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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

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...
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
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

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...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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, controlled...

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Related Experiment Video

Updated: Jun 4, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Creating and Evaluating a Dynamic Study Randomization and Enrollment Tool within a Robust EHRs.

Nareesa A Mohammed-Rajput1, Nyoman W Ribeka, Sylvester Kimaiyo

  • 1Regenstrief Institute, Inc., Indianapolis, IN;

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|February 25, 2011
PubMed
Summary

Implementing an automated Randomization and Enrollment Tool (RET) in electronic health records (EHRs) can streamline clinical trials. However, challenges with EHR data accuracy and clinic workflows must be addressed for optimal performance.

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Last Updated: Jun 4, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

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06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Health Informatics
  • Clinical Trials Methodology
  • Global Health

Background:

  • Randomized trials are challenging in resource-limited settings.
  • Electronic Health Records (EHRs) offer potential for improving trial efficiency.
  • Automated tools can support data collection and patient enrollment.

Purpose of the Study:

  • To assess the accuracy and adequacy of a novel Randomization and Enrollment Tool (RET).
  • To evaluate RET's performance in a real-world clinical setting for HIV decision support trials.
  • To identify limitations of EHR-based randomization tools.

Main Methods:

  • An observational assessment of the RET was conducted at three Kenyan HIV clinics.
  • The RET was integrated into live EHR systems to automate enrollment and randomization.
  • Manual reviews were performed to evaluate RET's enrollment accuracy and data integrity.

Main Results:

  • The RET enrolled 327 patients, but manual review found only 250 (76.5%) were eligible due to inaccurate EHR data.
  • An additional 23 eligible patients were missed due to reliance on EHR data.
  • 5.5% of enrolled patients did not receive the intervention due to missed appointments.

Conclusions:

  • Automated randomization tools show promise for reducing costs in EHR-based randomized trials.
  • Data quality issues within EHRs pose a significant vulnerability for automated trial tools.
  • Workflow integration and data validation are critical for successful implementation of such tools.