Introduction: WHO’s reference classifications, ICD and ICF have traditionally served as standards for disease, health and disability related
data. To meet the requirements of health (and disability) information systems in the 21st century WHO classifications need
to represent the knowledge digitally in a coherent semantic structure. The knowledge representation in a classification requires
that the information entities need to be identified with clear attributes and values and put into the context of an overall
information model.
Aims: To identify how we can possibly build mechanisms for meaningful data exchange in health information systems and discuss the
prospects and implications for digital systems for public health.
Results: Digitalization of health and disability information system is an emerging need around the world. The transition from an analogue
(and usually unsystematic information compilations) to digital health information system is a common observed trend which
is expected to gain dominance in forthcoming decades. The information communication technology (ICT) developments have created
multiple work streams to this field which are usually summarized as e-health. Further to the digital technology, the need
to define and provide the content standards is a shared responsibility of the both content and technology stakeholders: one
needs to define the content in a analogue form first, then convert into a digital application. Each health information rubric
should be operationally defined and then be digitally represented in computerized information systems. To achieve this aim
ontology as a computer science provides the scientific discipline and practical tools to define entities with their attributes
and values. Creating the ontological basis for classifications will enable to represent the underpinning knowledge structure
in an operational way; describe the logical rules as to how they relate to each other, identify measurable properties and
provide a basis to share information both digitally and among humans irrespective of linguistic differences. In this way,
health and disability information can be harmonized and aggregated at both individual and population levels.
Conclusions: Formalized knowledge representation will allow for better construction of health and disability information, enable research
and policy making by allowing meaningful exchange aggregation of data from multiple sources and enable science based decision-making.
Presentation slides available from: http://www.bridgingknowledge.net/Presentations/Keynote5_Ustun_Kostanjsek.pdf