Abstract

International classifications are a crucial tool for various aspects of healthcare, including general medical practice, population health analysis, and research. However, coding health records can prove to be a tedious and inaccurate task, given the complexity of the classifications, limited time available, need for previous training, and dependence on professional experience. Natural language processing encompasses a range of technologies that provide insight into text data and can be utilized for various purposes, including text data classification. The aim of this research is twofold. Firstly, to develop a neural network that can analyze primary care medical records and classify the reasons for patient visits using the International Classification of Primary Care, second edition. Secondly, to compare the results of automated coding with those coded by family physicians with varying levels of experience in ICPC for benchmarking purposes. The main hypothesis is that a natural language processing-based neural network will perform comparably to a general practitioner with some experience in ICPC coding. This technology has the potential to provide value by reducing distractions and saving healthcare providers time and cognitive effort for more patient-centered activities. Furthermore, it can generate consistent data that will contribute to future research and general practice. The primary objectives of this presentation are to discuss the problems that this technology can solve, present preliminary research results, and highlight expected limitations and challenges.