Integrating Structured and Unstructured Data in Total Hip Arthroplasty Evaluation: A Comprehensive Analysis for Enhanced Device Safety

OBJECTIVES: The safety and effectiveness of Total Hip Arthroplasty (THA) devices necessitates continuous evaluation due to the increasing rates of osteoarthritis and a growing aging population. Limited direct clinical comparative studies between different device types, manufacturers, and procedure characteristics hinder comprehensive assessment of the implanted device and performed procedure. Real-world data sources, such as electronic health records (EHRs), provide a potential solution to extract critical device and procedure-related characteristics for in-depth comparative effectiveness studies. This study aimed to identify device and procedure characteristics of THAs and examine their influence on THA revision rates, leveraging both structured and unstructured data from EHRs.

METHODS: This retrospective cohort study leveraged data from the Arkansas Clinical Data Repository. The study cohort comprised of patients who underwent THA from 2014 to 2021. Device and procedure-related characteristics were extracted using natural language processing (NLP) from clinical notes. Random Forest models were then employed to predict THA revision outcomes using structured and unstructured data, with model performance compared using metrics such as accuracy and area under the curve (AUC).

RESULTS: Among 1,137 individuals undergoing primary Total Hip Arthroplasty (THA), 64 (5.6%) required revision. Extracting device and procedure specifics from unstructured clinical notes revealed no significant enhancement in predictive capabilities when integrated with structured data. Variables derived from the unstructured data, including manufacturer, anesthesia, and surgical approach, emerged as influential factors, despite the overall limited impact on predictive outcomes.

CONCLUSIONS: The extraction of structured data from unstructured clinical notes in an EHR THA setting was feasible. While our current study sheds light on the preliminary aspects of this research area, it merely scratches the surface of the potential utility of NLP, underscoring the need for more nuanced investigations and advanced methodologies in the realm of NLP within clinical contexts.