1. Healthcare Services as Collective Activity Susan Wakenshaw Xiao MA

2. We studied Medical Tourism Medical Tourism Facilitators try to use medical ontology to match patient enquiries with healthcare providers services But They do NOT match

3. The Taj Medical Group (TMG) is a leading medical tourism facilitator, and has arranged treatment for over eight hundred international patients from the UK and other countries. A major problem faced by TMG was the level of resources required for the process of matching their patients’ enquiries with the capabilities of different medical treatment providers.

4. Rhinoplasty Nose? Ears? Face? Mouth?

5. Generic Terms Used by patients Medical Ontology Built by Healthcare Professionals

6. Quick Short development cycle, not in years like SNOMED Broad in coverage Links both professional and non-expert terms Economical Require less experts contribution

7. -This research aims to enhanced the health services through matching, collaboration, interaction at the community level. -Reversed Ontology engineering method (Ma, et al, 2014) was used.

8. Our pilot experiment in the field was for the largest medical tourism agent in the UK at the time – the Taj Medical Group, on an automated healthcare service matching platform. The ontology based matching mechanism significantly improved the matching result, and subsequently made a previously labor intensive process driven unfeasible commercial proposition into a semi- automated computer driven process that is commercially viable.

9. Ontology Ontology in the medical sector are built to enable healthcare information (such as patient data, diagnosis and care regimes) share, reuse and transfer in medical information systems. They normally contain detailed medical terminology (clearly defined medical domain concepts) and related terms around them. Healthcare ontology deal with both types of users: healthcare professionals who are expert in the field and patients who are non-specialists. Such ontology are expected to bridge the gaps between these two user groups.

10. Ontology Firstly, the purpose of ontologies is to provide a shared understanding of a given domain of interest (McGuinness, 2003). Neches et al. define ontologies as “the basic terms and relations comprising the vocabulary of a topic area.” (1991, p. 40). Ontologies, therefore can be considered as social representation that represent a shared interpretation of a part of the world (Borst, 1997), by capturing and providing consensual knowledge that is accepted by, or derived from a group (Fensel, 2000).

11. The filtering process was reasoned by reverse application to the ontology structure as demonstrated by Figure 4.13. A natural language enquiry may have descriptions that contain terms stored within the ontology structure. These terms may cover different aspects of a domain (or even cover multi domains), and they may not be representative of the domain. The derived ontology can locate these terms in its structure (at the outskirt connection zone), and track the appropriate paths (via the middle connection zone) to those highly representative concepts (in the core/top zone). Through such filtering mechanism, enquiries in natural language without clear indication of domain concepts could be tagged with representative concepts.

12. Options Ontology Structure Enquiries Natural language enquiry Translate enquiry to select best Category Category ACategory BCategory CCategory D

13. Enquiry Aspect A Aspect B Aspect C Aspect D Aspect E Aspect F

14. Enquiry Aspect A Aspect B Aspect C Aspect D Aspect E Aspect F

15. Conclusion The results generated provide a good overview of the subject area. They incorporate the latest discussions in the subject areas There are multiple ways of measuring the strength of the relationship between different words to produce a “social network analysis” of the subject area