ArticleClinical transplantation2026
Using Natural Language Processing to Characterize Early Steps in the Kidney Transplant Evaluation Process Documented in the National Veterans Affairs Electronic Health Record.
Article in Clinical transplantation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Abstract
backgroundEfforts to identify barriers and improve access to kidney transplantation in the United States are limited by a lack of population-level data on early steps in the transplant evaluation process.
methodsWe used a rule-based natural language processing (NLP) approach with clinical notes in the US Veterans Affairs Healthcare System (VA) electronic health record (EHR) and linkage with the United States Renal Data System registry to characterize sequential steps in the kidney transplant evaluation process. Adults with advanced kidney disease (estimated glomerular filtration rate ≤20 mL/min/1.73m
resultsAmong 45,174 cohort members, the median age was 71 (IQR 64, 80) years, and 97.2% were men. There was documentation of kidney transplant being mentioned as a treatment option for 46.3% of cohort members, 28.2% engaged in some type of evaluation for transplant, and 8.4% were referred to and 5.4% evaluated at a VA kidney transplant center. 6.9% of cohort members were added to the national deceased donor waitlist and 3.1% received a kidney transplant. Compared with events identified through EHR chart search and manual review by two clinicians, NLP identified events within 90 days with a precision of 0.82-0.94 and recall of 0.56-0.89.
conclusionThese results illuminate the substantial proportion of patients who engage in early steps in the kidney transplant evaluation process. The work also demonstrates that NLP can accurately identify these key steps in the process as documented in patients' EHRs.
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