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

Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

1.5K
Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
1.5K
Percentile01:18

Percentile

6.4K
A percentile indicates the relative standing of a data value when data are sorted into numerical order from smallest to largest. It represents the percentages of data values that are less than or equal to the pth percentile. For example, 15% of data values are less than or equal to the 15th percentile. Low percentiles always correspond to lower data values. High percentiles always correspond to higher data values.Percentiles divide ordered data into hundredths. To score in the...
6.4K
Statgraphics01:10

Statgraphics

480
Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
480
Ratio Level of Measurement00:54

Ratio Level of Measurement

13.6K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
13.6K
Healthcare Associated Infections II: Preventive Measures01:22

Healthcare Associated Infections II: Preventive Measures

4.7K
Essential infection prevention measures are based on the knowledge of the infection chain, the modes of transmission in healthcare settings, and the use of the best practices in all healthcare settings. Compulsory public reporting of healthcare-associated infection rates is needed to allow individuals and the community to make informed choices regarding selecting a healthcare facility.
The best practices for preventing healthcare-associated infections include hand hygiene, patient risk...
4.7K
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.8K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
1.8K

You might also read

Related Articles

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

Sort by
Same author

Health care utilization in veterans with Alzheimer disease.

The American journal of managed care·2026
Same author

Identification and characterization of intracerebral hemorrhage events in elderly veterans with alzheimer's disease in the veterans affairs healthcare system.

Scientific reports·2026
Same author

Defining Expanded Episode-Based Surgical Quality Measurement.

JAMA network open·2026
Same author

Coordinating specialty care across health systems: primary care provider survey.

The American journal of managed care·2026
Same author

Systemic Manifestations and Mortality Risk in Transthyretin V142I Variant Carriers: A Million Veteran Program Analysis.

JACC. CardioOncology·2026
Same author

The Impact of Race on Survival and Treatment in Veterans Treated for Metastatic Castration-Resistant Prostate Cancer.

Journal of the National Comprehensive Cancer Network : JNCCN·2026

Related Experiment Video

Updated: Apr 22, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.0K

A probability metric for identifying high-performing facilities: an application for pay-for-performance programs.

Michael Shwartz1, Erol A Peköz, James F Burgess

  • 1*Center for Healthcare Organization and Implementation Research, VA Boston Healthcare System (152M) †School of Management ‡School of Public Health §Department of Surgery, School of Medicine, Boston University ∥Center for Healthcare Organization and Implementation Research, Bedford VA Hospital, Bedford, MA.

Medical Care
|October 12, 2014
PubMed
Summary
This summary is machine-generated.

A new probability metric better identifies high-performing healthcare facilities than traditional methods. This metric aids in more accurate resource allocation for pay-for-performance programs.

Related Experiment Videos

Last Updated: Apr 22, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.0K

Area of Science:

  • Healthcare quality measurement
  • Statistical modeling in healthcare
  • Performance profiling

Background:

  • Traditional methods for identifying high-performing facilities (e.g., top quantiles or confidence intervals) often fail to adequately distinguish high performers from average ones.
  • These binary designations can be insufficient for nuanced performance assessment.

Purpose of the Study:

  • To introduce a continuous-valued metric: the probability of a facility being in a top performance quantile.
  • To demonstrate the implications of this probability metric for facility profiling and pay-for-performance initiatives.

Main Methods:

  • A composite quality measure was developed using 2007 data from 112 Veterans Health Administration nursing homes, based on 28 quality indicators.
  • A Bayesian hierarchical multivariate normal-binomial model and Markov Chain Monte Carlo methods were employed to estimate shrunken rates for quality indicators.
  • Opportunity-based weights were used to combine indicators into a composite measure, with the probability metric derived from simulation replications.

Main Results:

  • The proposed probability metric demonstrated superior discrimination of high-performing facilities compared to point or interval estimates of the composite score.
  • In pay-for-performance scenarios, smaller top quantiles (e.g., quintiles) directed more resources to top performers, while larger quantiles allocated resources more broadly, including to average performers.

Conclusions:

  • The probability metric shows promise for enhancing healthcare facility profiling and performance assessment.
  • Further evaluation by stakeholders across diverse healthcare delivery systems is recommended to validate its utility.