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Updated: Jul 2, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Face and content validity of a mobile delirium screening tool adapted for use in the medical setting (eDIS-MED):
Eamonn Eeles1,2,3, Oystein Tronstad4,5, Andrew Teodorczuk2,6,7,8,9
1Internal Medicine Services, The Prince Charles Hospital, Brisbane, Queensland, Australia.
Objectives:
Following a user-centred redesign and refinement process of an electronic delirium screening tool (eDIS-MED), further accuracy assessment was performed prior to anticipated testing in the clinical setting.
Methods:
Content validity of each of the existing questions was evaluated by an expert group in the domains of clarity, relevance and importance. Questions with a Content Validity Index (CVI) <0.80 were reviewed by the development group for potential revision. Items with CVI <0.70 were discarded. Next, face validity of the entirety of the tests was conducted and readability measured.
Results:
A panel of five clinical experts evaluated the test battery comprising eDIS-MED. The content validity process endorsed 61 items. The overall scale CVI was 0.92. Eighty-eight per cent of the responses with regard to question relevancy, usefulness and appropriateness were positive. The questions were deemed fifth grade level and very easy to read.
Conclusions:
A revised electronic screening tool was shown to be accurate according to an expert group. A clinical validation study is planned.
