Evaluation of a mHealth Data Quality Intervention to Improve Documentation of Pregnancy Outcomes by Health Surveillance Assistants in Malawi: A Cluster Randomized Trial.

Abstract

BACKGROUND:While community health workers are being recognized as an integral work force with growing responsibilities, increased demands can potentially affect motivation and performance. The ubiquity of mobile phones, even in hard-to-reach communities, has facilitated the pursuit of novel approaches to support community health workers beyond traditional modes of supervision, job aids, in-service training, and material compensation. We tested whether supportive short message services (SMS) could improve reporting of pregnancies and pregnancy outcomes among community health workers (Health Surveillance Assistants, or HSAs) in Malawi. METHODS AND FINDINGS:We designed a set of one-way SMS that were sent to HSAs on a regular basis during a 12-month period. We tested the effectiveness of the cluster-randomized intervention in improving the complete documentation of a pregnancy. We defined complete documentation as a pregnancy for which a specific outcome was recorded. HSAs in the treatment group received motivational and data quality SMS. HSAs in the control group received only motivational SMS. During baseline and intervention periods, we matched reported pregnancies to reported outcomes to determine if reporting of matched pregnancies differed between groups and by period. The trial is registered as ISCTRN24785657. CONCLUSIONS:Study results show that the mHealth intervention improved the documentation of matched pregnancies in both the treatment (OR 1.31, 95% CI: 1.10-1.55, p<0.01) and control (OR 1.46, 95% CI: 1.11-1.91, p = 0.01) groups relative to the baseline period, despite differences in SMS content between groups. The results should be interpreted with caution given that the study was underpowered. We did not find a statistically significant difference in matched pregnancy documentation between groups during the intervention period (OR 0.94, 95% CI: 0.63-1.38, p = 0.74). mHealth applications have the potential to improve the tracking and data quality of pregnancies and pregnancy outcomes, particularly in low-resource settings.