Health Information System Strengthening Model: Information Generation

The information generation area of the Health Information System Strengthening Model (HISSM) encompasses the entire process of collecting, cleaning, processing, managing, and analyzing health and health-related data from a variety of sources, as well as the creation and distribution of health information products.

The information generation area of the Health Information System Strengthening Model (HISSM) encompasses the entire process of collecting, cleaning, processing, managing, and analyzing health and health-related data from a variety of sources, as well as the creation and distribution of health information products. The subareas of information generation are data sources, data management, and information products and dissemination. 

The HISSM considers three categories of data sources: institution-based data sources, population-based surveys and the civil registration and vital statistics system, and mixed-data sources, such as the public health surveillance information system and national health accounts.

Data management underlies or supports the availability of data sources and the functioning of data subsystems, and it leads to the development and dissemination of information products. 

Many types of information products (e.g., facility service reports and national annual health statistics reports) are available for a variety of users and purposes, and can be disseminated narrowly or widely, depending on the product. Below, you can see examples of interventions or elements that belong under each subarea of information generation.

Area

Subareas

Examples

Information generation

Data sources

Population-, institution-, and resource-based data systems. Population: census, population surveys, facility surveys, and civil registration and vital events.

Institution: patient records. Human resource: infrastructure including technology, human resources, and pharmaceutical and laboratory supplies and logistics

Data management

Standard operating procedures, guidance for data managers, data quality assurance practices, development and production of data collection tools, supportive supervision procedures, data quality assessments, routine data quality assessments, and data ethics procedures.

Information products and dissemination

Routine reports, bulletins, data briefs, stakeholder dissemination meetings, and feedback mechanisms (dashboards and scorecards); nonroutine reports; websites and social media, and other publications

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