Chapter 2. Conceptual Design Principles

Table of Contents

2.1. All meta data can be added and modified through the user interface
2.2. A flexible data model supports different data sources to be integrated in one single data repository
2.3. Data input != Data output
2.4. Indicator-driven data analysis and reporting
2.5. Maintain disaggregated facility-data in the database
2.6. Support data analysis at any level in the health system

This chapter provides a introduction to some of the key conceptual design principles behind the DHIS2 software. Understanding and being aware of these principles will help the implementer to make better use of the software when customising a local database. While this chapter introduces the principles, the following chapters will detail out how these are reflected in the database design process.

The following conceptual design principles will be presented in this chapter:

In the following section each principle is described in more detail.

2.1. All meta data can be added and modified through the user interface

The DHIS2 application comes with a set of generic tools for data collection, validation, reporting and analysis, but the contents of the database, e.g. what data to collect, where the data comes from, and on what format, will depend on the context of use. These meta data need to be populated into the application before it can be used, and this can be done through the user interface and requires no programming. This allows for more direct involvement of the domain experts that understand the details of the HIS that the software will support.

The software separates the key meta data that describes the raw data being stored in the database, which is the critical meta data that should not change much over time (to avoid corrupting the data), and the higher level meta like indicator formulas, validation rules, and groups for aggregation as well as the various layouts for collection forms and reports, which are not that critical and can be changed over time without interfering with the raw data. As this higher level meta data can be added and modified over time without interfering with the raw data, a continuous customisation process is supported. Typically new features are added over time as the local implementation team learn to master more functionality, and the users are gradually pushing for more advanced data analysis and reporting outputs.