Leveraging Centralized Health System Data Management and Large Language Model-Based Data Preprocessing to Identify
Fekede Asefa Kumsa1, Christopher L Brett2, Soheil Hashtarkhani1
1Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN.
Purpose:
Unplanned treatment interruptions represent an important care quality shortfall for patients undergoing cancer radiotherapy. This study aimed to evaluate use of a centralized electronic health record warehouse and large language model-based data preprocessing to facilitate identification of risk factors for radiation therapy interruptions (RTI).
Methods:
We analyzed demographic, behavioral, clinical, and neighborhood-level data for 2,130 patients treated with radiotherapy at the University of Tennessee Medical Center in Knoxville. Treatment interruptions were measured as missed days, adjusted for weekends and holidays. Multinomial logistic regression was used to identify factors associated with moderate (2-4 days) and severe (≥5 days) RTI.
Results:
Moderate RTI occurred in 15.8% of patients, while 7.7% experienced severe RTI. Moderate delays were associated with genitourinary cancer (adjusted odds ratio (AOR), 3.81; 95% CI, 1.24 to 11.66), prostate cancer (AOR, 2.44; 95% CI, 1.34 to 4.46), and Medicaid coverage (AOR, 2.22; 95% CI, 1.32 to 3.73). Severe RTI was associated with marital status (AOR for divorced or separated patients, 1.86; 95% CI, 1.18 to 2.94), head and neck cancer (AOR, 2.31; 95% CI, 1.10 to 4.87), gynecologic cancer (AOR, 2.97; 95% CI, 1.30 to 6.79), Medicaid insurance (AOR, 3.43; 95% CI, 1.77 to 6.64), daily dose of ≤225 cGy (AOR, 2.55; 95% CI, 1.21 to 5.37), and a total dose of ≥6,000 cGy (AOR, 2.30; 95% CI, 1.09 to 4.88). Severe interruptions were also significantly associated with high neighborhood social vulnerability (AOR, 2.60; 95% CI, 1.32 to 5.09).
Conclusion:
Automated data preprocessing permitted efficient identification of treatment course length, marital status, disease site, Medicaid coverage, and socially vulnerable locations as significant factors associated with RTI. These findings underscore the need for data-driven risk assessment and intervention strategies to maintain cancer treatment quality at scale.
