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Blood test reports play a critical role in screening and monitoring of diseases but are usually presented in numerical and technical systems and hence lack understanding when presented to patients and non-clinical users. This paper is a proposal of a smart parser and analyzer that converts the raw data on blood tests into structured, readable, and easy-to-use formats. A Python-based algorithm derives essential parameters of the laboratory and assesses diabetes status, glycaemic control, complete blood count abnormalities, and diabetes risk based on the accepted medical rules. The analysis based on the use of anonymized datasets reveals a similar level of classification accuracy and a better interpretation of results, which can prove the usefulness of the tool as an educational decision-support tool.