Towards integrating personalized feedback research into clinical practice: Development of the Trier Treatment Navigator (TTN).
Journal:
Behaviour research and therapy
PMID:
31301550
Abstract
In this study, a computer-based feedback, decision and clinical problem-solving system for clinical practice will be described - the Trier Treatment Navigator (TTN). The paper deals with the underlying research concepts related to personalized pre-treatment recommendations for drop-out risk and optimal treatment strategy selection as well as personalized adaptive recommendations during treatment. The development sample consisted of 1234 patients treated with cognitive behavioral therapy (CBT). Modern statistical machine learning techniques were used to develop personalized recommendations. Drop-out analyses resulted in seven significant predictors explaining 12.0% of variance. The prediction of optimal treatment strategies resulted in differential prediction models substantially improving effect sizes and reliable improvement rates. The dynamic failure boundary reliably identified patients with a higher risk for no improvement or deterioration and indicated the usage of clinical problem-solving tools in risk areas. The probability to be reliably improved for patients identified as at risk for treatment failure was about half of the probability for other patients (35% vs. 62.15%; χ = 82.77, p < .001). Results related to the computer-based feedback system are discussed with regard to the implication for clinical applications as well as clinical training and future research possibilities.
Authors
Keywords
Adjustment Disorders
Adolescent
Adult
Aged
Anxiety Disorders
Clinical Decision-Making
Cognitive Behavioral Therapy
Decision Support Systems, Clinical
Feedback
Female
Humans
Machine Learning
Male
Mental Disorders
Middle Aged
Mood Disorders
Patient Dropouts
Personality Disorders
Precision Medicine
Problem Solving
Stress Disorders, Post-Traumatic
Surveys and Questionnaires
Young Adult