AI Article Synopsis

  • The study analyzed how speech-language pathologists (SLPs) in the U.S. make diagnostic decisions based on child language cases by reviewing data from standardized tests and informal measures.
  • When the assessment data matched (congruent), there was unanimous agreement on diagnoses, but when data conflicted (incongruent), agreement was not reached, indicating a strong reliance on standardized tests.
  • The research highlighted three decision-making patterns: using both types of data, beginning the assessment process with a concern tool, and primarily depending on standardized tests for diagnosis, suggesting a need for further studies in real-time decision-making.

Article Abstract

Purpose The current study used a case review diagnostic assessment task to examine the diagnostic decisions speech-language pathologists (SLPs) working in the United States made after reviewing child language cases. Method Fourteen SLPs were given 5 case studies that presented either congruent or incongruent results between standardized testing and informal measures. After reviewing the assessment data, SLPs were asked to make a diagnostic decision. Results Unanimous consensus regarding diagnostic decisions was found when the assessment data were congruent. When the data were incongruent, unanimous consensus was not achieved. Standardized testing seemed to guide the diagnostic decision. This pattern of reliance on standardized testing was evident even when 80% of SLPs reviewed informal language data. In 97% of cases, a standardized test was used to guide clinical decision making. Conclusion Three patterns of clinical decision making in child language assessment emerged: (a) use of both standardized testing and informal measures, (b) a reported concern tool as a 1st step in the process, and (c) standardized testing as the most influential data for guiding diagnostic decisions. Although this study provides initial evidence regarding the process of diagnostic decision making, future studies should examine decision making in real time to further validate the implicit rules used during decision making. Supplemental Material https://doi.org/10.23641/asha.7991174.

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Source
http://dx.doi.org/10.1044/2019_LSHSS-18-0044DOI Listing

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