Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.0K
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...
4.0K
What Are Outliers?01:12

What Are Outliers?

4.3K
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...
4.3K
Outliers and Influential Points01:08

Outliers and Influential Points

5.1K
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...
5.1K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

7.1K
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...
7.1K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
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...
6.3K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.1K
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...
2.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Equine Personality: Association With Breed, Use, and Husbandry Factors.

Journal of equine veterinary science·2019
Same author

Suspected hypertrophic osteopathy in an ancient canid: Differential diagnosis of possible etiologies.

International journal of paleopathology·2018
Same author

Payment reform a primer for taking on risk.

Healthcare financial management : journal of the Healthcare Financial Management Association·2015
Same author

Critical management tools for getting costs under control.

Physician executive·2013

Related Experiment Video

Updated: May 1, 2026

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance
04:58

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

3.7K

Cracking down on cost outliers.

Jill E Sackman, Levi Citrin

    Healthcare Financial Management : Journal of the Healthcare Financial Management Association
    |April 8, 2014
    PubMed
    Summary
    This summary is machine-generated.

    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.

    More Related Videos

    Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
    14:06

    Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

    Published on: June 23, 2012

    16.5K
    Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets
    06:40

    Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

    Published on: February 23, 2024

    1.9K

    Related Experiment Videos

    Last Updated: May 1, 2026

    Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance
    04:58

    Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

    Published on: December 13, 2024

    3.7K
    Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
    14:06

    Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

    Published on: June 23, 2012

    16.5K
    Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets
    06:40

    Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

    Published on: February 23, 2024

    1.9K

    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.