Cancer Symptom Control Through Enhanced EHR-facilitated Approach

Researchers at Mayo Clinic, in collaboration with healthcare professionals, have made significant progress in developing an Enhanced Electronic Health Record (EHR)-facilitated cancer symptom control approach. The Enhanced EHR-facilitated Cancer Symptom Control (E2C2) Trial, a pragmatic trial, was designed to test a collaborative care approach for managing common cancer symptoms. The study involved 50,559 patients from a large health system, with financial support from the National Cancer Institute of the NIH.

Key Takeaways:

• The E2C2 Trial compared three different approaches to determine cancer site and six strategies for identifying the presence of metastasis using EHR and cancer registry data.
• The study found that counting the two most prevalent ICD-10 cancer site diagnoses per patient detected a median of 92% of the cases identified by counting all cancer site diagnoses.
• The approach counting only the single most prevalent cancer site diagnosis identified a median of 65%, but agreement among the three approaches was very good (kappa 0.80) for most cancer sites.
• ICD and NLP methods had the highest agreement (kappa = 0.53) for identifying metastasis, and cancer registry data was available for less than half of the patients.
• The study concluded that the methods were pragmatic and may be acceptable for covariates, but likely require refinement for key dependent and independent variables.
• The researchers emphasized the feasibility of using EHR data to classify cancer site and metastasis in a large and diverse cohort of patients with common cancer symptoms.
• The study's findings have significant implications for improving cancer symptom control through enhanced EHR-facilitated approaches.

Statistics:

• 50,559 patients were included in the E2C2 cohort.
• 92% of cases were detected by counting the two most prevalent ICD-10 cancer site diagnoses per patient.
• 65% of cases were detected by counting only the single most prevalent cancer site diagnosis.
• Kappa agreement between the three approaches for most cancer sites was 0.80.
• Kappa agreement between ICD and NLP methods for identifying metastasis was 0.53.
• Cancer registry data was available for less than half of the patients (44.6%).

Sources:

• "Using Electronic Health Records to Classify Cancer Site and Metastasis." Applied Clinical Informatics, 2025; 16(03): 556-568.