Open Source Libraries for Health Analytics

According to Health Catalyst’s Director of Data Science Levi Thatcher, the main author of the project, these tools are tried and tested. Many of them are based on popular free software libraries in the general machine learning space: he mentions in particular the Python Scikit-learn library and the R language’s caret and and data.table libraries.

Andy Oram

The contribution of Health Catalyst is to build on these general tools to produce libraries tailored for the needs of health care facilities, with their unique populations, workflows, and billing needs. The company has used the libraries to deploy models related to operational, financial, and clinical questions. Eventually, Thatcher says, most of Health Catalyst’s applications will use predictive analytics based on healthcare.ai, and now other programmers can too.

Currently, Health Catalyst is providing libraries for R and Python. Moving them from internal projects to open source was not particularly difficult, according to Thatcher: the team mainly had to improve the documentation and broaden the range of usable data connections (ODBC and more). The packages can be installed in the manner common to free software projects in these language. The documentation includes guidelines for submitting changes, so that an ecosystem of developers can build up around the software. When I asked about RESTful APIs, Thatcher answered, “We do plan on using RESTful APIs in our work—mainly as a way of integrating these tools with ETL processes...”