The concept of personalized medicine not only promises to enhance the life of patients and increase the quality of clinical practice and targeted care pathways, but also to lower overall healthcare costs through early-detection, prevention, accurate risk assessments and efficiencies in care delivery. Current inefficiencies are widely regarded as substantial enough to have a significant impact on the economies of major nations like the US and China, and, therefore the world economy. A recent OECD report estimates healthcare expenditure for some of the developed western and eastern nations to be anywhere from 10% to 18%, and growing (with the US at the highest). Personalized medicine aims to use state-of-the-art genomic technologies, rich medical record data, tissue and blood banks and clinical knowledge that will allow clinicians and payors to tailor treatments to individuals, thereby greatly reducing the costs of ineffective therapies incurred through the current trial and error clinical paradigm. Pivotal to the field are drugs that have been designed to target a specific molecular pathway that has gone wrong and results in a diseased condition and the diagnostic tests that allow clinicians to separate responders from non-responders. However, the truly personalized approach in medicine faces two major problems: complex biology and complex economics; the pathways involved in diseases are quite often not well understood, and most targeted drugs are very expensive. As a result of all current efforts to translate the concepts of personalized healthcare into the clinic, personalized medicine becomes participatory and this implies patient decisions about their own health. Such a new paradigm requires powerful tools to handle significant amounts of personal information with the approach to be known as “P4 medicine”, that is predictive, preventive, personalized and participatory. P4 medicine promises to increase the quality of clinical care and treatments and will ultimately save costs. The greatest challenges are economic, not scientific.Electronic supplementary materialThe online version of this article (doi:10.1186/1877-6566-7-1) contains supplementary material, which is available to authorized users.

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In this paper, we propose a new methodology based on economic models to provide Quality 0.f Service (QoS) guarantees to competing traffic classes (classes of sessions) in packet networks. We consider an economic model of a packet network where resources are priced. Traffic classes compete for network resources and they purchase them to satisfy their QoS needs. Our contributions are the following: 1) We provide a new definition for QoS provisioning based on economic models (Pareto efficiency). 2) We obtain the set of optimal resource allocations (Pareto optimal) which provide QoS guarantees to competing traffic classes. 9) We show the impact #on equilibrium prices and optimal allocations due to traffic load and variability, and QoS requirements. 4 ) We propose packet scheduling and admission policies to provide QoS guarantees to traffic classes based on available QoS and prices in the network.

One scenario of the future of computation populates the Internet with vast numbers of software agents providing, trading, and using a rich variety of information goods and services in an open, free-market economy. An essential task in such an economy is the retailing or brokering of information: gathering it from the right producers and distributing it to the right consumers. This article investigates one crucial aspect of brokers' dynamical behavior, their price-setting mechanisms, in the context of a simple information-filtering economy. We consider only the simplest cases in which a broker sets its price and product parameters based solely on the system's current state, without explicit prediction of the future. Analytical and numerical results show that the system's dynamical behavior in such "myopic" cases is generally an unending cycle of disastrous competitive "wars" in price/product space. These in turn are directly attributable to the existence of multiple peaks in the brokers' profitability landscapes, a feature whose generality is likely to extend far beyond our model.