The answer, published in BMC Health Services Research, is a qualified yes, with important caveats about what happens after a positive test. Benjamin Sloan, Annie N. Cowell, Christina Schultz, Amy Sitapati, Marie-Reine Lauer, Ryan Moran and colleagues implemented an EHR-based algorithm to identify patients at risk of latent tuberculosis infection, or LTBI, using discrete data elements that trigger an Epic Health Maintenance topic flag. The Health Maintenance topic is a structured, patient-specific EHR feature used to document and track completion of recommended preventive care. In other words, rather than relying on clinicians to remember which patients need TB risk assessments, the system itself flags the recommendation, embedding it in the same preventive-care machinery that reminds providers about vaccines and cancer screening.

The technical architecture matters because LTBI screening is fundamentally a data problem. Risk factors for LTBI, such as birth or travel in high-prevalence countries, known exposure to active TB, immunosuppression, and homelessness or incarceration, are typically recorded in the chart as structured fields: demographic entries, problem-list items, social history elements. The UC San Diego team built staff- and clinician-centered workflows on top of these discrete data elements so that risk assessment determination could be completed during routine encounters. The algorithm does not diagnose LTBI; it identifies candidates for risk assessment and, where risk factors are confirmed, for Interferon-Gamma Release Assay testing, the modern blood-based diagnostic that has largely replaced the older tuberculin skin test in primary care.

To evaluate whether the algorithm actually changed behavior, the researchers conducted a retrospective quasi-experimental study tracking monthly QuantiFERON, or QFT, orders before and after the Health Maintenance topic went live in June 2025. The statistical approach was interrupted time series segmented regression over 47 months, from January 2022 through November 2025, a method that models the baseline trend, the immediate level change at intervention, and the post-intervention slope separately. Because seasonal patterns can distort such comparisons, the team also ran a seasonally matched sensitivity analysis comparing June through November 2025 against the same months in 2022 through 2024.

The results were striking. The interrupted time series analysis demonstrated a significant immediate increase of 547.42 additional QFT orders at implementation, with a p-value below 0.001. That is, in the month the algorithm switched on, testing volume jumped by roughly 550 orders, an effect far too large to attribute to chance. The post-intervention slope subsequently declined by 103.17 orders per month, p = 0.006, suggesting the initial surge partially receded as the backlog of flagged patients was worked through. The seasonally matched comparison confirmed significantly higher mean monthly QFT orders in June through November 2025, at 2055.17, versus 1795.33 in the same months of 2022 through 2024, a mean difference of 259.83 orders with a 95 percent confidence interval of 1.19 to 518.48 and p = 0.049. The effect size, Cohen’s d of 1.35, indicates a large difference by conventional statistical standards.

Over five months of post-implementation monitoring, more than 13,000 patients were identified by the algorithm, and QFT positivity rates ran at 10 percent. That positivity rate is notable: it suggests the algorithm is not merely casting a wide net but flagging patients whose risk profiles genuinely correlate with infection. In a low-prevalence setting, indiscriminate testing would yield mostly negative results; a 10 percent yield among flagged patients indicates meaningful enrichment, the kind of signal that makes population screening economically and clinically defensible.

But the study’s most sobering findings lie downstream of the blood draw. The researchers conducted a structured chart review of QFT-positive patients to evaluate care cascade outcomes, including chest radiography completion and LTBI treatment initiation. Among 103 QFT-positive patients, 75, or 72.8 percent, had chest radiography ordered. Of those, only 50, or 66.7 percent, had a documented imaging result available. And among patients with normal imaging, the standard prerequisite for ruling out active disease before starting preventive therapy, only 23.3 percent initiated LTBI treatment. The attrition is severe at every step: roughly one in four positive patients never got a chest image ordered, a third of those ordered never produced a documented result, and fewer than one in four eligible patients actually started the antibiotics that would eliminate their infection risk.

This cascade gap is where the study’s implications become sharpest for TB elimination strategy. Latent tuberculosis infection is asymptomatic, so treatment offers no immediate benefit the patient can feel; it requires months of medication, sometimes with side effects, for a benefit that is entirely probabilistic and deferred. Public health frameworks have long recognized that screening without treatment completion does little to reduce future TB cases, since the bacterium remains in the population. The UC San Diego findings highlight that the EHR algorithm succeeded at the front end, converting a mandate into testing volume, but that diagnostic follow-up and treatment initiation remain the persistent barriers. The authors point to low primary-care encounter ordering rates and incomplete follow-up of positive results as key gaps, framing them as opportunities for workflow optimization to advance AB 2132 compliance and regional TB elimination goals.

The technical lesson for health informatics is that EHR-based screening algorithms can be built quickly and can produce measurable population-level effects, but they function best as part of a closed loop rather than a one-way flag. A Health Maintenance topic that identifies 13,000 at-risk patients and drives a 10 percent positivity rate is a triumph of data engineering; a system in which only 23 percent of eligible positive patients start treatment is a triumph of identification without completion. Closing that loop will likely require the same discrete-data discipline applied downstream: automated tracking of chest radiography orders and results, automated outreach to patients with normal imaging, and treatment initiation prompts embedded in the same preventive-care machinery that drove the testing surge. The UC San Diego team has demonstrated that the front door of TB elimination can be opened with code. The harder work, as their own cascade data show, is what happens once patients walk through it.

Subject of Research: Health Services Research

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

Article References: Sloan, B., Cowell, A. N., Schultz, C., Sitapati, A., Lauer, M.-R., & Moran, R. (2026). EHR-based algorithm for latent tuberculosis infection screening in primary care: early implementation under California AB 2132. BMC Health Services Research. https://doi.org/10.1186/s12913-026-15658-w

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15658-w

Keywords: latent tuberculosis infection, EHR algorithm, AB 2132, primary care, San Diego, QuantiFERON, interrupted time series, care cascade, health informatics, public health policy, EHR-based, algorithm