Presentation (CTI)

OBJECTIVES: Survival analysis is commonly performed outside of Excel-based cost-effectiveness models (CEMs). As such, the output data are manually transferred into the CEMs, an error-prone and time-consuming process. This study’s objective was to assess the performance of an LLM-driven pipeline in automatically ingesting survival analysis outputs and integrating the datapoints into Excel-based CEMs.
METHODS: The pipeline was used to automatically insert survival analysis outputs from two trials (parameter estimates, Cholesky decompositions, knot positions, and AIC/BIC values) into an HTA-ready Excel CEM. The outputs were provided in two different, non-standardized Excel formats exported from R. The Excel CEM used a layout to facilitate ‘compression’ of the spreadsheet, optimizing for AI integration. An advanced self-consistency process flagged updates with less than 100% ‘AI-confidence’, facilitating subsequent human review. Accuracy was assessed through manual review.
RESULTS: The survival analysis outputs were integrated into the CEM without human intervention in 122, and 150 seconds, respectively. 81/82 (98.8%), and 51/51 (100.0%) of required updates were successfully performed. The single error was correctly flagged by the system as uncertain and requiring human review.
CONCLUSIONS: We found that an LLM-driven pipeline could accurately transfer survival analysis outputs from non-standardized export formats into an Excel-based CEM, which has implications for the automation and/or quality control of current manual processes. The LLM made accurate insertions over a spreadsheet area including >70,000 cells, demonstrating the power of compression as a route to integration of AI in Excel modelling. The self-consistency approach successfully flagged the single error as an uncertain action, highlighting this method as a useful tool for facilitating human-in-the-loop review of AI-driven tasks in HEOR.