Building Decision Support for Developing Nations

Ontology Programming Makes the Difference

Developing countries are increasing their reliance on technology to modernize their industries, and the health care industry is no exception. Their governments, health care ministries, and public health officials are utilizing IT solutions to improve health outcomes. International Health and Human Services (HHS) organizations such as the World Health Organization, the International Red Cross, and U.S. agencies such as USAID are assisting them by:

  • Providing resources that help them improve care by strengthening their health care-related infrastructures.
  • Providing them with expertise in the fields of medicine and disease management.
  • Utilizing technologies to drive important health care-related decisions for improving health care outcomes.

Some of the HHS organizations are utilizing technology to improve program effectiveness and to audit their own work in the field. One such organization is utilizing a cutting-edge ontological engineering software Decision Support System (a vector control software) to determine potential health care issues in a developing country and to also measure and monitor the impact of their disease control initiatives.

This paper describes the challenges and opportunities in building a health care DSS for developing nations. It also outlines how such nations are fusing their health care needs and ontology programming with their infrastructures (or lack thereof in various places) to accomplish desired outcomes.

Ontologically Engineered Software

A vector control software that’s an ontology-based platform with an integrated Geographic Information System (GIS) has been successfully designed to be used for disease management-related decision making in developing nations. Its initial release was developed for a prevalent disease (malaria) in an African country. However, it is currently being expanded to address numerous diseases in multiple countries.

The system is used to determine the disease footprint in a region so that remedial initiatives can be undertaken by the local public health officials. The HHS also utilizes the product as an audit tool to determine an initiative’s effectiveness and to refine the subsequent iterations of the program based on prior results. The disease control initiatives include a wide range of measures such as the use of various types of insecticide sprays.

The DSS uses ontological programming principles (or “semantic” technologies) to model geographic, entomological, and insecticidal nomenclature. Standardization of insect-related terminology allows data from multiple organizations to be effectively combined and queried. Additionally, since terms have hierarchical relationships, the technology allows for automatic categorization and grouping of related data.

As new terms are added, ontology programming allows dynamic queries to automatically include them. This provides the health care DSS with a high degree of flexibility, as terms and relationships between terms can change and adapt dynamically in the field to accommodate new requirements.

Furthermore, the geographic ontology standardizes terms for geographic features. This ensures data interoperability and allows for the GIS system to work, even in cases where the exact longitude and latitude of a data point is not known.

The DSS uses GIS to capture, store, and analyze data associated with geographic locations in order to generate maps as a visual tool. A map consists of one or more layers, with each layer defined by a query created in the DSS. The layers can be overlaid and color-coded into meaningful representations of relationships and correlations between the data and geographic locations. These custom-generated maps of vector control software greatly assist public health officials in making informed decisions regarding disease control.

This ontologically engineered product is capable of providing reports and query results using local data alone or data aggregated across geographic and governmental hierarchies. For example, an end user can query the system and utilize data from health care facilities at a village, city, district, state, or regional level-as well as a countrywide level.

The HHS expects geo-tagged data collection to occur throughout the African country and intends to use it for reports at all levels. This will be accomplished by deploying self-contained, fully functional copies of the DSS at all the locations and levels of interest. Data collected at each level will be forwarded to the next higher aggregation point in order to achieve a wider coverage report at subsequent levels, until eventually the entire country is covered.