The effect of an mHealth clinical decision-making support system on neonatal mortality in a low resource setting: A cluster-randomized controlled trial

Abstract

Background MHealth interventions promise to bridge gaps in clinical care but documentation of their effectiveness is limited. We evaluated the utilization and effect of an mhealth clinical decision-making support intervention that aimed to improve neonatal mortality in Ghana by providing access to emergency neonatal protocols for frontline health workers. Methods In the Eastern Region of Ghana, sixteen districts were randomized into two study arms (8 intervention and 8 control clusters) in a cluster-randomized controlled trial. Institutional neonatal mortality data were extracted from the District Health Information System-2 during an 18-month intervention period. We performed an intention-to-treat analysis and estimated the effect of the intervention on institutional neonatal mortality (primary outcome measure) using grouped binomial logistic regression with a random intercept per cluster. This trial is registered at ClinicalTrials.gov ( NCT02468310 ) and Pan African Clinical Trials Registry ( PACTR20151200109073). Findings There were 65,831 institutional deliveries and 348 institutional neonatal deaths during the study period. Overall, 47 ∙ 3% of deliveries and 56 ∙ 9% of neonatal deaths occurred in the intervention arm. During the intervention period, neonatal deaths increased from 4 ∙ 5 to 6 ∙ 4 deaths and, from 3 ∙ 9 to 4 ∙ 3 deaths per 1000 deliveries in the intervention arm and control arm respectively. The odds of neonatal death was 2⋅09 (95% CI (1 ∙ 00;4 ∙ 38); p = 0 ∙ 051) times higher in the intervention arm compared to the control arm (adjusted odds ratio). The correlation between the number of protocol requests and the number of deliveries per intervention cluster was 0 ∙ 71 (p = 0 ∙ 05). Interpretation The higher risk of institutional neonatal death observed in intervention clusters may be due to problems with birth and death registration, unmeasured and unadjusted confounding, and unintended use of the intervention. The findings underpin the need for careful and rigorous evaluation of mHealth intervention implementation and effects. Funding Netherlands Foundation for Scientific Research - WOTRO, Science for Global Development; Utrecht University.

Can an mhealth clinical decision-making support system improve adherence to neonatal healthcare protocols in a low-resource setting?

“…Commensurate efforts on neonatal health improvement training programmes in addition to the mCDMSS in the intervention clusters may have led to significant improvements in adherence to protocols in the intervention clusters as well. The higher number of concurrent neonatal health improvement activities in the control clusters could also explain the observed lower odds of neonatal death in the main CRCT findings [ 49 ] .…”

Findings from the Health Information Management Section of the 2020 International Medical Informatics Association Yearbook

“…There are new areas that could be considered for incorporation into HIM education programs. The increasing use of mobile technologies in healthcare (mHealth) and the research conducted on their effectiveness, such as the study by Amoakoh et al, [17], will likely lead to patient data from mobile devices being incorporated into the EHR. Managing these data will become more important and will require HIM professionals to have more understanding of the use of such technologies, as well as the challenges of incorporating and displaying the often extensive information from these devices.…”

Digital Transformation and Global Health in Africa

“…Ghana, a West African country, has evaluated Digital Health technologies in different scenarios. For example, mHealth and telemedicine have been used within healthcare settings in Ghana (Amoakoh et al 2019). A study on the use of mobile technologies (text messaging, voice messaging and calling patients, and interactive voice response system) by Laar et al (2019) was conducted in Ghana.…”