Accepted for/Published in: JMIR Medical Informatics
Date Submitted: May 15, 2023
Date Accepted: Nov 24, 2023
Additional value from free text diagnoses in electronic health records
ABSTRACT
Background:
Physicians are hesitant to forgo the opportunity of entering unstructured clinical notes for structured data entry in electronic health records (EHR). Does the flexibility of free text also increase informational value?
Objective:
To compare information from unstructured text-based chief complaints harvested and processed by a natural language processing algorithm, to clinician-entered structured diagnoses as to their potential utility for automated improvement of patient workflows.
Methods:
In 11 of the 12 symptom clusters, the NLP cluster was significant in predicting hospitalization. Eight clusters remained significant even when controlling for the cluster of clinician-determined chief complaints in the model. All 12 NLP symptom clusters were significant in predicting a low ESI score, of which 9 remained significant when controlling for clinician-determined chief complaints. The correlation of NLP clusters with chief complaints was low (r = -0.04-0.6), indicating a complementarity of information.
Results:
In 11 of the 12 symptom clusters, the NLP cluster was significant in predicting hospitalization. Eight clusters remained significant even when controlling for the cluster of clinician-determined chief complaints in the model. All 12 NLP symptom clusters were significant in predicting a low ESI score, of which 9 remained significant when controlling for clinician-determined chief complaints. The correlation of NLP clusters with chief complaints was low (-0.04-0.6), indicating a complementarity of information.
Conclusions:
The NLP-derived features and clinicians’ knowledge were complementary in explaining patient outcome heterogeneity. They can provide an efficient approach to patient flow management, for example, in an emergency medicine setting. We further demonstrate the feasibility of creating extensive and precise keyword dictionaries with NLP by medical experts without requiring programming knowledge. Using the dictionary, we could classify short and unstructured clinical texts into diagnostic categories defined by the clinician. Clinical Trial: None
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