2017 ICT4D Conference

OpenLMIS/eLMIS, an electronic system aimed at strengthening Logistics Management Information System (LMIS) in Zambia, has helped improve availability and use of data. OpenLMIS supports management of more than 1000 medicines and supplies that are used across 4 program areas in more than 2000 health facilities.

While Zambia has operated an effective paper-based logistics system for more than 10 years, improvement of accessibility, accuracy and efficiency in collection and use of health commodity logistics data was highly desired. In 2011, Zambia, in partnership with Tanzania and supported by USAID, The Rockefeller foundation, The Bill and Melinda Gates Foundation and key development partners JSI, Village Reach and PATH embarked on a journey to automate the end-to-end logistics systems. OpenLMIS initiative was born out of this coalition and has since been extensively implemented in Zambia with growing use and adoption by commodity users and managers. OpenLMIS has also been extended to the last mile through implementation of a robust transaction processing Facility Edition. eLMIS Facility Edition helps manage last mile inventory and gathers and transmits critical data.

Building on the successes of the manual LMIS, which helped the country to achieve a high level of performance, the electronic system enabled Zambia to analyze and visualize the vast amount of data generated and help meet the needs of a growing number of patients, even in the face of limited staffing levels. Following the development and implementation of eLMIS beginning 2013, there has been an increase in the use of data for decision making. Equipping health workers, supply chain managers with evidence for making informed decisions. With eLMIS, decentralization data input from the central warehouse to the Districts and health facilities has been achieved, decreasing workload at the central level by 92%. The eLMIS is the source of data for quantification and forecasting of health commodities.

In order to harness information technology to improve health outcomes, tested models should be applied.