Smart mrs bi project-presentation
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This document introduces SMARTMRS, a business intelligence (BI) solution for OpenMRS, an open-source medical record system. SMARTMRS uses a Python-based technology stack including the Cubes library for online analytical processing (OLAP) and data warehousing functionality. It connects directly to OpenMRS's backend MySQL database. The document discusses why a Python/Cubes approach was chosen over Pentaho, and provides an overview of how Cubes can be used to define dimensions and facts from OpenMRS data for OLAP and ad hoc analysis through a demo.
Smart mrs bi project-presentation
- 1. SMARTMRS A BI solution for OpenMRS Presented by V B Wickramasinghe (148245F)
- 2. ● Introduction ● OpenMRS data model ● SmartMRS technology stack ● Why not Pentaho? ● Easy OLAP ● Demo Overview
- 3. ● OpenMRS is a widely used open source medical record system. ● SmartMRS is a business intelligence solution for OpenMRS. ● A typical OpenMRS installation accumulates large amounts of data. ● This combined with the complexity of the medical domain makes it a hard task to extract intelligence from OpenMRS manually. ● But OpenMRS can provide very useful and critical insights for society if used correctly. Introduction
- 4. Introduction Detect spread of disease Detect environmental issues Predict medicine demand Predict medical staffing requirements Analyze social health trends Ensure medical data availability Ensure medical staff efficiency Ensure implementation of proper health policy +
- 5. OpenMRS data model is complex but centered around few main entities, ● Concept: Concepts are defined and used to support strongly coded data throughout the system ● Encounter: Contains the meta-data regarding health care providers interventions with a patient. ● Form: Essentially, the user interface description for the various components. ● Observation: This is where the actual health care information is stored. There are many observations per Encounter. ● Order: Things/actions that have been requested to occur. ● Patient: Basic information about patients in this system. ● User: Basic information about the people that use this system. ● Person: Basic information about person in the system. ● Business: Non medical data used to administrate openmrs ● Groups/Workflow: Workflows and Cohort data OpenMRS data model
- 7. ● Python based - Because of simplicity, ad hoc style of programming and great data analysis tools etc. ● Connects directly to backend MySQL db of OpenMRS for now. ● Uses Cubes library for OLAP and data warehouse functionality. ● Uses CubesViewer frontend for online user interface ● Can choose among the likes of ScikitLearn, Orange for Data Mining. SmartMRS technology stack
- 8. SmartMRS technology stack Datamining Reporting + dashboards Data warehouse + OLAP Framework Language + Data Integration framework Database
- 9. Pentaho is the natural choice for open source BI solutions. Which I also investigated for SmartMRS solution. But it has some disadvantages compared to the Python/Cubes based solution. 1. Complexity and sophistication of Pentaho makes it harder to customize. 2. Pentaho documentation is lacking for solution implementers(intentionally?). 3. Python vs Java Why not Pentaho?
- 10. ● Start with Cubes model definition(check IDE), ● We use Observation(obs table) as fact table. ● Location(location table) as dimension table. ● Use country, province, area as drill down levels. ● Also define observation time as a dimension. ● Can drill down definitions of observations using concept dimension. ● Many other cube definitions possible. ● With cubes you get a REST API for all cube queries as well. Easy OLAP
- 11. ● Cubes ad hoc analysis ● Cubes Viewer Demo
- 12. Any Questions?
- 13. Thank You