Hospital Resource Allocation Decisions When Market Prices Exceed Medicare Prices

Author(s):  
Yang Wang ◽  
Gerard Anderson
2020 ◽  
Vol 34 (3) ◽  
pp. 87-112
Author(s):  
Bei Dong ◽  
Stefanie L. Tate ◽  
Le Emily Xu

SYNOPSIS Regulations implemented by the SEC in 2003 and 2004 simultaneously shortened the financial statement filing deadlines and increased the time required for both the preparation of financial statements and the related audit of accelerated filers (AFs). However, there were indirect, unintended negative consequences for companies not subject to the regulations, namely, non-accelerated filers (NAFs). The new regulations imposed strains on auditor resources requiring auditors to make resource allocation decisions that negatively affected NAFs. We find that NAFs with an auditor who had a high proportion of AF clients (high-AF) had longer audit delays after the regulations were implemented than NAFs of an auditor with a low proportion of AF clients (low-AF). Further, we document that NAFs with high-AF auditors were more likely to change auditors than NAFs with low-AF auditors. Finally, NAFs that switched to auditors with less AFs experienced shorter audit delays after the auditor change. JEL Classifications: M42; M48.


Author(s):  
G.J. Melman ◽  
A.K. Parlikad ◽  
E.A.B. Cameron

AbstractCOVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke’s hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients.


Author(s):  
J. Robert Sims

Risk analysis has been used extensively to inform decisions throughout government and industry for many years. Many methodologies have been developed to perform these analyses, resulting in differences in terminology and approach that make it difficult to compare the results of an analysis in one field to that in another. In particular, many approaches result only in a risk ranking within a narrow area or field of interest, so the results cannot be compared to rankings in other areas or fields. However, dealing with terrorist threats requires prioritizing the allocation of homeland defense resources across a broad spectrum of possible targets. Therefore, a common approach is needed to allow comparison of risks. This presentation summarizes an approach that will allow the results of risk analyses based on using current methodologies to be expressed in a common format with common terminology to facilitate resource allocation decisions.


AGROFOR ◽  
2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Ruth MAGRETA ◽  
Henderson NG’ONG’OLA ◽  
Julius MANGISONI ◽  
Kennedy MACHILA ◽  
Sika GBEGBELEGBE

Using household data from Lilongwe districts, along with crop phenology, agronomic management and climatic data from Chitedze Research Station, the Target-MOTAD and DSSAT-CSM models examined the resource allocation decisions of smallholder farmers in maize farming systems under climate risk in Malawi. Specific aims were to evaluate the ability of DSSAT to predict and collate DTM and non-DTM yields under climatic risk and to use a bio-economic procedure developed using DSSAT and Target-MOTAD to explore the impact of climatic risk on allocation of resources to DTM and non-DTM production. The paper argues that higher average yields observed from DTM varieties make it the most optimal maize production plan, in maximizing household incomes, food security, and minimizing deviations from the mean while meeting the set target incomes of farmers compared to non-DTM varieties. The multidisciplinary nature of this paper has contributed to the body of research by providing a powerful analytical procedure of modelling farmers’ resource allocation decisions in maize based farming systems in Malawi. This study necessitates the use of a combination of biophysical and economic procedures when evaluating promising lines prior to variety release in order to identify the high yielding variety that will continuously bring sustained profits to the farmers amidst climate change.


Author(s):  
Ian Olver

IntroductionData linkage of population data sets often across jurisdictions or linking health data sets or health data with non-health data often involves balancing ethical principles such as privacy with beneficence as represented by the public good. Similar ethical dilemmas occur in health resource allocation decisions. The NHMRC have published a framework to guide policy on health resource allocation decisions that could be applied to ensure the justification of data linkage projects that is defensible as in the interest of the public good. Objectives and ApproachThe four main conditions for legitimacy of policy decisions about access to healthcare in a democracy with a public health system and limited resources wereexamined for their relevance to decisions about the use of public data and linking data sets. ResultsPublic policy decisions must be defensible and responsive to the interests of those affected. Decision-makers should articulate their reasoning and recommendations so that citizens can judge them. While the context of policy decisions will differ, their legitimacy depends upon (1) the transparency of the reasoning which should be free from conflicts of interest, the basis for decisions recorded and report widely, (2) the accountability of the decision-makers to the wider community, (3) the testability of the evidence used to inform the decision-making, which usually means that it will stand up to independent review and(4) the inclusive recognition of those the decision affects which often requires that the implications for disadvantaged groups are considered, even if they can’t always be accommodated. These conditions are interrelated but ensure that the good of society in general and not just specific dominant groups are accommodated. Conclusion / ImplicationsIt these principles are applied to decisions about data linkage projects they have clear applicability in society accepting data linkage projects having balanced the good against the ethical risks involved.


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