ArticleJMIR research protocols2019
Use of Big Data and Machine Learning Methods in the Monitoring and Evaluation of Digital Health Programs in India: An Exploratory Protocol.
Article in JMIR research protocols, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Heterogeneity in blood pressure and weight trajectories and associated mortality risk among hypertensive patients in rural South Africa: findings from cluster analysis of longitudinal clinical record linkage data.BMC public health · 2026Article
- A Standard Framework for Evaluating Large Health Care Data and Related Resources.MMWR supplements · 2024Article
- Article
- Assessing exposure to Kilkari: a big data analysis of a large maternal mobile messaging service across 13 states in India.BMJ global health · 2021Review
- Using Machine Learning to Optimize the Quality of Survey Data: Protocol for a Use Case in India.JMIR research protocols · 2020Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundDigital health programs, which encompass the subsectors of health information technology, mobile health, electronic health, telehealth, and telemedicine, have the potential to generate "big data."
objectiveOur aim is 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 for public health practitioners that 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 is an Interactive Voice Response audio training course for accredited social health activists (ASHAs) in India.
methodsStudy 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.
resultsThe 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 August/September 2019.
conclusionsRigorous 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 articles on analysis 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. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/11456.
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