate in one culture may be inappropriate in another.
Semantic barriers:
As a result of their tendency to use...
Barriers to Effective Communication I01:30

Barriers to Effective Communication I

A communication barrier is any distortion or interruption during a conversation, resulting in miscommunication of the message. A good communicator should know these barriers and continuously check for the listener's understanding by obtaining feedback.
Communication barriers include the following:
Physiological barriers: They are limitations caused by a person's health condition or disability, such as hearing loss, poor eyesight, illness, or unconsciousness. An example to overcome this barrier...

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Related Experiment Video

Updated: Jul 5, 2026

Examining Gesture Production in the Presence of Communication Challenges
07:18

Examining Gesture Production in the Presence of Communication Challenges

Published on: January 26, 2024

Developing a taxonomy of communication errors in heterogeneous information systems.

Samrend Saboor1, Elske Ammenwerth

  • 1Institute for Health Information Systems, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria. Samrend.Saboor@umit.at

Studies in Health Technology and Informatics
|May 20, 2008
PubMed
Summary

This study summarizes healthcare information system communication errors, identifying 130 reasons across 12 problem classes. This structured data aids in developing better error detection methods for systems like DICOM and HL7.

Related Experiment Videos

Last Updated: Jul 5, 2026

Examining Gesture Production in the Presence of Communication Challenges
07:18

Examining Gesture Production in the Presence of Communication Challenges

Published on: January 26, 2024

Area of Science:

  • Health Informatics
  • Information Systems Engineering
  • Medical Communication Systems

Background:

  • Established healthcare communication standards like DICOM and HL7 exist.
  • Heterogeneous information systems in healthcare still experience numerous communication errors.
  • System complexity hinders the identification of root causes for these errors.

Purpose of the Study:

  • To create a structured summary of communication errors and their underlying reasons.
  • To provide a foundation for developing methods to support error detection in healthcare systems.
  • To address the challenge of identifying error causes in complex, heterogeneous information systems.

Main Methods:

  • A systematic literature review was performed using PubMed.
  • References were iteratively filtered and analyzed.
  • Subsuming qualitative content analysis was applied to categorize errors and reasons.

Main Results:

  • A taxonomy of communication errors has been developed.
  • The current taxonomy includes 12 distinct problem classes.
  • These classes group 42 specific problems, with a total of 130 identified reasons.

Conclusions:

  • A saturation of new error types and classes has been observed, suggesting initial completeness.
  • Expert interviews are planned to enhance the validity and comprehensiveness of the findings.
  • The preliminary results indicate a promising approach to understanding and addressing communication errors.