ترغب بنشر مسار تعليمي؟ اضغط هنا

Hiring Doctors in E-Healthcare With Zero Budget

131   0   0.0 ( 0 )
 نشر من قبل Sajal Mukhopadhyay
 تاريخ النشر 2016
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

The doctors (or expert consultants) are the critical resources on which the success of critical medical cases are heavily dependent. With the emerging technologies (such as video conferencing, smartphone, etc.) this is no longer a dream but a fact, that for critical medical cases in a hospital, expert consultants from around the world could be hired, who may be present physically or virtually. Earlier, this interesting situation by taking the expert consultancies from outside the hospital had been studied, but under monetary perspective. In this paper, for the first time, to the best of our knowledge, we investigate the situation, where the below income group (BIG) people of the society may be served efficiently through the expert consultancy by the renowned doctors from outside of the hospital under zero budget. This will help us saving many lives which will fulfil the present day need of biomedical research. We propose three mechanisms: Random pick-assign mechanism (RanPAM), Truthful optimal allocation mechanism (TOAM), and Truthful optimal allocation mechanism for incomplete preferences (TOAM-IComP) to allocate the doctor to the patient. With theoretical analysis, we demonstrate that the TOAM is strategy-proof, and exhibits a unique core property. The mechanisms are also validated with exhaustive experiments.



قيم البحث

اقرأ أيضاً

Throughout the past decade, there has been an extensive research on scheduling the hospital resources such as the operation theatre(s) (OTs) and the experts (such as nurses, doctors etc.) inside the hospitals. With the technological growth, mainly ad vancement in communication media (such as smart phones, video conferencing, smart watches etc.) one may think of taking the expertise by the doctors (distributed around the globe) from outside the in-house hospitals. Earlier this interesting situation of hiring doctors from outside the hospitals has been studied from monetary (with patient having infinite budget) and non-monetary perspectives in strategic setting. In this paper, the more realistic situation is studied in terms of hiring the doctors from outside the hospital when a patient is constrained by budget. Our proposed mechanisms follow the two pass mechanism design framework each consisting of allocation rule and payment rule. Through simulations, we evaluate the performance and validate our proposed mechanisms.
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 hospital. With the advancement in technologies (such as video conferencing, smartp hone, etc.), it has become reality that, for the critical medical cases in the hospitals, expert consultants (ECs) from around the world could be hired, who will serve the patients by their physical or virtual presence. Earlier, this interesting healthcare scenario of hiring the ECs (mainly doctors) from outside of the hospitals had been studied with the robust concepts of mechanism design with or without money. We have tried to model the ECs (mainly doctors) hiring problem as a two-sided matching problem. In this paper, for the first time, to the best of our knowledge, we explore the more realistic two-sided matching in our set-up, where the members of the two participating communities, namely patients and doctors are revealing the strict preference ordering over all the members of the opposite community for a stipulated amount of time. We assume that patients and doctors are strategic in nature. With the theoretical analysis, we demonstrate that the proposed mechanism that results in a stable allocation of doctors to patients is strategy-proof (or truthful) and optimal. The proposed mechanism is also validated with exhaustive experiments.
Computational advertising has been studied to design efficient marketing strategies that maximize the number of acquired customers. In an increased competitive market, however, a market leader (a leader) requires the acquisition of new customers as w ell as the retention of her loyal customers because there often exists a competitor (a follower) who tries to attract customers away from the market leader. In this paper, we formalize a new model called the Stackelberg budget allocation game with a bipartite influence model by extending a budget allocation problem over a bipartite graph to a Stackelberg game. To find a strong Stackelberg equilibrium, a standard solution concept of the Stackelberg game, we propose two algorithms: an approximation algorithm with provable guarantees and an efficient heuristic algorithm. In addition, for a special case where customers are disjoint, we propose an exact algorithm based on linear programming. Our experiments using real-world datasets demonstrate that our algorithms outperform a baseline algorithm even when the follower is a powerful competitor.
During the last decade, scheduling the healthcare services (such as staffs and OTs) inside the hospitals have assumed a central role in healthcare. Recently, some works are addressed in the direction of hiring the expert consultants (mainly doctors) for the critical healthcare scenarios from outside of the medical unit, in both strategic and non-strategic settings under monetary and non-monetary perspectives. In this paper, we have tried to investigate the experts hiring problem with multiple patients and multiple experts; where each patient reports a preferred set of experts which is private information alongwith their private cost for consultancy. To the best of our knowledge, this is the first step in the direction of modeling the experts hiring problem in the combinatorial domain. In this paper, the combinatorial auction based scheme is proposed for hiring experts from outside of the hospitals to have expertise by the preferred doctors set to the patients.
In this paper, we design gross product maximization mechanisms which incentivize users to upload high-quality contents on user-generated-content (UGC) websites. We show that, the proportional division mechanism, which is widely used in practice, can perform arbitrarily bad in the worst case. The problem can be formulated using a linear program with bounded and increasing variables. We then present an $O(nlog n)$ algorithm to find the optimal mechanism, where n is the number of players.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا