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Semantic Expansion of Clinician Generated Data Preferences for Automatic Patient Data Summarization
Ashutosh Jadhav1, Tyler Baldwin1, Joy Wu1
1IBM Almaden Research Center, San Jose, CA.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|March 21, 2022
Summary
This study introduces a new method to summarize patient Electronic Health Records (EHRs) by expanding clinician-selected data preferences. This approach enhances clinical information retrieval and reduces data overload for healthcare professionals.
Area of Science:
- Medical Informatics
- Biomedical Data Science
Background:
- Electronic Health Records (EHRs) generate vast amounts of data, leading to clinician information overload.
- Efficient summarization of clinically relevant patient data is crucial, particularly in fields like radiology.
Purpose of the Study:
- To develop a novel approach for curating clinician EHR data preferences.
- To enable robust EHR summarization tailored to clinician needs.
Main Methods:
- Clinicians identified data items of interest across EHR categories.
- A knowledge-driven semantic concept expansion approach was developed using UMLS biomedical knowledge.
- Expanded 1094 seed concepts to 22,325 concepts.
Main Results:
- The semantic expansion approach significantly increased the coverage of relevant clinical concepts.
- 92.69% of the expanded concepts were identified as relevant by clinicians.
- Demonstrated a scalable method for enhancing EHR data preference curation.
Conclusions:
- The developed approach effectively expands clinician-identified EHR data preferences.
- This method addresses limitations of manual data curation for improved EHR summarization.
- Semantic concept expansion shows promise for optimizing clinical information access.
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