GDPR Compliant Collection of Therapist-Patient-Dialogues

TL;DR

This paper discusses the challenges and procedures involved in collecting therapist-patient dialogues compliant with GDPR for NLP research, highlighting ethical, legal, and technical hurdles.

Contribution

It provides a detailed overview of the process and potential pitfalls in ethically collecting and anonymizing therapy dialogue data under GDPR for NLP applications.

Findings

Identified key challenges in GDPR-compliant data collection.

Outlined procedural steps and pitfalls in anonymization and data formatting.

Highlighted the need for further research in ethical data gathering.

Abstract

According to the Global Burden of Disease list provided by the World Health Organization (WHO), mental disorders are among the most debilitating disorders.To improve the diagnosis and the therapy effectiveness in recent years, researchers have tried to identify individual biomarkers. Gathering neurobiological data however, is costly and time-consuming. Another potential source of information, which is already part of the clinical routine, are therapist-patient dialogues. While there are some pioneering works investigating the role of language as predictors for various therapeutic parameters, for example patient-therapist alliance, there are no large-scale studies. A major obstacle to conduct these studies is the availability of sizeable datasets, which are needed to train machine learning models. While these conversations are part of the daily routine of clinicians, gathering them is usually hindered by various ethical (purpose of data usage), legal (data privacy) and technical (data formatting) limitations. Some of these limitations are particular to the domain of therapy dialogues, like the increased difficulty in anonymisation, or the transcription of the recordings. In this paper, we elaborate on the challenges we faced in starting our collection of therapist-patient dialogues in a psychiatry clinic under the General Data Privacy Regulation of the European Union with the goal to use the data for Natural Language Processing (NLP) research. We give an overview of each step in our procedure and point out the potential pitfalls to motivate further research in this field.