The impact of an mHealth monitoring system on health care utilization by mothers and children: an evaluation using routine health information in Rwanda

Abstract

Maternal and child mortality rates remain unacceptably high globally, particularly in sub-Saharan Africa. A popular approach to counter these high rates is interventions delivered using mobile phones (mHealth). However, few mHealth interventions have been implemented nationwide and there has been little evaluation of their effectiveness, particularly at scale. Therefore, we evaluated the Rwanda RapidSMS programme-one of the few mHealth programmes in Africa that is currently operating nationwide. Using interrupted time series analysis and monthly data routinely reported by public health centres (n = 461) between 2012 and 2016, we studied the impact of RapidSMS on four indicators: completion of four antenatal care visits, deliveries in a health facility, postnatal care visits and malnutrition screening. We stratified all analyses based on whether the district received concurrent additional supports, including staff and equipment (10 out of 30 Districts). We found that community health workers in Rwanda sent more than 9.3 million messages using RapidSMS, suggesting the programme was successfully implemented. We found that the implementation of the RapidSMS system combined with additional support including training, supervision and equipment provision increased the use of maternal and child health services. In contrast, implementing the RapidSMS system alone was ineffective. This suggests that mHealth programmes alone may be insufficient to improve the use of health services. Instead, they should be considered as a part of more comprehensive interventions that provide the necessary equipment and health system capacity to support them.

Figure 1.

Average number of RapidSMS messages per 1000 catchment population in both UNICEF-supported and non-supported health centres

Figure 2.

Interrupted Times Series analysis of receipt of four standard ANC visits per 1000 catchment population in supported and non-supported Districts

Figure 3.

Interrupted Time Series analysis of facility deliveries per 1000 catchment population in supported and non-supported Districts

Figure 4.

Interrupted Time Series analysis of total PNC visits per 1000 catchment population in supported and non-supported Districts

Figure 5.

Interrupted time series (ITS) analysis of total PNC malnutrition screenings per 1000 catchment population in supported and non-supported District