Project Lead(s): Seif Shekalaghe
Infectious disease control programs remain inefficient in many low-income countries due, in part, to a lack of accurate diagnoses at point-of-care (POC), improper clinical management and delayed, incomplete and inaccurate information available to health program managers (HPMs).
Rapid diagnostic tests (RDTs) are considered a viable approach to deliver highly accurate diagnoses of both malaria and syphilis at POC, since they have demonstrated quite acceptable clinical performance in the lab.
However, there are obstacles to the widespread implementation of this strategy, such as a lack of proper quality assurance of RDT-based programs at POC and reporting constraints, especially in remote areas of low-income countries. Current RDT practices are also prone to human error that can impact testing accuracy.
There is a need to provide an automated interpretation of RDTs to improve accuracy of diagnoses and compliance to treatment guidelines, and to facilitate data collection.
By providing real-time electronic data collection using mobile phone networks, HPMs will be able to access timely epidemiological information in the cloud service, in order to monitor the quality of RDT-based diagnosis, appropriate use of resources, and to assist in the data-driven decision-making process.
The project was conducted in Geita District, Tanzania, to test using a mobile device integrated with information services to interpret RDTs for malaria and syphilis, and to collect and transmit health information from peripheral point-of-care facilities to the district, regional and national levels in real time.
Fionet is designed to assist peripheral healthcare workers in processing and interpreting RDTs, as well as in collecting and transmitting clinical data to a cloud information service by means of available local cell phone networks.
To accomplish this, 40 healthcare workers were trained on the use of mobile devices that contain electronic survey forms easily completed through a touch‐screen interface and, at the same time, would guide RDT processing and perform automated interpretation of the results.
All information collected and a high-resolution image of the RDT are transmitted to a central database located in a cloud information service (airFio™). Data is safely stored and organized according to predetermined reports and can be accessed via a website from anywhere in the world at any time.
Healthcare workers at all government healthcare facilities participating in the pilot were able to operate the system, and collected data on over 6,000 patients in the course of eight weeks.
Public health managers were able to log into the portal and review cases uploaded, aggregated and organized in predefined reports, enabling them to make recommendations about program management, including monitoring of RDT processing in the field.
After the encouraging results of the present study, scale-up of a system such as Fionet to at least a full‐district level is warranted, to fully demonstrate the benefits of the system.
Information about this project has been widely disseminated in conference presentations.