UNLABELLED: 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.
UNLABELLED: 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.
Authors: Anoop Dinesh Shah; Nicola J Quinn; Afzal Chaudhry; Ralph Sullivan; Julian Costello; Dermot O'Riordan; Jan Hoogewerf; Martin Orton; Lorraine Foley; Helene Feger; John G Williams Journal: BMJ Health Care Inform Date: 2019-12