2017 ICT4D Conference

Community-based Management of Acute Malnutrition (CMAM) is a proven approach in the treatment of acute malnutrition. However, its effectiveness is undermined by poor adherence to clinical protocols, inaccurate record keeping and weak supervision systems. Currently, paper-based systems are used for patient records, summary reporting and stock management. A CommCare mobile application (app) for CMAM was developed to provide health workers with case management tools and job aids, such as response-triggered decision tree algorithms, automated referral initiating and tracking, integrated media to support counselling, and automated reporting. Development of application specifications was initiated in 2013 based on Chad national protocol. The app was contextualized and deployed in Afghanistan over a 2 year period (2014-2016), and a 2-year pilot project was launched in Mali, Chad, Niger, and Kenya in 2014. Final evaluations of the 4-country pilot were conducted in 2016 to assess the effect of the app on protocol adherence, monitoring, reporting, supply management, user acceptability and competence. Significant improvements in CMAM programming were noted in the pilot: improved data completeness and protocol adherence; improved defaulter/absentee tracing; and good acceptance of the app by beneficiary and health workers. Key lessons included: developing a single set of ‘global specifications’ for a CMAM mHealth application was not feasible given the notable differences between national protocols; country contextualization was complex and time consuming; field testing and re-testing with end users was vital; working with a technology partner who can accommodate flexibility in timelines and works in fragile contexts would be beneficial, including the provision of in-country support and timely remote support; weak CMAM services in some countries affected uptake of the app. Future priorities include linking the app to national HMIS systems and development of monitoring and reporting standards based on individual child level data.