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Properly identifying patients is a critical feature of any electronic medical record system. In the real world, there can be many challenges to patient identification. While some countries have reliable universal identifiers, most countries do not and we rely on patient demographics (e.g., names, gender, date of birth, etc.) to identify patients. In many cases, proper identification can be challenging (e.g., variable spelling of names, estimated or unknown date of birth). While ongoing efforts to incorporate biometrics and other means of reliable identification, the reality is many systems ending up with duplicate records for patients.

Identifying and correcting duplicate records can be a painful process when performed manually. The OpenMRS Patient Matching module was created by one of the world's leading experts on patient matching techniques, Dr. Shaun Grannis, to use statistical methods to maximize the value of automated patient matching. We're very lucky to have this module; however, it has not been widely adopted because of some key features needed to make it easy for implementations to use.

The goal of this project is address the key features missing in the Patient Matching module, release a new 2.0 version with the features addressed, and, thereby, help the OpenMRS community benefit from the power of this patient matching module.