Can we design the next generation of digital health communication programs by leveraging the power of artificial intelligence to segment target audiences, bolster impact and deliver differentiated services? A machine learning analysis of survey data from rural India

Abstract

Objectives: Direct to beneficiary (D2B) mobile health communication programmes have been used to provide reproductive, maternal, neonatal and child health information to women and their families in a number of countries globally. Programmes to date have provided the same content, at the same frequency, using the same channel to large beneficiary populations. This manuscript presents a proof of concept approach that uses machine learning to segment populations of women with access to phones and their husbands into distinct clusters to support differential digital programme design and delivery.

Setting: Data used in this study were drawn from cross-sectional survey conducted in four districts of Madhya Pradesh, India.

Participants: Study participant included pregnant women with access to a phone (n=5095) and their husbands (n=3842) RESULTS: We used an iterative process involving K-Means clustering and Lasso regression to segment couples into three distinct clusters. Cluster 1 (n=1408) tended to be poorer, less educated men and women, with low levels of digital access and skills. Cluster 2 (n=666) had a mid-level of digital access and skills among men but not women. Cluster 3 (n=1410) had high digital access and skill among men and moderate access and skills among women. Exposure to the D2B programme 'Kilkari' showed the greatest difference in Cluster 2, including an 8% difference in use of reversible modern contraceptives, 7% in child immunisation at 10 weeks, 3% in child immunisation at 9 months and 4% in the timeliness of immunisation at 10 weeks and 9 months.

Conclusions: Findings suggest that segmenting populations into distinct clusters for differentiated programme design and delivery may serve to improve reach and impact.

Trial registration number: NCT03576157.

Keywords: community child health; information technology; public health.

Figure 1

Framework for segmentation analysis.

Figure 2

Elbow method used to help decide ultimate number of clusters appropriate for the data.

Figure 3

Silhouette analysis for three and four clusters.

Figure 4

Distribution of select characteristics with strong signals by Cluster. Variables which had at least a prevalence of 70% in one or more clusters and differed from another cluster by 50% or more were considered to have a strong signal (*reported by men interviewed; **observed by survey enumerators).