Related Experiment Video
Updated: May 28, 2026

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.
Introduction:
Objective Structured Clinical Examinations (OSCEs) are resource‑intensive, yet little empirical evidence exists on how specific design choices influence cost. This study evaluated the cost implications of multiple OSCE variations while examining whether these modifications affect test accuracy.
Methods:
A test accuracy and cost-minimization study was conducted using four years of OSCE data from an undergraduate pharmacy program. Variations to delivery mode (face-to-face (F2F) or online) and use of human simulated patients (yes or no) were evaluated. Test accuracy (Cronbach's alpha; standard error of measurement) was compared across delivery modes and use of simulated patients. A cost‑minimization analysis using the ingredients method modelled four OSCE variations, calculating average cost per student.
Results:
Neither delivery mode nor use of simulated patients significantly affected test accuracy. Online delivery with examiner dual role as examiner and patient was the lowest cost ($241), followed by F2F delivery with examiner dual role as examiner and patient ($339), online delivery with human simulated patients ($345), and F2F delivery with human simulated patients ($448).
Discussion:
This study highlights the potential for careful OSCE design to achieve cost savings without sacrificing test accuracy. Given the twofold difference between the lowest and highest cost variations, the cost implications are substantial. Within the context of a pharmacy program in a 'global north' country, cost-optimisation may be achieved through online delivery and having examiners perform the dual role of examining and acting as patients.
Related Concept Videos
Accuracy and Errors in Hypothesis Testing
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...
Testing a Claim about Standard Deviation
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...
Systematic Error: Methodological and Sampling 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...
Multiple Comparison Tests
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...

