No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Fair Healthcare Rationing to Maximize Dynamic Utilities
Abstract
Allocation of scarce healthcare resources under limited logistic and infrastructural facilities is a major issue in the modern society. We consider the problem of allocation of healthcare resources like vaccines to people or hospital beds to patients in an online manner. Our model takes into account the arrival of resources on a day-to-day basis, different categories of agents, the possible unavailability of agents on certain days, and the utility associated with each allotment as well as its variation over time.We propose a model where priorities for various categories are modelled in terms of utilities of agents. We give online and offline algorithms to compute an allocation that respects eligibility of agents into different categories, and incentivizes agents not to hide their eligibility for some category. The offline algorithm gives an optimal allocation while the online algorithm gives an approximation to the optimal allocation in terms of total utility. Our algorithms are efficient, and maintain fairness among different categories of agents. Our models have applications in other areas like refugee settlement and visa allocation. We evaluate the performance of our algorithms on real-life and synthetic datasets. The experimental results show that the online algorithm is fast and performs better than the given theoretical bound in terms of total utility. Moreover, the experimental results confirm that our utility-based model correctly captures the priorities of categories.
Data-driven refugee assignment
The continuing refugee crisis has made it necessary for governments to find ways to resettle individuals and families in host communities. Bansak et al. used a machine learning approach to develop an algorithm for geographically placing refugees to optimize their overall employment rate. The authors developed and tested the algorithm on segments of registry data from the United States and Switzerland. The algorithm improved the employment prospects of refugees in the United States by ∼40% and in Switzerland by ∼75%.
Science , this issue p. 325
In response to the coronavirus disease 2019 (COVID-19) pandemic and the societal disruption it has brought, national governments and the international community have invested billions of dollars and immense amounts of human resources to develop a safe and effective vaccine in an unprecedented time frame. Vaccination against this novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), offers the possibility of significantly reducing severe morbidity and mortality and transmission when deployed alongside other public health strategies and improved therapies.
Due to COVID-19’s strain on health systems across the globe, triage protocols determine how to allocate scarce medical resources with the worthy goal of maximising the number of lives saved. However, due to racial biases and long-standing health inequities, the common method of ranking patients based on impersonal numeric representations of their morbidity is associated with disproportionately pronounced racial disparities. In response, policymakers have issued statements of solidarity. However, translating support into responsive COVID-19 policy is rife with complexity. Triage does not easily lend itself to race-based exceptions. Reordering triage queues based on an individual patient’s racial affiliation has been considered but may be divisive and difficult to implement. And while COVID-19 hospital policies may be presented as rigidly focused on saving the most lives, many make exceptions for those deemed worthy by policymakers such as front-line healthcare workers, older physicians, pregnant women and patients with disabilities. These exceptions demonstrate creativity and ingenuity—hallmarks of policymakers’ abilities to flexibly respond to urgent societal concerns—which should also be extended to patients of colour. This paper dismantles common arguments against the confrontation of racial inequity within COVID-19 triage protocols, highlights concerns related to existing proposals and proposes a new paradigm to increase equity when allocating scarce COVID-19 resources.
This piece surveys a recent body of studies in “matching with constraints.” It discusses relationship with traditional matching theory and new applications, and concludes with some open questions.
In an earlier work (Kamada and Kojima 2015), we introduced “matching with constraints,” a two-sided matching problem in which the market is subject to feasibility constraints. That paper proposed and analyzed a possible solution to this problem and discussed various real-market applications of our results. The present paper reorganizes some of the findings from our more recent effort, based on Kamada and Kojima (2016a, b).
Many real matching markets are subject to distributional constraints. These constraints often take the form of restrictions on the numbers of agents on one side of the market matched to certain subsets on the other side. Real-life examples include restrictions on regions in medical matching, academic master's programs in graduate admission, and state-financed seats for college admission. Motivated by these markets, we study design of matching mechanisms under distributional constraints. We show that existing matching mechanisms suffer from inefficiency and instability, and propose a mechanism that is better in terms of efficiency, stability, and incentives while respecting the distributional constraints.
In this paper we develop and estimate a model to explain variations in immigration to the United States by source country since the early 1970s. The explanatory variables include ratios to the United States of source country income and education as well as relative inequality. In addition, we incorporate the stock of previous immigrants and a variety of variables representing different dimensions of the immigration quotas set by policy. We use the results to shed light on the impact of policy by simulating the effects of the key changes in immigration policy since the late 1970s. We also examine the factors that influenced the composition of U.S. immigration by source region over the entire period. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.
A central issue in school choice is the design of a student assignment mechanism. Education literature provides guidance for the design of such mechanisms but does not offer specific mechanisms. The flaws in the existing school choice plans result in appeals by unsatisfied parents. We formulate the school choice problem as a mechanism design problem and analyze some of the existing school choice plans including those in Boston, Columbus, Minneapolis, and Seattle. We show that these existing plans have serious shortcomings, and offer two alternative mechanisms each of which may provide a practical solution to some critical school choice issues.
The hardest questions doctors may face: who will be saved? Who won
- S Fink
Immigration policy, national origin, and immigrant skills: a comparison of Canada and the united states. Small differences that matter: labor markets and income maintenance in Canada and the United States
- G J Borjas
Refugee Resettlement
- D Delacrétaz
- S D Kominers
- A Teytelboym
Quotas and quality: the effect of h-1b visa restrictions on the pool of prospective undergraduate students from abroad
- T Kato
- C Sparber