TITLE:
Redefining Surgical Health Economics: The Potential of TabPFN for Real-Time Precision Modelling
AUTHORS:
Enoch Chi Ngai Lim, Chi Eung Danforn Lim
KEYWORDS:
Tabular Prior-Data Fitted Networks, Health Economics, Data Science, Surgery, Cost-Effectiveness Analysis, Artificial Intelligence
JOURNAL NAME:
Journal of Computer and Communications,
Vol.13 No.11,
November
12,
2025
ABSTRACT: Health economic evaluations within surgical disciplines face considerable methodological hurdles, particularly in relation to cost-effectiveness analyses (CEA) and budget-impact modelling (BIM). Conventional predictive modelling frameworks generally necessitate extensive hyperparameter optimisation and pre-processing pipelines, yet these frameworks remain poorly aligned with the limited sample sizes characteristic of surgical investigations. Tabular Prior-data Fitted Networks (TabPFN) introduce a transformer-based foundation model calibrated for tabular datasets, demonstrating state-of-the-art predictive accuracy through in-context learning and obviating the requirement for task-dedicated training epochs. This narrative review articulates TabPFN’s capacity to reshape surgical health economics by mitigating prevailing modelling constraints and by opening novel pathways for precision health economic outcomes research (P-HEOR). We first evaluate TabPFN’s methodological strengths, thereafter delineating persistent gaps in extant economic frameworks. We further delineate focused use cases, such as dynamically responsive resource distribution, patient-tailored cost-effectiveness evaluations, and iterative budget-impact simulations. While practical barriers, including regulatory impediments and heterogeneous data ecosystem integration, persist, TabPFN’s robust uncertainty quantification and superior performance in sparse datasets position it as a catalyst for methodological advancements in surgical economic evaluation. Subsequent investigations must emphasise the prototyping of surgery-tailored modules, the formulation of validation criteria, and the creation of sustainable operational pipelines to exploit TabPFN’s pedagogical strengths.