TB STARR: A mobile app for Tuberculosis Screening and Tracking for Accelerated Referral and Reporting in Nigeria

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Description of Session
This session will provide an interactive demo of a recently launched mobile app and dashboard for TB screening and case notification in Nigeria that was developed in partnership with the National TB Program (NTP). Independent clinicians who are not affiliated with the NTP in Nigeria do not have proper information or tools to diagnose and report TB because paper registers and reporting forms are time-consuming and expensive to duplicate, distribute, and process at scale. This results in missed opportunities to detect and report TB cases. To address this challenge, the Sustaining Health Outcomes through the Private Sector (SHOPS) Plus project developed an Android application for TB Screening and Tracking for Accelerated Referral and Reporting (TB STARR) that can be accessed and used by any clinical provider in Nigeria. SHOPS Plus used a participatory approach to design and develop the “independent provider” (IP) user type for the TB STARR mobile app and web-based backend dashboard for program management. The IP user type of the TB STARR mobile application was launched in June 2019 and is available for free in Google Play. In this session, we will provide an interactive demo of how clinicians can benefit from the different functions of the mobile app including registration, screening, documenting patient information, tests and referrals, generating case IDs, using search functions, and reviewing personal dashboard statistics. As we present the clinicians use case, we will also demo how data is processed and presented in the web-based dashboard. We will also discuss our process for testing and data security measures. Finally, we will demo the additional functions and features of the “Network Provider” user type which adds patient communication via SMS, automated referrals and alerts, and financing functionalities for clinicians, pharmacists, drug shops, and laboratories that have existing relationships with the NTP.
Abstract ID :
GDHF91117
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Digital Health Advisor
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Abt Associates

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