Application of Data Analytics in Healthcare for Regulatory Compliance
Reneta P. Barneva; Lisa Walters*
Healthcare organizations are heavily regulated by FDA - the Food and Drug Administration. They have to be in compliance with specific systems such as Quality Assurance System, Quarantine/Inventory Management System, Donor Eligibility System, Product Processing System, Product Testing System, and others.
In order to ensure this, the organizations need to prevent potentially risky situations. The detection of such situations is based on analysis of past records of deviation codes - that is, cases in which there were irregular or unsuccessful transactions. In large organizations, there may be hundreds of millions of such records and thousands of different deviation codes, which make the analysis and prediction quite challenging.
In this presentation we will explain how to make the problem manageable, extract rules for evaluating the risk, develop a decision model, implement it, and test it. We will also emphasize the interdisciplinary nature of the project and the role of various stakeholders.
The proposed approach is quite general and can be applied to a broad type of organizations. Computer software to implement the model described above was developed and deployed in 2012. It was found to be very useful in practice for indicating potential problems before they developed to critical situations. This also helped to strategically distribute the resources and lead to savings for the organization.