This paper presents the results of a research-oriented project to demonstrate that certain Data Segmentation for Privacy, orDS4P tasks can be enhanced through the use of clinical decision support (CDS) technology. It advances a novel use of CDS tools to 1) identify and sequester certain types of information from electronic medical records and to 2) help mitigate the potential risks of exchanging records from which data have been sequestered. The approach is called Decision Support for Data Segmentation, or DS2, builds upon standards-based open source CDS technology to create a familiar CDS-based platform for the development and testing of functions to identify and redact selected conditions from clinical summary documents in various contexts including Health Information Exchange (HIE) between healthcare providers. The DS2 prototype demonstrates how deterministic clinical rules and machine learning-based classifiers can work together to detect clinical facts that may imply a condition even if they are not directly related to the condition and how CDS at the point-of-care can potentially make use of clinical information even after it has been sequestered.