Related Experiment Video
Updated: May 28, 2026
06:16
Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
Published on: August 9, 2024
How can we reduce the cost of OSCEs? A cost-minimization and test accuracy study
Angelina Lim1,2, Daniel Thomas Malone1, Zanfina Ademi1
1Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Parkville, Australia.
Medical Teacher
|May 26, 2026
Summary
Objective Structured Clinical Examinations (OSCEs) can be made more cost-effective without impacting accuracy. Online delivery with examiners playing a dual role significantly reduces costs for pharmacy programs.
Area of Science:
- Medical Education
- Health Professions Education
- Pharmacy Education
Background:
- Objective Structured Clinical Examinations (OSCEs) are essential for assessing clinical skills but are resource-intensive.
- Limited empirical data exists on how specific OSCE design choices impact overall costs.
Purpose of the Study:
- To evaluate the cost implications of various OSCE design variations.
- To determine if modifications in OSCE design affect test accuracy.
Main Methods:
- A four-year retrospective study analyzed OSCE data from an undergraduate pharmacy program.
- Compared test accuracy (Cronbach's alpha, SEM) across different delivery modes (face-to-face vs. online) and use of simulated patients (human vs. none).
- Conducted a cost-minimization analysis using the ingredients method for four OSCE variations.
Main Results:
- Neither delivery mode nor the use of simulated patients significantly impacted test accuracy.
- Online delivery with examiners in a dual role (examiner and patient) was the most cost-effective ($241/student).
- Face-to-face delivery with human simulated patients was the most expensive ($448/student).
Conclusions:
- Careful OSCE design can lead to substantial cost savings without compromising test accuracy.
- Online OSCE delivery with examiners performing a dual role offers significant cost optimization for pharmacy programs.
- These findings are particularly relevant for pharmacy programs in 'global north' countries.
Related Concept Videos
01:13Accuracy and Errors in Hypothesis Testing
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
01:19Testing a Claim about Standard Deviation
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
01:15Systematic Error: Methodological and Sampling Errors
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
01:13Multiple Comparison Tests
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...