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OpenMRS is increasingly used for multiple data collection points across several systems. There are different models of using openMRS in health care for example where a user registers a patient at the point of encounter and subsequently enters all their details into the system, and in other cases a clinician writes patient details on a paper chart, that is later transferred to the openMRS system by data clerks. Moreover , a program may involve a mobile tool that registers patients while out in the field , and sends this data into the main system to create new patients. These data are combined into one record, and depending on the available data elements are appended to an existing patient or used to create a new patient.

Unfortunately , most of the registration work for patients in openMRS remains a data collection exercise. We wish to transform this by developing and implementing a data blocking algorithm that quickly uses the data elements entered during this registration to determine if the patient is existing or new to the system , or is in the 'grey zone ' that requires processing by the patient matching module implemented in openMRS. We aim to improve patient identification and matching , and build a framework to allow for incorporation of additional data elements like biometric data , latitude and longitude to improve patient registration