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Updated: Jul 31, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Prediction accuracy and financial savings of four screening tests for a sequential test of clinical performance
E R Petrusa1, J W Hales, L Wake
1Department of Medical Education, Department of Surgery, Duke University, Durham, North Carolina, USA. petru001@mc.duke.edu
Background:
Sequential testing of clinical performance is an effective strategy to reduce cost of testing.
Purpose:
To evaluate prediction accuracy and financial savings of 4 screening tests of clinical performance.
Methods:
Screening tests were created from a 13-case examination taken by 434 medical students at 4 schools. Regression analysis determined prediction accuracy for 2 test outcomes. Financial savings were computed from published estimates.
Results:
Zero false passes were obtained with the "Total Number of Cases Passed" screening test, but it saved only 27%. Sixty-two percent savings with 5% false passes occurred with the "Classification" screening test. The "Scale" and "Mini Test" screening tests would have excused 79% and 67% examinees with 5% and 1% false passes, respectively.
Conclusions:
Prediction accuracy varies with screening test and outcome measure. Sequential testing of clinical performance can save 40% to 60% with low false pass rates. However, programs need to consider loss of information for curriculum and individual feedback relative to financial savings.
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