Towards operationalizing outlier detection in community health programs

ABSTRACT

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.

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