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The Chulalongkorn-Hasanuddin Rifampicin-Resistant Tuberculosis Screening Tool (CUHAS-ROBUST) is an artificial intelligence–based (AI–based) application for rifampicin-resistant tuberculosis (RR-TB) screening. This study aims to elaborate on the drug-resistant TB (DR-TB) problem and the impact of CUHAS-ROBUST implementation on RR-TB screening.

A qualitative approach with content analysis was performed from September 2020 to October 2020. Medical staff from the primary care center were invited online for application trials and in-depth video call interviews. Transcripts were derived as a data source. An inductive thematic data saturation technique was conducted. Descriptive data of participants, user experience and the impact on the health service were summarized

A total of 33 participants were selected from eight major islands in Indonesia. The findings show that DR-TB is a new threat, and its diagnosis faces obstacles particularly prolonged waiting time and inevitable delayed treatment. Despite overcoming the RR-TB screening problems with fast prediction, the dubious screening performance, and the reliability of data collection for input parameters were the main concerns of CUHAS-ROBUST. Nevertheless, this application increases the confidence in decision-making, promotes medical procedure compliance, active surveillance and enhancing a low-cost screening approach.

The CUHAS-ROBUST achieved its purpose as a tool for clinical decision-making in RR-TB screening. Moreover, this study demonstrates AI roles in enhancing health-care quality and boost public health efforts against tuberculosis.

Underreporting of new tuberculosis (TB) cases is one of the main problems in TB control, particularly in countries with high incidence and dominating role of a private sector in TB cases diagnosing. The purpose of this paper was to explore behavioral determinants of underreporting of new TB cases among private sector physicians in Iran.

The authors conducted a population-based, cross-sectional study of physicians working in private clinics. The data collection tool was designed using the theory of planned behavior (TPB). The authors used structural equation models with maximum likelihood estimation to examine attitude toward the notification behavior.

Of 519 physicians, 433 physicians completed the questionnaire. Attitude toward notification had the highest score (mean score = 87.65; sd = 6.79; range: 0–100). The effect of perceived behavioral controls on the notification behavior ((β^) = 0.13; CI: 0.01–0.25) was stronger than the total effect of attitude ((β^) = 0.06; CI: 0.00–0.12) and subjective norms ((β^) = 0.01; CI: −0.00–0.03) on the behavior. However, the attitude was the main predictor of intention and justified 46% of the intention variance. Intention had a significant effect on the behavior ((ß^) = 0.09; CI: 0.1–0.16).

Considering stronger effect of perceived behavioral control on the behavior, interventions aiming at facilitating notification process would be more effective than those aiming at changing the attitude or enhancing intention among physicians.

To the best of our knowledge, no other study previously explored determinants of underreporting from the behavioral and cognitive perspective. Specifically, the authors explored the role of the TPB constructs in predicting intention to notify new TB cases.