Natural Language Processing and Inference Rules as Strategies for Updating Problem List in an Electronic Health Record

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.