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Elevator Pitch
This squad’s work will shorten the time and improve the quality of using OMRS data in indicator reporting, reduce unplanned technical team member overtime, and make it easy to drill down to patient-level data to confirm the numbers are correct.
Background and strategic fit
One of the key opportunities for greater collaboration within the OpenMRS community is around reporting through ETL (Extract, Transform, and Load). This was underscored during discussions at OMRS19. It’s difficult to do modern analytics on OpenMRS data. The manual process of designing and developing ETL queries is time-consuming, complex, and is being tackled one implementation at a time.
We have prototyped ETL approaches with both Spark and FHIR models.
We are handling most of the PEPFAR MER indicators using the traditional ETL pipeline, but we don't have a modular approach that allows more aggregate level analytics. Want to extract data out of OMRS in a structure that is easier to query so that it is easier to query these specific indicators. Using 2-3 specific indicators can be a benchmark - need to ensure we cover both complex and simple ones.
Goals
- Pick one concrete problem to solve
- An output that is general, that most implementations can use. (Since inputs are so different by organization.)
- Build on top of a FHIR-based datawarehouse (assumes people are comfortable working with FHIR schemas, and comfortable writing complex SQL queries → Need to validate)
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