OBJECTIVE

To determine the perceptions of persons with tuberculosis (TB) and health workers on Care TB – a mobile application for the community-led monitoring (CLM) of TB services.

METHODS

We used a qualitative research method. Six people with tuberculosis and ten health workers were chosen through purposive sampling for semi-structured interviews. The narrative data produced from the interviews were subjected to qualitative content analysis in order to uncover salient themes and patterns.

RESULTS

The community-led monitoring mobile application was shown to be acceptable both to TB healthcare providers and patients. It enhances information access and streamlines the process of reporting care barriers. The application also allows persons with TB to interact with one another, potentially eliminating stigma and discrimination. Potential challenges to implementing the CLM program include issues with internet connectivity, costs, and human resources.

CONCLUSION

This study provides preliminary evidence of the acceptability and perceived feasibility of a mobile application for the community-led monitoring of TB services. For the CLM initiative to be scaled up across the country, more financial and technical support is required.

Tuberculosis ; Patient Acceptance Of Health Care ; Human Rights ; Social Stigma ; Social Discrimination

Background and Objectives: The Philippines faces challenges in the screening of tuberculosis (TB), one of them being the shortage in the health workforce who are skilled and allowed to screen TB. Deep learning neural networks (DLNNs) have shown potential in the TB screening process utilizing chest radiographs (CXRs). However, local studies on AIbased TB screening are limited. This study evaluated qXR3.0 technology's diagnostic performance for TB screening in Filipino adults aged 15 and older. Specifically, we evaluated the specificity and sensitivity of qXR3.0 compared to radiologists' impressions and determined whether it meets the World Health Organization (WHO) standards. Methods: A prospective cohort design was used to perform a study on comparing screening and diagnostic accuracies of qXR3.0 and two radiologist gradings in accordance with the Standards for Reporting Diagnostic Accuracy (STARD). Subjects from two clinics in Metro Manila which had qXR 3.0 seeking consultation at the time of study were invited to participate to have CXRs and sputum collected. Radiologists' and qXR3.0 readings and impressions were compared with respect to the reference standard Xpert MTB/RiF assay. Diagnostic accuracy measures were calculated. Results: With 82 participants, qXR3.0 demonstrated 100% sensitivity and 72.7% specificity with respect to the reference standard. There was a strong agreement between qXR3.0 and radiologists' readings as exhibited by the 0.7895 (between qXR 3.0 and CXRs read by at least one radiologist), 0.9362 (qXR 3.0 and CXRs read by both radiologists), and 0.9403 (qXR 3.0 and CXRs read as not suggestive of TB by at least one radiologist) concordance indices. Conclusions qXR3.0 demonstrated high sensitivity to identify presence of TB among patients, and meets the WHO standard of at least 70% specificity for detecting true TB infection. This shows an immense potential for the tool to supplement the shortage of radiologists for TB screening in the country. Future research directions may consider larger sample sizes to confirm these findings and explore the economic value of mainstream adoption of qXR 3.0 for TB screening.
Tuberculosis ; Diagnostic Imaging ; Deep Learning