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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.
The whole project can be grouped into two categories
In this part mainly one of the limitations of Patient Matching Module is addressed, which is making comparisons with all of the patient records.
For instance, if we have 10,000 patients in our system and we need to match the patients using first name and the date of birth. Goal is to check for the duplicates among them. If we compare all patients to all the others that is roughly 50 million comparisons ( 10,000 x (10,000 - 1) / 2 ). After couple of days if we run the same match where 90 patients have been added and 10 updated, with the current version it would still carry out the same method of comparison and this time it would be about 51 million comparisons!
In this project a solution is given, where the module performs comparisons only for the added and updated records for that particular strategy. Patient Matching 2.0 has this amazing method to perform this task by making about 1 million [(100 x 99 / 2) + (100 x 9990)] comparisons rather than 51 million of comparisons. Congratulations we saved the valuable time of the module neglecting 50 million comparisons.
If some of the patients in a group supposed to be the same then the user can merge those patients making they will not appear again in a patient matching report. To merge patients you have to select set of patients from a group and hit the corresponding merge button. For example in the above image the three selected patients can be merged by clicking "Merge GroupId 0" button.
There can be some scenarios where the module results some of patients to be same but in real life those patients are related to totally different people. If this happens Patient Matching 2.0 project provides a functionality to eliminate such records without them repeatedly appearing on a patient matching report. To exclude patients you have to select set of patients from a group and hit the corresponding "Exclude Match" button.
Weekly blog posts during the development period