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While OpenMRS serves as a platform for building a medical record system within a specific healthcare network, it also can provide very valuable data which, when analyzed, creates insight into the disease burden of a region and its population health. A large instance of OpenMRS contains an immense set of data that can significantly benefit individuals that are interested in analyzing it. The question of privacy Privacy is an important consideration with data aggregation , however – especially with as healthcare data as it contains the contains a large amount of patients' personal information of the patients.
The results from the analysis of this anonymous data balances the needs of both the implementers of OpenMRS and the research community. A closer look at information involving disease burden and population health gives the implementers of OpenMRS a better perspective as to what new features may be needed or old features that need to be updated/improved by the developers. Likewise, the analysis will provide researchers with better knowledge of diseases in specific areas and the burden they place on different parts of that region.
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The objective of the Data Aggregation Module is to aggregate anonymous data across multiple instances of OpenMRS through a REST-LIKE Like interface. The module utilizes Hibernate to execute the SQL statement needed to access the requested, anonymous data from the database. Currently, the module supports 2 queries: Tests Ordered and Disease Counts.
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The data can be returned in 3 different file formats: JSON, XML, and CSV. These file formats are chosen to help organize the data in a way that is easy to graph. In addition, the purpose of this module is to make it possible to combine data from data, allowing it to be graphed easily utilizing different data visualization tools like D3, R, or even a simple spreadsheet application like Excel.
When used with multiple instances of OpenMRS, and it provides these output formats to make it easier to evaluate this datathis module can produce robust data to be graphed and help answer really interesting questions about population health.
The results themselves can be used to analyze different aspects of population health and disease burden, such as:
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