Error Detection in Patients’ Pharmaceutical Data: Application of Ontology-Based Text Miner
Archives of Advances in Biosciences,
Vol. 13 No. 2 (2022),
Introduction: Medication errors in patients’ medical records can influence the healthcare quality and cause risks for them. It is, therefore, crucial to apply appropriate procedures to reduce these errors. This study sought to develop a software for detecting medication errors through qualitative analysis of patients’ medical records.
Materials and Methods: The software was developed using object-oriented analysis and Java. The text was first pre-analyzed using a framework known as Stanford Core NLP. In the next stage, the text was turned into a semi-structured passage to be connected to Dr Onontology using Apache Jena framework. The name and dosage of available drugs were then extracted in the physician order forms and the patient progress notes. The areas of mismatch were identified through comparing the data obtained from these two forms.
Results: Software assessment was conducted in two stages. In the first stage, the capability of the software in proper recognition of medicine’s name was measured, as100 completed forms containing physician order forms with a total number of 1014 drugs were used for text mining and error detection. After running the analysis in the error detection software, 93% of the drugs were properly recognized. In the next stage, comparisons were made between the physician order forms and the patient progress notes to find possible mismatches. Out of 1000 recorded drugs in the analyzed forms, the software was able to properly detect mismatches in 91.8% of the cases. The medication data available in i2b2 were used for conducting the assessment.
Conclusion: Given that medical records are of paramount importance and their human analysis is a complicated and time-consuming process, deployment of a text miner with the capability of quality analysis could facilitate error detection efficiently and effectively.
- Medical records, Medication error detection software, Qualitative analysis, Text miner
How to Cite
WHO. Manila: WHO Regional Office for the Western Pacific [Internet] 2003. Available from: http://apps.who.int/iris/handle/10665/206974
Gilligan L. HES Standards and recommended Practices for Health Care Records Management. Transmission physics and consequences for materials selection, manufacturing, and applications. J Eur Ceram Soc. 2014. p.10-12. https://www.hse.ie/ eng/about/who/qid/quality-and-patient-safety-documents/v3.pdf
Uzuner O, Solti I, Cadag E. Extracting medication extraction from clinical text. J Am Med Inform Assoc. 2010; 17(5):514-8. [DOI:10.1136/jamia.2010.003947] [PMID] [PMCID]
Huffman E. Medical record management. 9th edition. United States: Physicians Record Company;1990. https://books.google.com/books?id=tmxrAAAAMAAJ&q=Huffman+E.+Medical+record+management&dq=Huffman+E.+Medical+record+management&hl=en&sa=X&ved=2ahUKEwjaiPiE-df4AhWERPEDHYO3CowQ6AF6BAgJEAI
Karp D, Huerta JM, Dobbs CA, Dukes DL, Kenady K. Medical record Documentation for patient safety and physician defensibility. California: Medical insurance Exchange of California; 2008. file:///C:/Users/ mahlaa/Downloads/medical-record-documentation-for-patient-safety%20(1).pdf
Moghaddasi H. Information Quality in Health Care. 2th edition.Tehran: Vajehpardaz Publish company.2012.
Moghaddasi H, Rahimi F. A systemic biologic model for healthcare data quality. HIM-interchange. 2016;
(1):28-39. https://www.researchgate.net/publication/ 299598216_A_systemic_biologic_model_for_healthcare_data_quality
Gerg M. The essential nature of healthcare databases in critical care medicine. Crit Care. 2008; 12:1-2. [DOI:10.1186/cc6993]
Kushima M, Araki K, Suzuki M, Araki S, Nikama T. Text data mining of electronic Medical Record of the chronic hepatitis patient. Proc Int Multiconf Comp Sci
Inf Technol. 2012; 1:1-5. http://www.iaeng.org/ publication/IMECS2012/IMECS2012_pp569-573.pdf
Khare R, An Y, Wolf S, Nyirjesy P, Liu L, Chou E. Understanding the EMR error control practices among Gynecologic Physicians. iConference 2013 Proceedings. 2013:289-301. [DOI:10.9776/13197]
Chiaramello E, Pinciroli F, Bonalumi A, Caroli A, Tognola, G. Use of “off-the-shelf” information extraction algorithms in clinical informatics: a feasibility study of MetaMap annotation of Italian medical notes. J Biomed Inform. 2016; 63:22-32. [DOI:10.1016/j.jbi.2016.07.017]
Chen HC, Fuller S, Friedman C, Hersh W. Medical Informatics: Knowledge Management and Data Mining in Biomedicine. New York: Springer; 2005. https://link.springer.com/book/10.1007/b135955
Gurulingapa H. Mining the Medical and Patent Literature to Support Healthcare and Pharmacovigilance. (Ph.D. dissertation). Germany: University of Bonn; 2012. https://dblp.org/rec/phd/dnb/Gurulingappa12.html
Pereira L, Rijo R, Silva C, Martinho R. Text mining applied to electronic medical records: a literature review. Int. J E-Health Med Commun. 2015; 6(3):1-18. [DOI:10.4018/IJEHMC.2015070101]
Huang Z, Hu X. Disease named entity recognition by machine learning using semantic type of metathesaurus. International Int J Mach Learn Comput. 2013; 3(6):494-98. [DOI:10.7763/IJMLC.2013.V3.367]
KolaliKhormuji M, Bazrafkan M. Persian named entity recognition based with local filters. Int J Comput Appl.
; 100(4):1-6. [DOI:10.5120/17510-8062]
RxNorm. National Library of Medicine. [Internet]. Available from: http://www.nlm.nih.gov/research/umls/ rxnorm/
Jiang M, Wu Y, Shah A, Priyanka P, Denny JC, Xu H. Extracting and standardizing medication information in clinical text- the medEx-UIMA system. AMIA Jt Summits Transl Sci Proc. 2014:37-42. [PMID] [PMCID]
Bodnary A. A Medication extraction framework for electronic medical record. Massachusetts Institute Of Technology. 2013. https://www.researchgate.net/ publication/279815871_A_medication_extraction_framework_for_electronic_health_records
Doan S, Bastarache L, Klimkowski S, Denny J, Xu H. Integrating existing natural language processing tools for medication extraction from discharge summaries.
J Am Med Inform Assoc. 2010; 17(5):528-31. [DOI:10.1136/jamia.2010.003855] [PMID] [PMCID]
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