Mental Disorder
Conditions
Keywords
In silico Psychotherapy, Artificial intelligence (AI) applications, Motivational Interviewing
Brief summary
The study assesses the potential of using computational models, specifically large language models, to simulate psychotherapeutic sessions, aiming to improve therapy outcomes and advance therapist training through innovative technology.
Detailed description
Health research has evolved significantly, increasingly incorporating computational models that improve our understanding and effectiveness of medical interventions. This shift from traditional to computational methods represents a major advancement in medical research, offering a more sustainable and innovative approach for conceptual advances and therapeutic discovery. In silico models, based on scientific simulation, use computational algorithms to mimic real-world systems or processes. This virtual environment allows researchers to explore phenomena impractical, unethical, dangerous, expensive, or impossible to study otherwise. Psychotherapy is widely acknowledged as a primary treatment for a variety of mental health conditions, from depression and anxiety to personality disorders, offering significant pathways to recovery and improved quality of life. Yet current methods have shown limited effectiveness, prompting a need for innovative research approaches. In silico psychotherapy research leverages computational simulations, large language models (LLMs), and generative artificial intelligence to explore and refine psychotherapeutic interventions. By simulating human-like conversations, this approach provides insights into therapy dynamics and holds promise for revolutionizing therapist training and expanding treatment techniques. This study aims to establish a proof-of-concept for simulating psychotherapeutic sessions using LLMs, focusing specifically on motivational interviewing. It involves the simulation of 512 psychotherapy sessions using LLMs as well as 8 real-world psychotherapy transcripts. By modeling human interactions, the study seeks to enhance healthcare delivery, therapist training, and personalized psychotherapy.
Interventions
BEHAVIORALHigh Levels of Common Therapeutic Factors
The therapist large language model (LLM) is designed to show high levels of empathy, warmth, and genuineness. This setup aims to create a supportive and trusting therapeutic environment to improve patient engagement. High levels of these positive factors are linked to better psychotherapy outcomes and a stronger therapist-patient relationship.
BEHAVIORALLow Levels of Common Therapeutic Factors
The therapist LLM for this group is designed to show low levels of empathy, warmth, and genuineness. This setup aims to examine how a less supportive and empathetic therapist affects psychotherapy sessions. Lower levels of these positive behaviors can lead to reduced patient engagement and a weaker therapist-patient relationship, potentially hindering therapy outcomes.
BEHAVIORALStandard motivational interviewing
Motivational interviewing techniques as applied during the sessions on which the transcripts are based.
Sponsors
University Hospital, Basel, Switzerland
University of Trier
RWTH Aachen University
University of Basel
Study design
Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
OTHER
Masking
SINGLE (Subject)
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Simulation of psychotherapy sessions of conversations between an adult person presenting with a mental or behavioral health problem and a psychotherapist using large language models and 8 real-world transcripts
Exclusion criteria
* Simulation protocols with severe simulation errors
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Simulation's Accuracy in generating Psychotherapeutic Dialogues | 12 months | Assessment of the simulation's ability to accurately produce psychotherapeutic dialogues that adhere to the principles and techniques of motivational interviewing (MI), as determined by the average global scores of the Motivational Interviewing Treatment Integrity (MITI) code 4.2. The MITI code 4.2 includes various subscales, such as empathy and MI spirit, each scored on a scale from 1 to 5, with lower scores suggesting a need for improvement in MI delivery, while higher scores reflect stronger therapeutic skills and better patient outcomes. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Number of Errors/Deviations | 12 months | The number of errors or deviations from expected psychotherapeutic practices is counted, providing a quantitative measure of simulation quality. This measure also serves as exclusion criteria from any other assessment. |
| Metric of Verbal Content (Therapist) | 12 months | Assessment of the text metrics of the therapist, based on the number of sentences, words, syllables, characters, and lexical diversity. |
| Metric of Verbal Content (Patient) | 12 months | Assessment of the text metrics of the patient, based on the number of sentences, words, syllables, characters, and lexical diversity. |
| Turn-takings | 12 months | Assessment of the turn-takings, based on the number of exchanges between the therapist and patient within a session, indicating the dynamic interaction flow. |
| Improvement of Patient | 12 months | Improvement of the patient is evaluated using an an observer-rated, circularly framed version of the Importance and Confidence Rulers, measuring the simulated patient's psychotherapeutic progress on a circular scale from 0 to 10. Lower scores indicate lower perceived importance or confidence, while higher scores suggest greater perceived importance or confidence in making the change. |
| Credibility of Patient Behavior | 12 months | The credibility of the patient's behavior is estimated using a 0 to 10 scale indicating how likely the evaluator found that the participants are real humans or simulations, offering insight into the perceived authenticity of the simulated interactions. The credibility of the 8 real-world transcripts served as a comparison baseline/benchmark for this evaluation. Lower scores indicate lower authenticity of the patient large-language model's (LLM's) simulated behavior, while higher scores suggest higher authenticity of the patient LLM's simulated behavior. |
| Credibility of Therapist Behavior | 12 months | The credibility of the therapist's behavior is estimated using a 0 to 10 scale indicating how likely the evaluator found that the participants are real humans or simulations, offering insight into the perceived authenticity of the simulated interactions. The credibility of the 8 real-world transcripts served as a comparison baseline/benchmark for this evaluation. Lower scores indicate lower authenticity of the therapist large-language model's (LLM's) simulated behavior, while higher scores suggest higher authenticity of the therapist LLM's simulated behavior. |
| Manipulation Check | 12 months | The implemented level of psychotherapeutic common factors by the therapist-LLM is approximated using the Therapist Empathy Scale (TES) as rough manipulation checks. The TES rates the therapist's ability to understand and share a patient's feelings on a scale from 1 to 7. Higher scores indicate greater empathy, reflecting a stronger connection and understanding of the patient's emotions, while lower scores suggest less empathy. |
Countries
Switzerland
Contacts
PRINCIPAL_INVESTIGATORGunther Meinlschmidt, Prof. Dr.
University Hospital and University of Basel
Outcome results
None listed