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

Interpreting Run Charts01:25

Interpreting Run Charts

4.2K
Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
4.2K
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

897
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
897
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

367
Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
367
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

520
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
520
Regression Toward the Mean01:52

Regression Toward the Mean

7.3K
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...
7.3K
Clinical Trials01:16

Clinical Trials

11.1K
Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
11.1K

You might also read

Related Articles

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

Sort by
Same author

Virulence of Burkholderia pseudomallei Strains from Western Hemisphere and Africa in Mice.

Emerging infectious diseases·2026
Same author

Vascular-associated bacterial burden and neuroinflammatory transcriptional responses observed in models of pneumonic plague.

Frontiers in microbiology·2026
Same author

Characterization of an isogenic <i>bimA</i> mutant in the ATS2021 strain of <i>Burkholderia pseudomallei</i>.

Infection and immunity·2026
Same author

Buprenorphine extended-release (Ethiqa XR) impacts the immunological response in mice exposed to aerosolized <i>Burkholderia pseudomallei</i> or <i>Yersinia pestis</i>.

Frontiers in immunology·2026
Same author

Recommendations made and accepted by a telehealth-enabled Antimicrobial Stewardship program implemented at rural veterans affairs medical centers.

Infection control and hospital epidemiology·2026
Same author

Exploring Goal-Concordant Medication Use Among VA Community Living Center Residents With Dementia.

Journal of the American Geriatrics Society·2025

Related Experiment Video

Updated: Mar 17, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.9K

Large-Scale No-Show Patterns and Distributions for Clinic Operational Research.

Michael L Davies1, Rachel M Goffman2, Jerrold H May3

  • 1Access and Clinic Administration Program (ACAP), U.S. Department of Veterans Affairs, Washington, DC 57741, USA. michael.davies@va.gov.

Healthcare (Basel, Switzerland)
|July 16, 2016
PubMed
Summary

Male patients and younger individuals exhibit higher primary care appointment no-show rates, particularly as appointment age increases. These patterns vary by age and gender within the Veterans Health Administration (VHA).

Keywords:
frequent attendersno-showsoutpatient appointmentsstatistical analysis

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.4K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.7K

Related Experiment Videos

Last Updated: Mar 17, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.9K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.4K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.7K

Area of Science:

  • Health Services Research
  • Healthcare Management
  • Patient Access and Engagement

Background:

  • Patient no-shows for primary care appointments are a significant issue, negatively impacting care quality, access, provider productivity, and increasing costs.
  • Understanding variations in no-show rates based on patient demographics and appointment characteristics is crucial for developing targeted interventions.

Purpose of the Study:

  • To describe patterns of no-show variation by patient age, gender, appointment age, and type of appointment request.
  • To analyze these patterns across six service lines within the United States Veterans Health Administration (VHA).

Main Methods:

  • Retrospective observational descriptive study analyzing 25,050,479 VHA appointments from FY07-FY14 for 555,183 patients.
  • Multifactor analysis of variance (ANOVA) used to examine no-show rate as the dependent variable against factors including gender, age group, appointment age, new patient status, and service line.

Main Results:

  • Males showed higher no-show rates than females until age 65; thereafter, rates were similar.
  • No-show rates generally decreased with age until 75-79, then increased.
  • Increasing appointment age correlated with higher no-show rates for males and new patients, with younger patients being particularly susceptible.

Conclusions:

  • Patient age and gender significantly influence primary care appointment no-show rates, with distinct patterns observed across different age groups and appointment ages.
  • Findings offer valuable insights for healthcare practitioners and management scientists to better characterize no-show behaviors and inform targeted interventions.
  • Further research on general population data is needed to determine the generalizability of these VHA-specific findings.