Evaluating the accuracy of data collection on mobile phones: a study of forms, sms, and voice
ICTD'09 Proceedings of the 3rd international conference on Information and communication technologies and development
Usher: Improving Data Quality with Dynamic Forms
IEEE Transactions on Knowledge and Data Engineering
Automated quality control for mobile data collection
Proceedings of the 2nd ACM Symposium on Computing for Development
Shreddr: pipelined paper digitization for low-resource organizations
Proceedings of the 2nd ACM Symposium on Computing for Development
Using behavioral data to identify interviewer fabrication in surveys
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Improving form-based data entry with image snippets
Proceedings of Graphics Interface 2013
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Efficient health systems require reliable data. In developing countries the need for accurate data is particularly acute, as organizations are often forced to make decisions on a tight budget with limited capacity for data collection. In this note, we describe recent progress toward developing a set of algorithms that can help detect and classify anomalies in health worker data. Building on recent efforts to use unsupervised multinomial techniques for outlier detection, we outline the steps required to turn a set of statistical tests into a framework that can be implemented by health organizations, and calibrate these algorithms on a large dataset from a partner health organization. Here, we describe the core methods, present results from ongoing analyses, and outline our plan for future work, including plans to obtain labeled training data that will allow us to detect and classify different types of outlier in community health worker data.