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Knowledge graphs based on meta-analysis papers improve the quality of case formulation: a mixed methods design.

K. Yokotani,Yasumitsu Jikihara,Kohei Koiwa

2025 · DOI: 10.2196/76808
JMIR Formative Research · 0 Citations

TLDR

It is demonstrated that providing a knowledge graph based on the meta-analytic literature can enhance CF quality and indicates that providing knowledge graphs to novice therapists increases the correctness, completeness, and feasibility of CF.

Abstract

BACKGROUND

Case formulation (CF) is a core skill for therapists; however, creating high-quality CF requires considerable time.

OBJECTIVE

This study demonstrates that providing a knowledge graph based on the meta-analytic literature can enhance CF quality.

METHODS

Five groups were established, including four large language model (LLM) groups and one human expert group, each generating 25 CFs based on 25 vignettes. The Control group with Claude Sonnet 3.7 produced 25 CFs. The Personalization group served as the control group with additional personalization prompts. The Knowledge Graph group employed an LLM that generated 25 CFs, which was provided with a meta-analysis Knowledge Graph. Further incorporation of additional personalization prompts then comprised the Knowledge Graph with Personalization group. Finally, the Expert Group consisted of 25 CFs generated by a human expert. These 125 CFs in total were evaluated for general quality (i.e., correctness, completeness, feasibility, and consistency) using a 7-point scale and 18 essential elements with binary scores (0 or 1) by another human expert. The CFs were also qualitatively analyzed.

RESULTS

The Knowledge Graph and Knowledge Graph with Personalization groups scored significantly higher than the control group in terms of correctness, completeness, and feasibility. The Expert group scored significantly higher on consistency than the machine-generated groups. Additionally, there was no significant difference in the feasibility scores between the Knowledge Graph, Knowledge Graph with Personalization, and expert groups. The qualitative evaluation suggested that human CFs narrow the text to content that is easy for the client to read, whereas machine CFs are more likely to include expressions that are unnatural to the client.

CONCLUSIONS

These results indicate that providing knowledge graphs to novice therapists increases the correctness, completeness, and feasibility of CF. Providing experienced therapists with knowledge graphs is suggested to improve the quality of their CF and mental health services.

CLINICALTRIAL

None.