Natural language processing and inference rules as strategies for updating problem list in an electronic health record

PLAZZOTTA, FERNANDO; OTERO, CARLOS; LUNA, DANIEL; DE QUIROS, FERNAN GONZALEZ BERNALDO

Studies in Health Technology and Informatics

IOS Press

Año: 2013 vol. 192

0926-9630

Physicians do not always keep the problem list accurate, complete and updated. Objective: To analyze natural language processing (NLP) techniques and inference rules as strategies to maintain completeness and accuracy of the problem list in EHRs. Methods: Non systematic literature review in PubMed, in the last 10 years. Strategies to maintain the EHRs problem list were analyzed in two ways: inputting and removing problems from the problem list. Results: NLP and inference rules have acceptable performance for inputting problems into the problem list. No studies using these techniques for removing problems were published Conclusion: Both tools, NLP and inference rules have had acceptable results as tools for maintain the completeness and accuracy of the problem list. © 2013 IMIA and IOS Press.