JMIR Publications

ABSTRACT

Background:

Digital health programs, which encompass the sub-sectors of health information technology, mobile health, electronic health, telehealth, and telemedicine, have the potential to generate ‘big data’. As part of efforts to evaluate two digital health programs in India – the maternal mobile messaging service (Kilkari) and the mobile training resource for frontline health workers (Mobile Academy) – we illustrate possible applications of machine learning (ML) for public health practitioners which can be applied to generate evidence on program effectiveness and improve implementation. Kilkari is an outbound service that delivers weekly, gestational age appropriate audio messages about pregnancy, childbirth, and childcare directly to families on their mobile phones, starting from the second trimester of pregnancy until the child is one-year-old. Mobile Academy (MA) is an Interactive Voice Response (IVR) audio training course for ASHAs in India.

Objective:

Through the use of two digital health program examples, we illustrate possible applications of machine learning which can be applied to generate evidence on effectiveness, as well as to more broadly improve program implementation. We seek to provide an explanation in layman terms of the methods under consideration, for an audience unfamiliar with machine learning algorithms or advanced statistical methodologies.

Methods:

Study participants include pregnant and postpartum women (Kilkari) as well as frontline health workers (Mobile Academy) across 13 states in India. Data elements are drawn from system generated databases used in the routine implementation of programs to provide users with health information. We explain the structure and elements of the extracted data and the proposed process for their linkage. We then outline the various steps to be undertaken to evaluate and select final algorithms for identifying gaps in data quality, poor user performance, predictors for call receipt, user listening levels, and linkages between early listening and continued engagement.

Results:

The project has obtained the necessary approvals for the use of data in accordance with global standards for handling personal data. The results are expected to be published in the summer and fall of 2019.

Conclusions:

Rigorous evaluations of digital health programs are limited, and few have included applications of machine learning. By describing the steps to be undertaken in the application of machine learning approaches to the analysis of routine system generated data, we aim to demystify the use of machine learning not only in evaluating digital health education programs but in improving their performance. Where analysis articles offer an explanation of the final model selected, here we aim to emphasize the process; thereby illustrating to program implementors and evaluators with limited exposure to machine learning its relevance and potential use within the context of broader program implementation and evaluation. Study participants include pregnant and postpartum women (Kilkari) as well as frontline health workers (Mobile Academy) across 13 states in India. Data elements are drawn from system generated databases used in the routine implementation of programs to provide users with health information. We explain the structure and elements of the extracted data and the proposed process for their linkage. We then outline the various steps to be undertaken to evaluate and select final algorithms for identifying gaps in data quality, poor user performance, predictors for call receipt, user listening levels, and linkages between early listening and continued engagement. Clinical Trial: None