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Several accidents caused by human error are presented, and human error data collection systems, framework for human error models, and task analyses are discussed in this chapter.
For example, to reduce data collector error, data collection was verified for reliability and consistency.
The errors were identified by pharmacists as part of their routine work, a common method of prescribing error data collection [ 8, 9, 16].
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Huge effort and resource-expenditure go into error-free data collection; errors should not be introduced subsequently by less-than-meticulous data management.
In Open sources DEMs such as SRTM, ASTER and Cartosat-1, various factors affecting the accuracy of satellite based DEM such as errors during data collection, systematic errors and unknown errors that are geographically dependent on terrain conditions cannot be avoided.
Our evaluation provides additional evidence that medical record abstraction and transcription are the steps most likely to introduce error into data collection and management processes, and that source-to-database error rates may vary depending on therapeutic area and according to site data practices.
Data from two subjects were excluded from the analysis reported here, one because of an error in data collection and the other because their data failed to yield usable ICs in any of the four clusters we studied intensively, leaving 10 subjects (3 women) with usable data.
Second, there are potentials for error in data collection.
These reports come from outside sources, so a minor chance of error in data collection exists.
This paper has focused upon response quality as one source of error in data collection.
Due to an error in data collection, no MFV results were obtained in one patient.
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