Hiring expert consultants in e-healthcare: an analytics-based two sided matching approach

Very often in some censorious healthcare scenario, there may be a need to have some expert consultancies (especially by doctors) that are not available in-house to the hospitals. Earlier, this interesting healthcare scenario of hiring the expert consultants (mainly doctors) from outside of the hospitals had been studied with the robust concepts of mechanism design with money and mechanism design without money. In this paper, we explore the more realistic two sided matching market in our healthcare set-up. In this, the members of the two participating communities, namely the patients and the doctors are revealing the strict preference ordering over the members of the opposite community for a stipulated amount of time. We assume that the patients and doctors are strategic in nature. With the theoretical analysis, we demonstrate that the TOMHECs, that results in the stable allocation of doctors to the patients, satisfies the several economic properties such as strategy-proof-ness (or truthfulness) and optimality. Further, the analytically based analysis of our proposed mechanisms i.e. RAMHECs and TOMHECs are carried out on the ground of the expected distance of the allocation done by the mechanisms from the top most preference. The proposed mechanisms are also validated with the help of exhaustive experiments.Peer ReviewedPostprint (author's final draft

An iconic language for the graphical representation of medical concepts

<p>Abstract</p> <p>Background</p> <p>Many medication errors are encountered in drug prescriptions, which would not occur if practitioners could remember the drug properties. They can refer to drug monographs to find these properties, however drug monographs are long and tedious to read during consultation. We propose a two-step approach for facilitating access to drug monographs. The first step, presented here, is the design of a graphical language, called VCM.</p> <p>Methods</p> <p>The VCM graphical language was designed using a small number of graphical primitives and combinatory rules. VCM was evaluated over 11 volunteer general practitioners to assess if the language is easy to learn, to understand and to use. Evaluators were asked to register their VCM training time, to indicate the meaning of VCM icons and sentences, and to answer clinical questions related to randomly generated drug monograph-like documents, supplied in text or VCM format.</p> <p>Results</p> <p>VCM can represent the various signs, diseases, physiological states, life habits, drugs and tests described in drug monographs. Grammatical rules make it possible to generate many icons by combining a small number of primitives and reusing simple icons to build more complex ones. Icons can be organized into simple sentences to express drug recommendations. Evaluation showed that VCM was learnt in 2 to 7 hours, that physicians understood 89% of the tested VCM icons, and that they answered correctly to 94% of questions using VCM (versus 88% using text, <it>p </it>= 0.003) and 1.8 times faster (<it>p </it>< 0.001).</p> <p>Conclusion</p> <p>VCM can be learnt in a few hours and appears to be easy to read. It can now be used in a second step: the design of graphical interfaces facilitating access to drug monographs. It could also be used for broader applications, including the design of interfaces for consulting other types of medical document or medical data, or, very simply, to enrich medical texts.</p

Defining the role of common variation in the genomic and biological architecture of adult human height

Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated ∼2,000, ∼3,700 and ∼9,500 SNPs explained ∼21%, ∼24% and ∼29% of phenotypic variance. Furthermore, all common variants together captured 60% of heritability. The 697 variants clustered in 423 loci were enriched for genes, pathways and tissue types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/β-catenin and chondroitin sulfate-related genes. We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid. Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants