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Register a new userA Budget Feasible Mechanism for Hiring Doctors in E-Healthcare
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Added by
Sajal Mukhopadhyay
Publication date
2016
fields
Informatics Engineering
and research's language is
English
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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 advancement 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.
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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.
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, smartphone, 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.
We develop and extend a line of recent works on the design of mechanisms for heterogeneous tasks assignment problem in crowdsourcing. The budgeted market we consider consists of multiple task requesters and multiple IoT devices as task executers; where each task requester is endowed with a single distinct task along with the publicly known budget. Also, each IoT device has valuations as the cost for executing the tasks and quality, which are private. Given such scenario, the objective is to select a subset of IoT devices for each task, such that the total payment made is within the allotted quota of the budget while attaining a threshold quality. For the purpose of determining the unknown quality of the IoT devices, we have utilized the concept of peer grading. In this paper, we have carefully crafted a truthful budget feasible mechanism; namely TUBE-TAP for the problem under investigation that also allows us to have the true information about the quality of the IoT devices. The simulations are performed in order to measure the efficacy of our proposed mechanism.
We revisit the well-studied problem of budget-feasible procurement, where a buyer with a strict budget constraint seeks to acquire services from a group of strategic providers (the sellers). During the last decade, several strategyproof budget-feasible procurement auctions have been proposed, aiming to maximize the value of the buyer, while eliciting each sellers true cost for providing their service. These solutions predominantly take the form of randomized sealed-bid auctions: they ask the sellers to report their private costs and then use randomization to determine which subset of services will be procured and how much each of the chosen providers will be paid, ensuring that the total payment does not exceed budget. Our main result in this paper is a novel method for designing budget-feasible auctions, leading to solutions that outperform the previously proposed auctions in multiple ways. First, our solutions take the form of descending clock auctions, and thus satisfy a list of properties, such as obvious strategyproofness, group strategyproofness, transparency, and unconditional winner privacy; this makes these auctions much more likely to be used in practice. Second, in contrast to previous results that heavily depend on randomization, our auctions are deterministic. As a result, we provide an affirmative answer to one of the main open questions in this literature, asking whether a deterministic strategyproof auction can achieve a constant approximation when the buyers valuation function is submodular over the set of services. In addition, we also provide the first deterministic budget-feasible auction that matches the approximation bound of the best-known randomized auction for the class of subadditive valuations. Finally, using our method, we improve the best-known approximation factor for monotone submodular valuations, which has been the focus of most of the prior work.
In the budget-feasible allocation problem, a set of items with varied sizes and values are to be allocated to a group of agents. Each agent has a budget constraint on the total size of items she can receive. The goal is to compute a feasible allocation that is envy-free (EF), in which the agents do not envy each other for the items they receive, nor do they envy a charity, who is endowed with all the unallocated items. Since EF allocations barely exist even without budget constraints, we are interested in the relaxed notion of envy-freeness up to one item (EF1). The computation of both exact and approximate EF1 allocations remains largely open, despite a recent effort by Wu et al. (IJCAI 2021) in showing that any budget-feasible allocation that maximizes the Nash Social Welfare (NSW) is 1/4-approximate EF1. In this paper, we move one step forward by showing that for agents with identical additive valuations, a 1/2-approximate EF1 allocation can be computed in polynomial time. For the uniform-budget and two-agent cases, we propose efficient algorithms for computing an exact EF1 allocation. We also consider the large budget setting, i.e., when the item sizes are infinitesimal compared with the agents budgets, and show that both the NSW maximizing allocation and the allocation our polynomial-time algorithm computes have an approximation close to 1 regarding EF1.
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