A case study on parsing chemotherapy related free-text data

When modelling and simulating healthcare related processes, free-text data is often the only possible source of information. This data may contain vocabulary variations such as mistyped, misspelled and/or abbreviated words. This paper describes a semi-automated approach to free-text normalisation based on a combination of commonly used techniques and local expertise of medical oncology nurses. The approach emphasises the effectiveness of the vocabulary creation process through an interactive software application. When local knowledge is successfully captured, normalisation of large data sets can be done very rapidly with a high accuracy rate achieved. Furthermore, the techniques for localised normalisation can have significant benefits to free-text parsing accuracy when data is aggregated from multiple sites (hospitals). This research may lead to increased understanding of issues associated with chemotherapy related free-text data which in turn may impact patient treatment safety.