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Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
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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.
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Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

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Body: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...
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Applying A/B Testing to Clinical Decision Support: Rapid Randomized Controlled Trials.

Jonathan Austrian1,2, Felicia Mendoza3, Adam Szerencsy1,2

  • 1Department of Medicine, NYU Grossman School of Medicine, New York, NY, United States.

Journal of Medical Internet Research
|April 9, 2021
PubMed
Summary
This summary is machine-generated.

Rapid A/B testing in electronic health records (EHRs) can optimize clinical decision support (CDS) tools. This iterative approach improves usability and reduces physician burnout by refining alert design.

Keywords:
AB testingalert fatigueclinical decision supportclinical informaticsrandomized controlled trialsusability

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Area of Science:

  • Health Informatics
  • Medical Device Design
  • Implementation Science

Background:

  • Clinical decision support (CDS) in electronic health records (EHRs) aims to enhance patient care quality and safety.
  • However, complex CDS design can lead to alert fatigue and physician burnout.
  • A/B testing offers a method to rapidly improve CDS tools within EHRs.

Purpose of the Study:

  • To describe a rapid randomized controlled trial (RCT) process for evaluating and improving CDS design.
  • To enhance CDS usability, decrease alert fatigue, and promote quality of care through iterative design.
  • To demonstrate the application of this process in real-world EHR settings.

Main Methods:

  • A multistep process integrating user-centered design, A/B testing, and implementation science.
  • Rapid-cycle randomized tests (RCTs) were embedded within EHRs to compare CDS design variations.
  • CDS engagement metrics, such as alert views and acceptance rates, were used for evaluation.

Main Results:

  • An influenza alert redesign, following iterative testing, significantly reduced alert firings per patient per day (23.1 to 7.3).
  • Testing of tobacco cessation alerts showed no significant difference in acceptance rates based on message framing or image inclusion.
  • The process facilitated rapid development, deployment, and evaluation of CDS changes.

Conclusions:

  • The described process enables rapid, rigorous evaluation and improvement of CDS within EHRs.
  • Iterative A/B testing is effective for optimizing CDS design and user experience.
  • This approach holds potential for enhancing the impact and usability of CDS tools.