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Accepted for/Published in: JMIR Formative Research

Date Submitted: May 1, 2025
Date Accepted: Mar 6, 2026
Date Submitted to PubMed: Mar 6, 2026

The final, peer-reviewed published version of this preprint can be found here:

Knowledge Graphs Based on Meta-Analysis Papers Improve the Quality of Case Formulation: Mixed Methods Design

Yokotani K, Jikihara Y, Koiwa K

Knowledge Graphs Based on Meta-Analysis Papers Improve the Quality of Case Formulation: Mixed Methods Design

JMIR Form Res 2026;10:e76808

DOI: 10.2196/76808

PMID: 42378671

PMCID: 13318205

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

  • Kenji Yokotani; 
  • Yasumitsu Jikihara; 
  • Kohei Koiwa

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. Clinical Trial: None


 Citation

Please cite as:

Yokotani K, Jikihara Y, Koiwa K

Knowledge Graphs Based on Meta-Analysis Papers Improve the Quality of Case Formulation: Mixed Methods Design

JMIR Form Res 2026;10:e76808

DOI: 10.2196/76808

PMID: 42378671

PMCID: 13318205

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