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

Reinforcement Schedules01:24

Reinforcement Schedules

504
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
504
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.5K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.5K
Data Reporting and Recording01:24

Data Reporting and Recording

5.4K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
5.4K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

44.6K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
44.6K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

38.0K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
38.0K
ATP Driven Pumps I: An Overview01:27

ATP Driven Pumps I: An Overview

9.9K
ATP-driven pumps, also known as transport ATPases, are integral membrane proteins. They have binding sites for ATP located on the membrane's cytosolic side and the ion-conducting domain in the transmembrane region. These pumps use the free energy released from ATP hydrolysis to move the solutes across cell membranes against an electrochemical gradient.
There are four main types of ATP-driven pumps - P-type, V-type, F-type, and ABC transporter. All these pumps are of varying complexities and...
9.9K

You might also read

Related Articles

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

Sort by
Same author

Adherence to Red Reflex and Vision Screening Recommendations: A Deep Dive into Primary Care Implementation Gaps.

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

Application of a Quantitative Vascular Severity Score in Retinopathy of Prematurity in the United States and India: New Insights Into Disease Epidemiology and Pathophysiology.

American journal of ophthalmology·2026
Same author

Web-Based Amblyopia Decision Support Tool.

JAMA ophthalmology·2026
Same author

Leveraging deep learning to infer continuous predictions from ordinal labels in medical imaging.

PLOS digital health·2026
Same author

Coloboma associated with VACTERL characterized by Ultra-Widefield Optical Coherence Tomography.

Retinal cases & brief reports·2026
Same author

Incidence of secondary intraocular lens implantation in school-aged children left aphakic to age 4.5 years in the Infant Aphakia Treatment Study.

Journal of cataract and refractive surgery·2026

Related Experiment Video

Updated: Feb 4, 2026

Author Spotlight: Establishing a Practical and Cost-Effective Protocol for Corneal Sensitivity Testing in Clinical Settings
04:00

Author Spotlight: Establishing a Practical and Cost-Effective Protocol for Corneal Sensitivity Testing in Clinical Settings

Published on: August 2, 2024

3.2K

Data-Driven Scheduling for Improving Patient Efficiency in Ophthalmology Clinics.

Michelle R Hribar1, Abigail E Huang1, Isaac H Goldstein2

  • 1Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon.

Ophthalmology
|October 13, 2018
PubMed
Summary

Developing an ophthalmology scheduling template using simulation models and electronic health records (EHR) data improved clinic efficiency. The new template increased patient volume and reduced wait times for pediatric ophthalmologists.

More Related Videos

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

543
Characterizing Exon Skipping Efficiency in DMD Patient Samples in Clinical Trials of Antisense Oligonucleotides
05:16

Characterizing Exon Skipping Efficiency in DMD Patient Samples in Clinical Trials of Antisense Oligonucleotides

Published on: May 7, 2020

7.3K

Related Experiment Videos

Last Updated: Feb 4, 2026

Author Spotlight: Establishing a Practical and Cost-Effective Protocol for Corneal Sensitivity Testing in Clinical Settings
04:00

Author Spotlight: Establishing a Practical and Cost-Effective Protocol for Corneal Sensitivity Testing in Clinical Settings

Published on: August 2, 2024

3.2K
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

543
Characterizing Exon Skipping Efficiency in DMD Patient Samples in Clinical Trials of Antisense Oligonucleotides
05:16

Characterizing Exon Skipping Efficiency in DMD Patient Samples in Clinical Trials of Antisense Oligonucleotides

Published on: May 7, 2020

7.3K

Area of Science:

  • Ophthalmology
  • Health Services Research
  • Medical Informatics

Background:

  • Clinic efficiency in ophthalmology is crucial for patient access and provider productivity.
  • Traditional scheduling methods may not optimize patient flow or resource utilization.
  • Electronic Health Records (EHR) offer valuable data for operational improvements.

Purpose of the Study:

  • To develop and evaluate an ophthalmology scheduling template using simulation models and EHR data.
  • To improve clinic efficiency, including patient volume and wait times.
  • To assess the generalizability of the scheduling template across multiple pediatric ophthalmologists.

Main Methods:

  • A computer simulation model was created using EHR timestamp data from a pediatric ophthalmology clinic.
  • The simulation model was used to develop a scheduling template based on predicted appointment lengths (short, medium, long).
  • The scheduling template's impact on clinic efficiency was assessed by comparing pre- and post-implementation metrics (clinic volume, wait time, examination time) in 5 practices.

Main Results:

  • EHR timestamps accurately reflected observed physician examination times, validating the data source.
  • Simulation models accurately predicted patient wait times, confirming model reliability.
  • Implementation of the new scheduling template led to statistically significant increases in clinic volume (1-3 patients/session) and improvements in patient wait times (3-4 minutes/patient) for most providers.

Conclusions:

  • Simulation models, leveraging big data from EHRs, can effectively test operational changes before real-world implementation.
  • A scheduling template that predicts appointment length enhances clinic efficiency and shows potential for broader application in ophthalmology practices.
  • EHR data holds significant potential as a tool for supporting and driving improvements in clinical operations.