W^∗H: The Conceptual Model for Services

Abstract

Services as an emerging paradigm in modern information technology (IT) infrastructures underwent the first hype for service-oriented computing caused by Web services and the second hype by IT market pressures on large corporations (e.g. SAP), leading to standardisations incorporating logical-level specifications leaving much of the low-level details unaccounted for.The conception of a service needs a conceptual reflection. However, the service notation lacks a conceptual model. This gap is caused by the variety of aspects that must be reflected, such as the handling of the services as a collection of offerings, a proper annotation facility beyond ontologies, a tool to describe the service concept and the specification of the added value of a business user. Those requirements must be handled at the same time. Therefore, this chapter contributes to the development of a conceptual model of a service through a specification framework W^∗H and through an embedding framework to the concept-content-annotation triptych and Hermagoras of Temnos inquiry frames.

Appendix: Application of W∗H Specification Framework

An ophthalmologic research institute in the Netherlands initiated to establish a disease diagnosis decision support network (DDDSN) for ophthalmologists [1]. The initiative is on the creation of an expert community of ophthalmologists who contribute their knowledge to a repository for validating individual disease diagnoses. The motivation behind this DDDSN proposal is to harvest tangible benefit in the form of shared access to this unique image repository to achieve interoperability and location-independent decision support. The system is for the benefit of specialists all over the world to diagnose diseases at early stages and for treating their patients with up-to-date disease diagnosis decision support.

Medical diagnosis decision support is hardly in existence [25]. An impressive amount of medical images are daily generated in hospitals and medical centres. Consequently, the physicians have an increasing number of images to analyse manually [15]. In the practice, an ophthalmologist’s diagnoses are not contested or validated by another specialist. Access to a system that generates a second opinion has an added value in serving as an extra pair of eyes without violating the autonomy, professionalism or credibility of the ophthalmology profession. Such a system contributes vastly to the decision-making process in all medical fields [15, 25], and it has greater odds of acceptance by specialists.

An example of an ophthalmologic disease is age-related macular degeneration (ARMD). This disease results in a deterioration of the central retinal function and is the leading cause of blindness in people over 65 years of age in Europe and the USA. Because of the localisation of the macula in the centre of the retina, advanced age-related macular degeneration often leads to irreversible loss of social skills, like reading ability. Two forms of ARMD are distinguished: the atrophic form and the neovascular, exudative or wet form. There are 3,000 such diseases in the field of ophthalmology.

This advanced cross-disciplinary DDDSN system has three services for practicing ophthalmologists:

• (1) Decision support image base

• (2) Continuous knowledge enhancement of the image base

• (3) On-demand learning module for specialists and residents to update their diagnosis and knowledge (see Table 1).

(8) Application domain
∗ Application area (wherein)	– ARMD	– Maintenance	– Expert Learning Evt.
∗ Application case (wherefrom)	– During diagnosis	– New knowledge	– On-demand learning
∗ Problem (for-what)	– Treatment at early stages	– Disease evolution	– Personal-wallets
∗ Organizational unit (where)	– Ophthalmologic unit	– Image repository	– IT unit
∗ Triggering events (whence)	– Successful match	– New submission	– Request for learning
∗ IT {data, control computation}
∗ (what)	– Image comparison	– Knowledge enhancem.	– Learning module
∗ (how)	– Data	– Data	– Data
(9) Context
∗ System context (whereat)	– Location independent Ophtalm.’s workspace	– Location independent workspace	– Location independent L-environment
∗ Story context (where-about)	– Impaired vision of patient	– New knowledge	– Knowledge enhancem.
∗ Coexistence context(whither)	– Integrateable i-base sys.	– Integrated DDDSN	– Integrated to DDDSN
∗ Time context (when)	– On-demand	– On-demand	– On-demand

All fundus images that are acquired for clinical care at the institute are stored centrally in Topcon IMAGEnet i-base [33]. It provides access to these stored images from workstations around the hospital. The system contains a database of both images and patient records. Table 1 is a summarised version of the application of the W^∗H specification frame for service systems modelling.

Our approach to service modelling supports the evaluation of a service in dependence on the answers to the W^∗H questions. The evaluation can be based on SWOT analysis that evaluates the benefit of some artifact to the environment. Figure 9 surveys the evaluation sheet for a health service based on the profile of the service, the opportunities provided and the threats. The health care is based on a task portfolio within the current situation. Therefore, we can select the most appropriate tactics. This evaluation supports communication between the stakeholders.

Fig. 9

SWOT analysis for a health service

The model is now used for understanding what would be the benefit of the service, what kind of service can be expected, what changes must be made for an integration of the service and what is the added value. The typical situation is the nonexistence of a service model. Therefore, actors in health care act on partial information. This partial knowledge results in bad integration of a service and in waste of investment. Therefore, a service model will be an essential element of service deployment.