Measuring Data Collection Diligence for Community Healthcare

TL;DR

This paper introduces a diligence score for community health workers' data collection, leveraging domain expertise and clustering to evaluate and predict data quality in low-resource settings, validated through field tests in India.

Contribution

It presents a novel, domain-guided scoring method for assessing and predicting data collection diligence of community health workers in developing countries.

Findings

Validated on field data from India

Effective in ranking and predicting CHW diligence

Deployed in Rajasthan, India

Abstract

Data analytics has tremendous potential to provide targeted benefit in low-resource communities, however the availability of high-quality public health data is a significant challenge in developing countries primarily due to non-diligent data collection by community health workers (CHWs). In this work, we define and test a data collection diligence score. This challenging unlabeled data problem is handled by building upon domain expert's guidance to design a useful data representation of the raw data, using which we design a simple and natural score. An important aspect of the score is relative scoring of the CHWs, which implicitly takes into account the context of the local area. The data is also clustered and interpreting these clusters provides a natural explanation of the past behavior of each data collector. We further predict the diligence score for future time steps. Our framework has been validated on the ground using observations by the field monitors of our partner NGO in India. Beyond the successful field test, our work is in the final stages of deployment in the state of Rajasthan, India.