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Related Concept Videos

Quantifying and Rejecting Outliers: The Grubbs Test01:02

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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What Are Outliers?01:12

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Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
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Outliers and Influential Points01:08

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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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Detection of Gross Error: The Q Test01:00

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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...
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Regression Toward the Mean01:52

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Testing a Claim about Standard Deviation01:19

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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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Cracking down on cost outliers.

Jill E Sackman, Levi Citrin

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    Summary
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    Targeting high-cost patients and physicians with a systematic approach effectively reduces healthcare costs. This involves real-time data analysis and continuous improvement strategies for sustainable cost management.

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    Area of Science:

    • Healthcare Management
    • Health Economics
    • Clinical Finance

    Background:

    • Healthcare costs are a significant concern for providers and patients.
    • Identifying and addressing cost outliers is crucial for financial sustainability.
    • Existing cost-reduction strategies may lack a systematic, data-driven approach.

    Purpose of the Study:

    • To present a systematic approach for reducing healthcare costs.
    • To outline strategies for targeting high-cost patient and physician outliers.
    • To emphasize the role of real-time data and specialist teams in cost management.

    Main Methods:

    • Implementing a systematic approach targeting cost outliers.
    • Forming multidisciplinary teams of specialists for data analysis.
    • Utilizing a care map analytic framework for problem identification.
    • Establishing mechanisms for real-time data monitoring and intervention.

    Main Results:

    • Effective reduction of healthcare costs through targeted interventions.
    • Improved identification of problem areas and cost drivers.
    • Sustainable mechanisms for long-term cost control and management.
    • Enhanced financial performance through data-driven decision-making.

    Conclusions:

    • A systematic, data-driven approach targeting cost outliers is essential for sustainable healthcare cost reduction.
    • Multidisciplinary teams and real-time analytics are key to identifying and managing high-cost areas.
    • Care map analytics provide a framework for hospitals to control and manage costs effectively in the long term.