The Issue:

Geographic data is an essential piece of data for health and social programs. For example, immunisation programs, NTD eradication programs or bednet distribution need villages, population and location information. Facilities and the services they offer, roads, transportation hubs, electricity and water supply sources, and political boundaries are all important datas to plan and implement health programs.

Information systems store this geographical information in various formats. Village names and identifiers differ from one data-base to another. Health facility location and names vary from one data system to another. Territories are divided in administrative and health regions managed separately by different authorities, leading to inconsistencies. Reference geospatial information tends to evolve over time in their respective systems, leading to growing discrepancies. 

This data fragmentation and lack of standardisation leads to duplicative efforts,  imprecise metrics, time consuming data integration efforts and errors. This lack of data accuracy can have devastating effects during outbreaks.

The Solution:

Iaso is a geospatial data management platform (a georegistry) developed by Bluesquare to support the continuous management geographical information. The tool supports data management based on master lists of generic geographical objects often called organizational units (“org. units”, which are for example health areas, schools, provinces, health districts, or wells). 

The data management platform has two components: 

  • A central georegistry that is used to manage, update and validate multiple master lists of org. units, including their geographical features, through the unification of multiple data sources. 
  • A data collection tool that supports structured and generic collection of information linked to org. units, e.g. collecting school location, or village location, or health service availability, or population estimates, across a territory.  

Iaso provides a number of core features in support of continuous geospatial data management: 

  • mobile applications that can be tailored for updating the georegistries, but are also able to support surveys and routine data collection processes such as supervision. 
  • a web dashboard in support to data management, and in particular in support to the geospatial data validation process. Thanks to a GPS/user based activity tracking, it is possible to track where and when data collection took place and to reuse this data to locate health structures more accurately.
  • A matching feature to merge various data sources into a central reference data source, while keeping the full traceability of where each data point comes from, so that you know what to correct if needed.
  • A data science and scripting interface. This allows analysts and data scientists to develop algorithms and metrics in support of georegistries, for instance for matching new data sources or evaluating the quality of new geospatial data. 
  • A seamless bi-directional integration with DHIS2 that allows continuous update of geospatial data in the data platform used in multiple countries as Health and Education Information systems. 

Iaso as georegistry is used in several countries in support of a process where updated data improves the health information system based on DHIS2. 

How does Iaso work ?

Why you should use it:

Managing as many referential lists as you want

Iaso has the ability to store and support the management of an indefinite number of master lists. This allows users to load different versions of master lists and support their matching. The tool then creates links between the various lists, allowing it to build a master reference list out of several fragmented data sets. 

From the integration of multiple lists and data sets, the tool allows the building of a reference list of geographical objects for a region, and provides web and mobile interfaces so that multiple stakeholders can update, enrich and maintain the reference information. 

Illustration of the setup of a reference master list of geographical objects in Niger based on multiple sources. Three sources, SNIS, Commune and Renaloc were merged in one single consistent master list.

Iaso interface in Niger: visualising villages and health and administrative boundaries in a single interface

Facilitating the continuous data management of the master lists

 The tool provides interfaces so that authorized users can propose new data through mobile devices, web interfaces or data uploads. This data can then be selected and validated by other users, following standard validation processes. The system keeps track of all the changes and change requests made by the different users.

Leveraging routine data collection

A strength of Iaso is that its data collection tools can be used to support surveys and routine data collection processes. Its seamless integration with DHIS2 makes it a very attractive instrument for supporting programs supported by DHIS2. The result of this is that the geo-registry is continuously enriched by data coming from routine data collection carried out by health programs. For instance, while reporting screening tests performed in villages, the sleeping sickness elimination program in DRC collected, in the background of their activity, 700.000 geographical points of high value for the national health system.

Easy to use by multiple teams in parallel

 Iaso facilitates the continuous distributed management of master lists by different teams in different locations. Field workers have user friendly offline mobile interfaces to propose updated data. The validation work can be distributed by geographical sub-area and administrative entities. For example a new settlement name can be proposed by a health district, and validated by the provincial administrative authority. The master list is then automatically updated in the mobile applications, so that health workers have the most recent information available.

Data sciences in support of georegistry management

 The tool provides interfaces that makes the data accessible to data scientists. Data scientists can then provide and define algorithms for better master list management and validation. An example of this is to include the development of algorithms for the selection of a geographical reference when multiple points and references exist for an object. For example, when a great number of collected coordinates are available for a hospital, an algorithm could be designed to keep one that is consistent with the majority of the reports.

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