data standards

Tipsheet: Sources of HRH Data

Objective Understand the sources of HRH data and communicate the value of health workforce information. As much as possible, the HIS should use common datasets and a standardized data exchange format. This will ensure that the HRIS can regularly share data with other systems. Linking HRH data with broader health information–such as disease burden, health […]

Worksheet: Standard Datasets

Objective Document data standards, or standard ways of listing data in dropdown menus. Use the worksheet to identify all of the lists to be used in iHRIS and develop standard lists of data types for each. These will be added to the systems requirements for customization of iHRIS. While this worksheet documents standardized data lists […]

Tipsheet: Standardized Data Lists

Objective Identify standards to use when sharing data. The iHRIS software will likely not exist in isolation from other parts of the health information system. As your iHRIS deployment matures, more and more systems will want to use the data contained in iHRIS. There are two important concepts to understand when sharing data: standardized data […]


A planning phase is essential for defining the system requirements before installation of iHRIS begins. These requirements should build on the work done during the Assess stage. Planning (as well as all subsequent stages) should be done in consultation with stakeholders. Understanding the various perspectives of the stakeholders and finding areas of consensus are important […]

Worksheet: Customization of Fields

Objective Customize fields, forms, and modules in iHRIS according to the agreed-upon data standards. It is unlikely that iHRIS as installed will collect all the data you need using the standards that the stakeholders have agreed upon. That is why iHRIS has been designed to easily be customized. The software developers will probably need to […]

Example: HRIS Data Quality Guidelines

Objective Institute procedures to reduce data entry errors. Use the example guidelines from Uganda as a model for preparing a data quality plan, with the goal of minimizing errors during data entry. Data Quality: Concepts When entering data from paper forms, maintaining the quality, or accuracy, of the data is the most important consideration. Introduce […]