Abstract
ObjectivesThe secondary use of medical data contained in electronic medical records, such as hospital discharge letters, is a valuable resource for the improvement of clinical care (e.g. in terms of medication safety) or for research purposes. However, the automated processing and analysis of medical free text still poses a huge challenge to available natural language processing (NLP) systems. The aim of this study was to implement a knowledge-based best of breed approach, combining a terminology server with integrated ontology, a NLP pipeline and a rules engine.MethodsWe tested the performance of this approach in a use case. The clinical event of interest was the particular drug-disease interaction “proton-pump inhibitor [PPI] use and osteoporosis”. Cases were to be identified based on free text digital discharge letters as source of information. Automated detection was validated against a gold standard.ResultsPrecision of recognition of osteoporosis was 94.19%, and recall was 97.45%. PPIs were detected with 100% precision and 97.97% recall. The F-score for the detection of the given drug-disease-interaction was 96,13%.ConclusionWe could show that our approach of combining a NLP pipeline, a terminology server, and a rules engine for the purpose of automated detection of clinical events such as drug-disease interactions from free text digital hospital discharge letters was effective. There is huge potential for the implementation in clinical and research contexts, as this approach enables analyses of very high numbers of medical free text documents within a short time period.
Highlights
Increasing patient numbers and ever-shorter length of hospital stays, as well as growing multimorbidity and polypharmacy call for information technology solutions to achieve considerable improvements in the quality and efficiency of health care, especially with regard to the medication process
pump inhibitors (PPI) were detected with 100% precision and 97.97% recall
Comprehensive medical information pertaining to a given patient are usually available in electronic medical records (EMR)
Summary
Increasing patient numbers and ever-shorter length of hospital stays, as well as growing multimorbidity and polypharmacy call for information technology solutions to achieve considerable improvements in the quality and efficiency of health care, especially with regard to the medication process. Comprehensive medical information pertaining to a given patient are usually available in electronic medical records (EMR). These data, such as medical history, exam results, physician notes, and in particular hospital discharge letters, contain high-quality information, and are a valuable resource which could be utilized to improve the quality of care (e.g. in terms of care quality assessment, disease surveillance, and adverse event detection), and for research purposes. What is missing is high-performing systems that can process, read and analyze medical free text documents in a highly automated manner
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