A new adaptive testing algorithm for shortening health literacy assessments

• Published in: BMC medical informatics and decision making Vol. 11; no. 1; p. 52
• Main Authors: Kandula, Sasikiran, Ancker, Jessica S, Kaufman, David R, Currie, Leanne M, Zeng-Treitler, Qing
• Format: Journal Article
• Language: English
• Published: London BioMed Central 06.08.2011; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Adult; Aged; Algorithms; Analysis; Calibration; Care and treatment; Clinical medicine; Computer literacy; Decision Theory; Educational evaluation; Educational Measurement - methods; Educational Status; Female; Health aspects; Health care; Health education; Health Informatics; Health literacy; Health Literacy - methods; Humans; Information Systems and Communication Service; Male; Management of Computing and Information Systems; Medicine; Medicine & Public Health; Methods; Middle Aged; Older people; Patients; Probability; Research Article; Studies; United States; Computerize Adaptive Testing; Calibration Scheme; Health Literacy Questionnaire; Item Response Theory; Health Literacy
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/1472-6947-11-52

Background Low health literacy has a detrimental effect on health outcomes, as well as ability to use online health resources. Good health literacy assessment tools must be brief to be adopted in practice; test development from the perspective of item-response theory requires pretesting on large participant populations. Our objective was to develop a novel classification method for developing brief assessment instruments that does not require pretesting on large numbers of research participants, and that would be suitable for computerized adaptive testing. Methods We present a new algorithm that uses principles of measurement decision theory (MDT) and Shannon's information theory. As a demonstration, we applied it to a secondary analysis of data sets from two assessment tests: a study that measured patients' familiarity with health terms (52 participants, 60 items) and a study that assessed health numeracy (165 participants, 8 items). Results In the familiarity data set, the method correctly classified 88.5% of the subjects, and the average length of test was reduced by about 50%. In the numeracy data set, for a two-class classification scheme, 96.9% of the subjects were correctly classified with a more modest reduction in test length of 35.7%; a three-class scheme correctly classified 93.8% with a 17.7% reduction in test length. Conclusions MDT-based approaches are a promising alternative to approaches based on item-response theory, and are well-suited for computerized adaptive testing in the health domain.