Building Decision Support for Developing Nations

Building Decision Support for Developing Nations

Ontology Programming makes a difference.

The health care sector is not the only one that is increasing its dependence on technology in developing countries. Their governments, health care ministries, public health officials, and health professionals are using IT solutions to improve their health outcomes. International Health and Human Services organizations (HHS), such as the World Health Organization and the International Red Cross, and U.S. agencies such as USAID, are supporting them.

They will be able to access resources that can help improve their care and strengthen their infrastructure.
They will be able to offer their expertise in medicine and disease management.
Technology can be used to improve health care outcomes by driving important decisions in health care.
HHS agencies are using technology to increase program effectiveness and audit their work in the field. One such organization uses a cutting-edge ontological engineering software Decision Support System (a vector management software) to identify potential health care problems in developing countries and also to monitor and measure the impact of their disease prevention initiatives.

This paper discusses the opportunities and challenges of building a DSS in health care for developing countries. This paper also describes how these nations combine their ontology and health care programming with their infrastructures to achieve desired outcomes.

Software that is ontologically engineered

The vector control software is an ontology-based platform that integrates a Geographic Information System (GIS). It has been designed for use in disease management-related decisions in developing countries. The initial release was intended to address a common illness, malaria, in an African country. It is being designed to treat multiple diseases in different countries.

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It is used to identify the disease footprint of a particular region and determine what remedial actions can be taken by local health officials. HHS uses the product to evaluate the effectiveness of an initiative and refine subsequent iterations based on previous results. A wide variety of measures are used in disease control programs, including the spraying of different types of insecticides.

DSS uses ontological programming principles or “semantic technologies” to model geographical, entomological and insecticidal nomenclature. Data from different organizations can be combined and questioned by standardizing insect-related terminology. The technology also allows for the automatic categorization of and grouping of related data, as terms are linked in hierarchical ways.

Ontology programming can automatically include new terms as they are added. This allows the DSS in health care to be flexible as the terms and relationships between them can change or adapt dynamically in order to meet new requirements.

The geographic ontology also standardizes terms used for geographical features. This allows data interoperability and enables the GIS system to work even when the exact latitude and longitude of a data point are not known.

GIS is used by the DSS to store, analyze, and capture data associated with geographical locations to create maps. Each layer of a map is defined by a query in the DSS. These layers can be coloured-coded and overlaid to create meaningful representations of geographic locations and relationships. Public health officials can use these maps to make informed decisions about disease control.

The product can provide reports and query results with local data only or data aggregated across geographical and governmental hierarchies. An end-user can query this system to obtain data from local health facilities at the village, city or district level, as well as at the countrywide level.

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HHS expects that geo-tagged data will be collected in every African country. It intends to use this data for reports at all levels. The DSS will be deployed at all levels and locations to ensure that it is fully functional. To achieve more excellent coverage at higher levels, data collected at each level will then be sent to the next higher aggregate point.



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