Adaptation and performance of a mobile application for early detection of cutaneous leishmaniasis

Abstract

Detection and management of neglected tropical diseases such as cutaneous leishmaniasis present unmet challenges stemming from their prevalence in remote, rural, resource constrained areas having limited access to health services. These challenges are frequently compounded by armed conflict or illicit extractive industries. The use of mobile health technologies has shown promise in such settings, yet data on outcomes in the field remain scarce. We adapted a validated prediction rule for the presumptive diagnosis of CL to create a mobile application for use by community health volunteers. We used human-centered design practices and agile development for app iteration. We tested the application in three rural areas where cutaneous leishmaniasis is endemic and an urban setting where patients seek medical attention in the municipality of Tumaco, Colombia. The application was assessed for usability, sensitivity and inter-rater reliability (kappa) when used by community health volunteers (CHV), health workers and a general practitioner, study physician. The application was readily used and understood. Among 122 screened cases with cutaneous ulcers, sensitivity to detect parasitologically proven CL was >95%. The proportion of participants with parasitologically confirmed CL was high (88%), precluding evaluation of specificity, and driving a high level of crude agreement between the app and parasitological diagnosis. The chance-adjusted agreement (kappa) varied across the components of the risk score. Time to diagnosis was reduced significantly, from 8 to 4 weeks on average when CHV conducted active case detection using the application, compared to passive case detection by health facility-based personnel. Translating a validated prediction rule to a mHealth technology has shown the potential to improve the capacity of community health workers and healthcare personnel to provide opportune care, and access to health services for underserved populations. These findings support the use of mHealth tools for NTD research and healthcare. Cutaneous leishmaniasis (CL) and other tropical diseases primarily affect people in developing and underserved parts of the world where healthcare infrastructure, resources and trained personnel are scarce. Smartphones, connected to the internet and running innovative health-applications—"apps"—have shown promise to bridge gaps in access to healthcare in even the most remote areas. Using participatory and human centered-design methods, we developed a mobile application for Android smartphones that supports community health workers to screen potential cases of CL in rural settings. We developed this application using a previously validated clinical prediction rule and tested it with patients, community workers and health workers in the municipality of Tumaco on the Pacific Coast of Colombia. Results from this study showed that the mobile app was easy to use, could detect cutaneous leishmaniasis in over 95% of cases, and reduced the time to diagnosis by half compared to passive case detection dependent on consultation at referral facilities. Overall, this work showed the value of designing health interventions with early engagement of all users, and supports the general use of mobile health applications for neglected tropical disease research and care.