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.
Health Informatics Department, Hospital Italiano de Buenos Aires, Argentina.
Fernando
F
Plazzotta
Carlos
C
Otero
Daniel
D
Luna
Fernan Gonzalez Bernaldo
FG
de Quiros
D000465:Algorithms; D001185:Artificial Intelligence; D057286:Electronic Health Records / Q000458:organization & administration*; D016247:Information Storage and Retrieval / Q000379:methods*; D008501:Medical Records, Problem-Oriented; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods / Q000592:standards; D011785:Quality Assurance, Health Care / Q000379:methods*; D014584:User-Computer Interface; D018875:Vocabulary, Controlled