Development and validation of the provider documentation summarization quality instrument for large language models.

Abstract

Objectives: As large language models (LLMs) are integrated into electronic health record (EHR) workflows, validated instruments are essential to evaluate their performance before implementation and as models and documentation practices evolve. Existing instruments for provider documentation quality are often unsuitable for the complexities of LLM-generated text and lack validation on real-world data. The Provider Documentation Summarization Quality Instrument (PDSQI-9) was developed to evaluate LLM-generated clinical summaries. This study aimed to validate the PDSQI-9 across key aspects of construct validity.

Materials and methods: Multi-document summaries were generated from real-world EHR data across multiple specialties using several LLMs (GPT-4o, Mixtral 8x7b, and Llama 3-8b). Validation included Pearson correlation analyses for substantive validity, factor analysis and Cronbach's α for structural validity, inter-rater reliability (ICC and Krippendorff's α) for generalizability, a semi-Delphi process for content validity, and comparisons of high- versus low-quality summaries for discriminant validity. Raters underwent standardized training to ensure consistent application of the instrument.

Results: Seven physician raters evaluated 779 summaries and answered 8329 questions, achieving over 80% power for inter-rater reliability. The PDSQI-9 demonstrated strong internal consistency (Cronbach's α = 0.879; 95% CI, 0.867-0.891) and high inter-rater reliability (ICC = 0.867; 95% CI, 0.867-0.868), supporting structural validity and generalizability. Factor analysis identified a 4-factor model explaining 58% of the variance, representing organization, clarity, accuracy, and utility. Substantive validity was supported by correlations between note length and scores for Succinct (ρ = -0.200, P = .029) and Organized (ρ = -0.190, P = .037). The semi-Delphi process ensured clinically relevant attributes, and discriminant validity distinguished high- from low-quality summaries (P<.001).

Discussion: The PDSQI-9 showed high inter-rater reliability, internal consistency, and a meaningful factor structure that reliably captured key dimensions of documentation quality. It distinguished between high- and low-quality summaries, supporting its practical utility for health systems needing an evaluation instrument for LLMs.

Conclusions: The PDSQI-9 demonstrates robust construct validity, supporting its use in clinical practice to evaluate LLM-generated summaries and facilitate safer, more effective integration of LLMs into healthcare workflows.