Hansenapp: Development of a mobile application to assist primary healthcare providers to control leprosy.

OBJECTIVE: To describe the development and validation of a mobile application to assist health professionals in the management of patients with leprosy and surveillance of contacts in primary healthcare. METHOD: A methodological and developmental study was conducted in three phases: integrative literature review, mobile application development and application validation by health professionals. The construction of the application was supported by the literature review, Nielsen's heuristics and expert validation. Five experts individually analysed the prototype draft and performed two rounds of iterations to refine their recommendations. The validation step was performed by consulting health professionals working in primary healthcare, who evaluated the application for relevance, clarity and usability using a questionnaire based on task-technology fit theory. RESULTS: The mobile app's content, navigation methods and interaction were refined based on the discussions with experts. Their recommendations were applied, and the mobile app was revised until the final version was approved. Content validity indexes of 0.94 (p = 0.007), 0.99 (p > 0.0001) and 0.93 (p = 0.01) were obtained. CONCLUSION: The developed application is a technological tool that could assist primary healthcare providers in dealing with leprosy patients and their contacts in terms of management, planning, monitoring, evaluation, treatment and follow-up, in addition to leprosy control actions.

Leprosy Screening Based on Artificial Intelligence: Development of a Cross-Platform App.

BACKGROUND: According to the World Health Organization, achieving targets for control of leprosy by 2030 will require disease elimination and interruption of transmission at the national or regional level. India and Brazil have reported the highest leprosy burden in the last few decades, revealing the need for strategies and tools to help health professionals correctly manage and control the disease. OBJECTIVE: The main objective of this study was to develop a cross-platform app for leprosy screening based on artificial intelligence (AI) with the goal of increasing accessibility of an accurate method of classifying leprosy treatment for health professionals, especially for communities further away from major diagnostic centers. Toward this end, we analyzed the quality of leprosy data in Brazil on the National Notifiable Diseases Information System (SINAN). METHODS: Leprosy data were extracted from the SINAN database, carefully cleaned, and used to build AI decision models based on the random forest algorithm to predict operational classification in paucibacillary or multibacillary leprosy. We used Python programming language to extract and clean the data, and R programming language to train and test the AI model via cross-validation. To allow broad access, we deployed the final random forest classification model in a web app via shinyApp using data available from the Brazilian Institute of Geography and Statistics and the Department of Informatics of the Unified Health System. RESULTS: We mapped the dispersion of leprosy incidence in Brazil from 2014 to 2018, and found a particularly high number of cases in central Brazil in 2014 that further increased in 2018 in the state of Mato Grosso. For some municipalities, up to 80% of cases showed some data discrepancy. Of a total of 21,047 discrepancies detected, the most common was "operational classification does not match the clinical form." After data processing, we identified a total of 77,628 cases with missing data. The sensitivity and specificity of the AI model applied for the operational classification of leprosy was 93.97% and 87.09%, respectively. CONCLUSIONS: The proposed app was able to recognize patterns in leprosy cases registered in the SINAN database and to classify new patients with paucibacillary or multibacillary leprosy, thereby reducing the probability of incorrect assignment by health centers. The collection and notification of data on leprosy in Brazil seem to lack specific validation to increase the quality of the data for implementations via AI. The AI models implemented in this work had satisfactory accuracy across Brazilian states and could be a complementary diagnosis tool, especially in remote areas with few specialist physicians.