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Automatic Placement Within a Hierarchical Clinical Decision Support Terminology
Skyler Resendez1,2, Frank LeHouillier1,2, Guresh Mehta1
1Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
Automated algorithms accurately place clinical terms in decision support terminologies, reducing clinician burden. This improves electronic health record (EHR) systems by identifying correct clinical groupers efficiently.
Area of Science:
- Informatics
- Natural Language Processing
- Clinical Decision Support
Background:
- Hierarchical terminologies are crucial for clinical decision support in electronic health record (EHR) systems.
- Automating the placement of clinical terms within these terminologies can enhance efficiency.
Purpose of the Study:
- To evaluate two algorithms for automating the assignment of clinical terms to a hierarchical decision support terminology.
- To assess the accuracy and efficiency of these automated methods in reducing clinician workload.
Main Methods:
- Utilized a feature ranking system, analogous to TF-IDF, for term placement.
- Employed a clinical Bidirectional Encoder Representations from Transformers (BERT) machine learning system.
- Tested algorithms on 100 clinical terms initially assigned a placeholder grouper.
Main Results:
- One of the two algorithms correctly identified the appropriate grouper in 93% of test cases.
- When both algorithms agreed on the top grouper, accuracy reached 97.9% for 48 cases.
- Demonstrated significant potential for reducing manual effort in terminology management.
Conclusions:
- Automated algorithms show high accuracy in assigning clinical terms to hierarchical terminologies.
- These methods can substantially decrease the burden on clinicians and improve EHR functionality.
- The study highlights the potential of NLP and machine learning in optimizing clinical decision support.
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