Dynamic Checklist Generation: Adaptive Evaluation Frameworks for Evolving Clinical Note Standards

While existing works (e.g., Ben Abacha et al., 2023; Wang et al., 2024) utilize static checklists or predetermined evaluation criteria for clinical note generation, clinical guidelines and the content requirements for notes often change over time or vary across specialties. This research proposes creating an adaptive, LLM-driven checklist generator that automatically synthesizes relevant evaluation criteria from updated clinical documentation standards, specialty-specific guidelines, or even local institutional policies. The approach would continuously monitor sources such as new research publications and health authority updates, then synthesize and update checklist items in real-time. Unlike the CONSORT-NLP approach (Fan Wang et al., 2020), which targets a fixed reporting guideline, this system would flexibly adapt to changing standards and potentially flag outdated or missing checklist items in both generated and human-written notes. This could revolutionize both the automation and the relevance of checklist-based evaluation, ensuring that automated note generation stays aligned with the latest clinical and regulatory expectations.

References:

1. An Empirical Study of Clinical Note Generation from Doctor-Patient Encounters. Asma Ben Abacha, Wen-wai Yim, Yadan Fan, Thomas Lin (2023). Conference of the European Chapter of the Association for Computational Linguistics.
2. Towards Adapting Open-Source Large Language Models for Expert-Level Clinical Note Generation. Hanyin Wang, Chufan Gao, Bolun Liu, Qiping Xu, Guleid Hussein, Mohamad El Labban, Kingsley Iheasirim, H. Korsapati, Chuck Outcalt, Jimeng Sun (2024). Annual Meeting of the Association for Computational Linguistics.
3. Development and Validation of a Natural Language Processing Tool to Generate the CONSORT Reporting Checklist for Randomized Clinical Trials. Fan Wang, R. Schilsky, David Page, R. Califf, K. Cheung, Xiaofei Wang, H. Pang (2020). JAMA Network Open.