EHR-based algorithm for latent tuberculosis infection screening in primary care: early implementation under California AB 2132

Abstract Background Latent tuberculosis infection (LTBI) remains a major public health challenge in California, with San Diego County carrying a disproportionately high burden driven by migration patterns, social determinants of health, and persistent exposure risks. Recent legislation (California Assembly Bill (AB)2132; effective January 1, 2025) mandates primary-care settings to conduct TB risk assessments and testing for adults with identified risk factors. Beyond screening alone, gaps across the LTBI care cascade including diagnostic follow-up and treatment completion remain a persistent barrier to TB elimination. Methods We implemented an electronic health record (EHR) algorithm to identify patients with risks of LTBI using discrete data elements triggering an Epic Health Maintenance topic flag, with staff and clinician centered workflows for risk assessment determination. We then conducted a retrospective quasi-experimental study evaluating changes in monthly Interferon-Gamma Release Assay (IGRA) ordering following implementation of an EHR Health Maintenance (HM) topic in June 2025. The HM topic is a structured, patient-specific EHR feature used to document and track completion of recommended preventive care. Analyses used interrupted time series (ITS) segmented regression over 47 months (January 2022–November 2025) and a seasonally matched sensitivity analysis. We also conducted a structured chart review of QFT-positive patients to evaluate downstream care cascade outcomes, including chest radiography completion and LTBI treatment initiation. Results ITS analysis demonstrated a significant immediate increase of 547.42 additional QFT orders at implementation ( p < 0.001), despite a subsequent decline in post-intervention slope (− 103.17 orders/month, p = 0.006). Seasonally matched comparison confirmed significantly higher mean monthly QFT orders in June–November 2025 versus June–November 2022–2024 (2055.17 vs. 1795.33; mean difference 259.83; 95% CI 1.19-518.48; p = 0.049; Cohen’s d = 1.35). Over five months of post-implementation monitoring, more than 13,000 patients were identified, with QFT positivity rates of 10%. Among 103 QFT-positive patients, 75 (72.8%) had chest radiography ordered, 50 of whom (66.7%) had a documented imaging result available, while only 23.3% of patients with normal imaging initiated LTBI treatment, highlighting substantial attrition across the care cascade. Conclusions An EHR-based LTBI algorithm significantly increased QFT testing at implementation and higher ordering relative to historical baselines. Key care cascade gaps include low primary-care encounter ordering rates and incomplete follow-up of positive results, highlighting opportunities for workflow optimization to advance AB 2132 compliance and regional TB elimination goals.