A potential method to identify poor breast screening performance
conference contributionposted on 2015-11-23, 12:09 authored by Leng Dong, Yan Chen, Alastair Gale, Dev P. Chakraborty
In the UK all breast screeners undertake the PERFORMS scheme where they annually read case sets of challenging cases. From the subsequent data it is possible to identify any individual who is performing significantly lower than their peers. This can then facilitate them being offered further targeted training to improve performance. However, currently this under-performance can only be calculated once all screeners have taken part, which means the feedback can potentially take several months. To determine whether such performance outliers could usefully be identified approximately much earlier the data from the last round of the scheme were re-analysed. From the information of 283 participants, 1,000 groups of them were selected randomly for fixed group sizes varying from four to 50 individuals. After applying bootstrapping on 1,000 groups, a distribution of low performance threshold values was constructed. Then the accuracy of estimation was determined by calculating the median value and standard error of this distribution as compared with the known actual results. Data indicate that increasing sample sizes improved the estimation of the median and decreased the standard error. Using information from as few as 25 individuals allowed an approximation of the known outlier cut off value and this improved with larger sample sizes. This approach is now implemented in the PERFORMS scheme to enable individuals who have difficulties, as compared to their peers, to be identified very early after taking part which can then help them to improve their performance.
This work is partly supported by the UK National Health Service Breast Screening Programme.
- Computer Science