By Luis M. Camarinha-Matos (auth.), Américo Azevedo (eds.)
International Federation for info Processing
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Extra resources for Innovation in Manufacturing Networks: Eighth IFIP International Conference on Information Technology for Balanced Automation Systems, Porto, Portugal, June 23–25, 2008
Sample text
Fuzzy reasoning also prRYLGHVPHWKRGVWR³DVVHPEOH´SDUWLDOLQIRUPDWLRQVRLQWKLV case the partial information about compatible values and incompatible values is used in order to reason about the alignment between Value Systems. Belief Networks capture relations (which may be uncertain, ambiguous, or imprecise) between a set of variables. Based on the set of states that each variable can have and its belief level (prior probability) it is possible to infer how the prior probability is updated by new data items (Jensen, 1996).
To the outsider and given the growing economic importance of healthcare this large and notionally integrated market place appears to be an ideal innovation opportunity for the UK economy and business to work with healthcare delivery to identify and create the new healthcare products both capable of success in world markets and of giving real Feeling the pain: Disruptive innovation in healthcare markets 27 patient benefit. This opportunity was formally recognized in the UK with the publication of the Cooksey Review (Cooksey, 2006).
The most well known example of a disruptive technology in healthcare is less invasive medicine, particularly cardiac angioplasty and ultimately use of the stent (Hourd and Williams, 2007). Within the UK, Independent Sector Treatment Centres, which are commercially run, focused treatment centres operating in parallel to the mainstream NHS that take NHS patients, are another more service oriented instance of disruptive innovation. Disruptive technologies have three distinctive characteristics: they target non-consumers; they have a novel business model or value proposition and they drive out the incumbent in a niche where they deliver equivalent quality at a lower price.