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

Prevalence and Incidence01:08

Prevalence and Incidence

2.3K
In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
2.3K
Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

7.0K
Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
7.0K
Margin of Error01:27

Margin of Error

8.0K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
8.0K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

4.1K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
4.1K
Odds Ratio01:09

Odds Ratio

2.2K
The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
2.2K
Infectious Diseases and Their Occurrence01:28

Infectious Diseases and Their Occurrence

31
Infectious diseases appear in populations through various transmission patterns, influenced by pathogen characteristics, population immunity, environmental conditions, and social behavior. Understanding these patterns is essential for effective public health surveillance and intervention. These categories—sporadic, outbreak, epidemic, pandemic, and endemic—help frame the nature and scope of disease events.Sporadic diseases occur irregularly and infrequently, without a predictable...
31

You might also read

Related Articles

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

Sort by
Same author

BRIDGE: benchmarking large language models for understanding real-world clinical practice texts.

Nature biomedical engineering·2026
Same author

Clinical validation of an HPV whole-genome sequencing assay for MRD detection in patients with HPV+ head and neck cancer treated with surgery.

Science translational medicine·2026
Same author

What Utah's clinical AI sandbox reveals about independent oversight.

Nature medicine·2026
Same author

Coconstructing CHAMP, an Artificial Intelligence Chatbot for Pediatric Infectious Symptoms Management: Protocol for a Multiphase Participatory Study.

JMIR research protocols·2026
Same author

Can AI Say "I Don't Know"?

The New England journal of medicine·2026
Same author

<i>SHORTKIT-ML</i>: A UNIFIED MULTI-PERSPECTIVE FRAMEWORK FOR DETECTING SHORTCUT LEARNING IN MEDICAL IMAGING EMBEDDINGS.

medRxiv : the preprint server for health sciences·2026
Same journal

A digital marker for stratifying cardiovascular metabolic comorbidities among the middle-aged and elderly adults.

PLOS digital health·2026
Same journal

User engagement in the tuberculosis treatment support tools intervention and its impact on treatment outcomes: A secondary analysis of a pragmatic trial.

PLOS digital health·2026
Same journal

Machine learning for risk stratification of hypertensive disorders of pregnancy: Enhancing clinical efficiency in low-resource antenatal care in Tanzania.

PLOS digital health·2026
Same journal

The trust in AI-generated health advice (TAIGHA) scale and short version (TAIGHA-S): Development and validation study.

PLOS digital health·2026
Same journal

Time-series prediction of adverse birth outcomes in the U.S. using multilayer perceptron neural networks.

PLOS digital health·2026
Same journal

Exploring attitudes and acceptance of artificial intelligence in multiple sclerosis from the patient perspective.

PLOS digital health·2026
See all related articles

Related Experiment Video

Updated: Mar 25, 2026

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs
07:13

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs

Published on: April 9, 2021

4.7K

A problem of Epic proportion.

Rawan Abulibdeh1, Matthew G Crowson2,3, Molly J Douglas4

  • 1Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada.

PLOS Digital Health
|March 23, 2026
PubMed
Summary
This summary is machine-generated.

Epic Systems dominates the U.S. electronic health record market, raising antitrust concerns. Reforms are proposed to ensure digital health infrastructure serves the public good and fosters innovation.

More Related Videos

High-throughput Detection Method for Influenza Virus
10:05

High-throughput Detection Method for Influenza Virus

Published on: February 4, 2012

26.9K
Estimating Virus Production Rates in Aquatic Systems
10:49

Estimating Virus Production Rates in Aquatic Systems

Published on: September 22, 2010

13.1K

Related Experiment Videos

Last Updated: Mar 25, 2026

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs
07:13

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs

Published on: April 9, 2021

4.7K
High-throughput Detection Method for Influenza Virus
10:05

High-throughput Detection Method for Influenza Virus

Published on: February 4, 2012

26.9K
Estimating Virus Production Rates in Aquatic Systems
10:49

Estimating Virus Production Rates in Aquatic Systems

Published on: September 22, 2010

13.1K

Area of Science:

  • Health Informatics
  • Health Economics
  • Antitrust Law

Background:

  • Epic Systems holds a dominant market share in U.S. electronic health records (EHRs).
  • The company's market power has grown significantly, controlling a substantial portion of acute care hospitals and beds.
  • This concentration raises concerns about competition and governance in healthcare IT.

Purpose of the Study:

  • To investigate Epic Systems' rise to dominance in the EHR market.
  • To analyze the mechanisms contributing to its market entrenchment.
  • To examine the implications of this market concentration and propose reforms.

Main Methods:

  • Analysis of peer-reviewed literature, federal antitrust filings, and cross-national case studies.
  • Quantitative analysis of U.S. hospital EHR market concentration trends (Herfindahl-Hirschman Index).
  • Examination of reinforcing mechanisms, standard practices, and alleged anti-competitive behaviors.

Main Results:

  • The U.S. EHR market has become highly concentrated post-2018, with Epic capturing a majority of new contracts.
  • Dominance is reinforced by network effects, high switching costs, bundling, and workforce restrictions.
  • International deployment failures suggest market structure, not solely technological superiority, drives Epic's success.

Conclusions:

  • Current EHR market concentration poses significant governance challenges.
  • Regulatory approaches in the EU, like the European Health Data Space, aim to prevent monopolization through mandatory interoperability and testing.
  • Reforms including antitrust enforcement, structural separation, and public utility models are proposed to govern EHRs as essential public health infrastructure.