Exploring the diffusion of patient innovations : a multiple-case study

A significant number of patients develop medical devices, treatments, or therapies for personal use. However, most of these patient innovations do not diffuse because the developers do not share them. This problem is known as a form of market failure and requires policy intervention. Yet, little is known about why and when patient innovators decide to diffuse their solutions. A better understanding of these mechanisms is essential to address the problem of market failure. Hence, this study aims to learn from cases of diffused solutions to derive propositions and practical recommendations about how to increase diffusion rates. Nine cases of diffused innovations have been collected. Information was gathered through semi-structured interviews and through additional information from online resources. The results suggest that the diffusion decision is preceded by an awareness of the solution’s value to other patients. The subsequent diffusion is motivated by the variable of life enrichment: being aware of the solution’s value to other patients, innovators find an opportunity to pursue a meaningful purpose or engage in a fulfilling task. Eventually, the diffusion is moderated by the access to complementary assets. Addressing the identified dimensions, digital health platforms, governments and medical professionals can contribute to an environment, which makes innovators become aware of their solutions’ value and supports them in solution sharing.Um número significativo de doentes desenvolve dispositivos médicos, tratamentos ou terapias para uso pessoal. Contudo, a maioria destas inovações não é difundida porque os inovadores não as partilham. Este problema é conhecido como uma forma de falha do mercado e requer uma política de intervenção. No entanto, existe pouca informação sobre a razão e a altura em que doentes inovadores decidem difundir as suas soluções. Uma melhor compreensão deste processo é essencial para resolver o problema da falha de mercado. Assim, este estudo tem o objetivo de aprender através de casos de soluções difundidas a definir teorias e recomendações práticas sobre como aumentar as taxas de difusão. Foram recolhidos nove casos de inovações difundidas. Foram recolhidas evidências através de entrevistas semiestruturadas e de informações adicionais disponíveis em recursos online. Os resultados sugerem que a decisão de difusão é precedida pela consciencialização do valor da solução para outros. A difusão subsequente é motivada pela variável de realização pessoal: cientes do valor das suas soluções para os outros doentes, os inovadores encontram uma motivação para alcançar um propósito significativo ou para se envolver numa tarefa gratificante. Eventualmente, a difusão é moderada pela disponibilidade de ativos complementares. A análise das dimensões identificadas permite que, através de plataformas digitais de saúde, os governos e os profissionais de saúde contribuam para um ambiente que torna os inovadores conscientes do valor das suas soluções e que os apoia na partilha das mesmas

Hard Problems on Random Graphs

Many graph properties are expressible in first order logic. Whether a graph contains a clique or a dominating set of size k are two examples. For the solution size as its parameter the first one is W[1]-complete and the second one W[2]-complete meaning that both of them are hard problems in the worst-case. If we look at both problem from the aspect of average-case complexity, the picture changes. Clique can be solved in expected FPT time on uniformly distributed graphs of size n, while this is not clear for Dominating Set. We show that it is indeed unlikely that Dominating Set can be solved efficiently on random graphs: If yes, then every first-order expressible graph property can be solved in expected FPT time, too. Furthermore, this remains true when we consider random graphs with an arbitrary constant edge probability. We identify a very simple problem on random matrices that is equally hard to solve on average: Given a square boolean matrix, are there k rows whose logical AND is the zero vector? The related Even Set problem on the other hand turns out to be efficiently solvable on random instances, while it is known to be hard in the worst-case

First-Order Model-Checking in Random Graphs and Complex Networks

Complex networks are everywhere. They appear for example in the form of biological networks, social networks, or computer networks and have been studied extensively. Efficient algorithms to solve problems on complex networks play a central role in today's society. Algorithmic meta-theorems show that many problems can be solved efficiently. Since logic is a powerful tool to model problems, it has been used to obtain very general meta-theorems. In this work, we consider all problems definable in first-order logic and analyze which properties of complex networks allow them to be solved efficiently. The mathematical tool to describe complex networks are random graph models. We define a property of random graph models called $\alpha$-power-law-boundedness. Roughly speaking, a random graph is $\alpha$-power-law-bounded if it does not admit strong clustering and its degree sequence is bounded by a power-law distribution with exponent at least $\alpha$ (i.e. the fraction of vertices with degree $k$ is roughly $O(k^{-\alpha})$). We solve the first-order model-checking problem (parameterized by the length of the formula) in almost linear FPT time on random graph models satisfying this property with $\alpha \ge 3$. This means in particular that one can solve every problem expressible in first-order logic in almost linear expected time on these random graph models. This includes for example preferential attachment graphs, Chung-Lu graphs, configuration graphs, and sparse Erd\H{o}s-R\'{e}nyi graphs. Our results match known hardness results and generalize previous tractability results on this topic

Indiscernibles and Flatness in Monadically Stable and Monadically NIP Classes

Monadically stable and monadically NIP classes of structures were initially studied in the context of model theory and defined in logical terms. They have recently attracted attention in the area of structural graph theory, as they generalize notions such as nowhere denseness, bounded cliquewidth, and bounded twinwidth. Our main result is the - to the best of our knowledge first - purely combinatorial characterization of monadically stable classes of graphs, in terms of a property dubbed flip-flatness. A class C of graphs is flip-flat if for every fixed radius r, every sufficiently large set of vertices of a graph G ? C contains a large subset of vertices with mutual distance larger than r, where the distance is measured in some graph G\u27 that can be obtained from G by performing a bounded number of flips that swap edges and non-edges within a subset of vertices. Flip-flatness generalizes the notion of uniform quasi-wideness, which characterizes nowhere dense classes and had a key impact on the combinatorial and algorithmic treatment of nowhere dense classes. To obtain this result, we develop tools that also apply to the more general monadically NIP classes, based on the notion of indiscernible sequences from model theory. We show that in monadically stable and monadically NIP classes indiscernible sequences impose a strong combinatorial structure on their definable neighborhoods. All our proofs are constructive and yield efficient algorithms

Information Management and Market Engineering. Vol. II

The research program Information Management and Market Engineering focuses on the analysis and the design of electronic markets. Taking a holistic view of the conceptualization and realization of solutions, the research integrates the disciplines business administration, economics, computer science, and law. Topics of interest range from the implementation, quality assurance, and advancement of electronic markets to their integration into business processes and legal frameworks