METHOD FOR STATISTICAL DROPOUT RISK ASSESSMENT IN PSYCHOLOGICAL THERAPY BASED ON DE-IDENTIFIED SESSION DATA
Keywords:
psychological therapy, therapy dropout risk, de-identified data, statistical analysis, z-score, anomaly detection, data privacyAbstract
The paper proposes a method for statistical assessment of the risk of premature termination of psychological therapy, operating exclusively on de-identified session data without access to clients' personal information. The relevance of the research is driven by the rapid growth of the online therapy market and the strengthening of regulatory requirements for the protection of sensitive medical data, particularly GDPR provisions that limit the application of traditional prediction methods based on clinical information. The developed Dropout Risk Score method integrates three independent behavioral signals – miss rate, duration trend, and inter-session interval z-score – into a weighted composite score with defined classification thresholds across three risk levels. A distinctive feature of the method is the use of personalized baselines instead of population norms, which provides increased sensitivity for clients with non-standard attendance patterns. Additionally, an anomaly detection method based on personalized interval analysis is described, and a system architecture with physical separation of identification and analytical layers is proposed for deploying the method under personal data protection requirements. The method does not require large training datasets and ensures interpretability of results for practitioners.
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