Exploratory study of automated linguistic analysis for progress monitoring and outcome assessment

Objective: The most successful approach to dealing with treatment failure employs progress monitoring and outcome assessment (PMOA) measures that enable clinicians to identify clients who are failing to progress. On the basis of previous research indicating that word use relates to individuals’ psychological characteristics as well as clinical outcomes, we investigated automated linguistic analysis of client speech as an alternative PMOA approach. Methods: We employed the Linguistic Inquiry and Word Count (LIWC) program to study the language of a subset of 12 clients from the York I Depression Study. Automated analyses examined transcripts of 24 individual psychotherapy sessions, one early in treatment (T1) and one late in treatment (T2), split between six good and six poor outcome cases. Results: Good outcomes were associated with more positive emotion words and fewer past focus words and negation words at T1. Logistic regression models predicted good versus poor treatment outcome for 70–82% of transcripts. However, analyses failed to support the hypothesis that language use patterns changed from T1 to T2 during the course of therapy. Conclusions: Clinicians have been slow to adopt PMOA measures as part of routine practice, partially as a result of additional paperwork and time burdens. Automated analysis of client language provides one alternative method for decreasing client and therapist workload. This study's results, while mixed, provide evidence that additional research should be conducted to investigate the potential for LIWC and other automated measures to provide PMOA data for clinical feedback.