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Related Concept Videos

Quality Assurance01:19

Quality Assurance

3.7K
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
3.7K
Quality Control01:05

Quality Control

4.1K
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
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Statistical Analysis: Overview01:11

Statistical Analysis: Overview

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
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Accuracy and Precision01:52

Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.  Highly accurate...
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Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

3.0K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
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Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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The Missing Ingredient In Quality Measurement.

Thomas Reinke

    Managed Care (Langhorne, Pa.)
    |January 26, 2017
    PubMed
    Summary

    Process measures are commonly used to assess healthcare quality but may not fully capture true patient outcomes. Experts question if these common measures truly reflect the core of quality care delivery.

    Area of Science:

    • Healthcare quality assessment
    • Clinical process measurement

    Background:

    • Current healthcare quality assessment relies heavily on process measures.
    • Process measures, such as checking diabetic foot care, are easier to track than actual patient outcomes.

    Purpose of the Study:

    • To evaluate the effectiveness of current process measures in assessing healthcare quality.
    • To explore expert opinions on whether process measures adequately reflect the core of quality care.

    Main Methods:

    • Review of current practices in healthcare quality assessment.
    • Analysis of the limitations of process measures compared to outcome measures.

    Main Results:

    • Process measures are the predominant method for evaluating healthcare quality.

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  • A significant portion of experts believe process measures do not fully represent the essence of quality care.

    • Conclusions:

    • There is a recognized gap between process measures and the actual quality of patient care.
    • Further research or alternative methods may be needed to better assess true healthcare quality.