Meghna Jani

Warren Del-Pinto, George Demetriou, Meghna Jani et al.

Coding of unstructured clinical free-text to produce interoperable structured data is essential to improve direct care, support clinical communication and to enable clinical research.However, manual clinical coding is difficult and time consuming, which motivates the development and use of natural language processing for automated coding. This work evaluates the quality and consistency of both manual and automated clinical coding of diagnoses from hospital outpatient letters. Using 100 randomly selected letters, two human clinicians performed coding of diagnosis lists to SNOMED CT. Automated coding was also performed using IMO's Concept Tagger. A gold standard was constructed by a panel of clinicians from a subset of the annotated diagnoses. This was used to evaluate the quality and consistency of both manual and automated coding via (1) a distance-based metric, treating SNOMED CT as a graph, and (2) a qualitative metric agreed upon by the panel of clinicians. Correlation between the two metrics was also evaluated. Comparing human and computer-generated codes to the gold standard, the results indicate that humans slightly out-performed automated coding, while both performed notably better when there was only a single diagnosis contained in the free-text description. Automated coding was considered acceptable by the panel of clinicians in approximately 90% of cases.

Yusuf Yildiz, Goran Nenadic, Meghna Jani et al.

Objective: Large language models (LLMs) are attracting increasing interest in healthcare. This commentary evaluates the potential of LLMs to improve clinical prediction models (CPMs) for diagnostic and prognostic tasks, with a focus on their ability to process longitudinal electronic health record (EHR) data. Findings: LLMs show promise in handling multimodal and longitudinal EHR data and can support multi-outcome predictions for diverse health conditions. However, methodological, validation, infrastructural, and regulatory chal- lenges remain. These include inadequate methods for time-to-event modelling, poor calibration of predictions, limited external validation, and bias affecting underrepresented groups. High infrastructure costs and the absence of clear regulatory frameworks further prevent adoption. Implications: Further work and interdisciplinary collaboration are needed to support equitable and effective integra- tion into the clinical prediction. Developing temporally aware, fair, and explainable models should be a priority focus for transforming clinical prediction workflow.