  With calls for increasing transparency, governments are releasing greater
amounts of data in multiple domains including finance, education and
healthcare. The efficient exploratory analysis of healthcare data constitutes a
significant challenge. Key concerns in public health include the quick
identification and analysis of trends, and the detection of outliers. This
allows policies to be rapidly adapted to changing circumstances. We present an
efficient outlier detection technique, termed PIKS (Pruned iterative-k means
searchlight), which combines an iterative k-means algorithm with a pruned
searchlight based scan. We apply this technique to identify outliers in two
publicly available healthcare datasets from the New York Statewide Planning and
Research Cooperative System, and California's Office of Statewide Health
Planning and Development. We provide a comparison of our technique with three
other existing outlier detection techniques, consisting of auto-encoders,
isolation forests and feature bagging. We identified outliers in conditions
including suicide rates, immunity disorders, social admissions,
cardiomyopathies, and pregnancy in the third trimester. We demonstrate that the
PIKS technique produces results consistent with other techniques such as the
auto-encoder. However, the auto-encoder needs to be trained, which requires
several parameters to be tuned. In comparison, the PIKS technique has far fewer
parameters to tune. This makes it advantageous for fast, "out-of-the-box" data
exploration. The PIKS technique is scalable and can readily ingest new
datasets. Hence, it can provide valuable, up-to-date insights to citizens,
patients and policy-makers. We have made our code open source, and with the
availability of open data, other researchers can easily reproduce and extend
our work. This will help promote a deeper understanding of healthcare policies
and public health issues.
