Related Experiment Video
Updated: Jan 28, 2026

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
Published on: December 9, 2022
Measuring 21 low-value hospital procedures: claims analysis of Australian private health insurance data (2010-2014)
Kelsey Chalmers1,2, Sallie-Anne Pearson3, Tim Badgery-Parker1,2
1Menzies Centre for Health Policy, University of Sydney School of Public Health, Sydney, New South Wales, Australia.
Objective:
To examine the prevalence, costs and trends (2010-2014) for 21 low-value inpatient procedures in a privately insured Australian patient cohort.
Design:
We developed indicators for 21 low-value procedures from evidence-based lists such as Choosing Wisely, and applied them to a claims data set of hospital admissions. We used narrow and broad indicators where multiple low-value procedure definitions exist.
Setting And Participants:
A cohort of 376 354 patients who claimed for an inpatient service from any of 13 insurance funds in calendar years 2010-2014; approximately 7% of the privately insured Australian population.
Main Outcome Measures:
Counts and proportions of low-value procedures in 2014, and relative change between 2010 and 2014. We also report both the Medicare (Australian government) and the private insurance financial contributions to these low-value admissions.
Results:
Of the 14 662 patients with admissions for at least 1 of the 21 procedures in 2014, 20.8%-32.0% were low-value using the narrow and broad indicators, respectively. Of the 21 procedures, admissions for knee arthroscopy were highest in both the volume and the proportion that were low-value (1607-2956; 44.4%-81.7%).Seven low-value procedures decreased in use between 2010 and 2014, while admissions for low-value percutaneous coronary interventions and inpatient intravitreal injections increased (51% and 8%, respectively).For this sample, we estimated 2014 Medicare contributions for admissions with low-value procedures to be between $A1.8 and $A2.9 million, and total charges between $A12.4 and $A22.7 million.
Conclusions:
The Australian federal government is currently reviewing low-value healthcare covered by Medicare and private health insurers. Estimates from this study can provide crucial baseline data and inform design and assessment of policy strategies within the Australian private healthcare sector aimed at curtailing the high volume and/or proportions of low-value procedures.
Related Concept Videos
Hospitals-II
Nurses that work in...
Hospitals-I
Testing a Claim about Mean: Known Population SD
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
Testing a Claim about Population Proportion
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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...
Testing a Claim about Mean: Unknown Population SD
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...

