The value of probabilistic forecasting in emergency medical resource planning under uncertainty

PurposeMost epidemic transmission forecasting methods can only provide deterministic outputs. This study aims to show that probabilistic forecasting, in contrast, is suitable for stochastic demand modeling and emergency medical resource planning under uncertainty.Design/methodology/approachTwo probabilistic forecasting methods, i.e. quantile regression convolutional neural network and kernel density estimation, are combined to provide the conditional quantiles and conditional densities of infected populations. The value of probabilistic forecasting in improving decision performances and controlling decision risks is investigated by an empirical study on the emergency medical resource planning for the COVID-19 pandemic.FindingsThe managerial implications obtained from the empirical results include (1) the optimization models using the conditional quantile or the point forecasting result obtain better results than those using the conditional density; (2) for sufficient resources, decision-makers' risk preferences can be incorporated to make tradeoffs between the possible surpluses and shortages of resources in the emergency medical resource planning at different quantile levels; and (3) for scarce resources, the differences in emergency medical resource planning at different quantile levels greatly decrease or disappear because of the existing of forecasting errors and supply quantity constraints.Originality/valueVery few studies concern probabilistic epidemic transmission forecasting methods, and this is the first attempt to incorporate deep learning methods into a two-phase framework for data-driven emergency medical resource planning under uncertainty. Moreover, the findings from the empirical results are valuable to select a suitable forecasting method and design an efficient emergency medical resource plan.

COVID-19-associated encephalitis or Creutzfeldt–Jakob disease: a case report

Background: Accurate diagnosis and management of patients with rapidly progressive dementia may be challenging during the COVID-19 pandemic, which has negatively influenced the diagnostic performances, medical resource allocation and routine care for all non-COVID-19 diseases. Case Presentation: We herein present a case of a 57‐year‐old male with rapidly progressive cognitive decline, headache, diplopia, myalgia, unsteady gait, aggression, depression, insomnia, hallucinations and delusions of persecution. COVID-19-associated encephalitis was briefly considered as a differential diagnosis. However, this hypothesis was rejected upon further investigation. A final diagnosis of sporadic Creutzfeldt–Jakob disease was made. Conclusion: A timely and accurate diagnosis of Creutzfeldt–Jakob disease gives patients and their families the chance to receive a good standard of healthcare and avoid extensive evaluations for other conditions.

Reliability Aware Medical Resource Allocation for Health Care Industrial Internet of Things (IIoT) Using Tabu Search and Alo Algorithm

The medical data integrating system allows the hospital’s resource constraints to be more effectively utilized. Moreover, by improving the resource management and allocation method, the hospital’s operations may be more organized, and the effectiveness of healthcare can be improved without breaking the medical agreements. Significant catastrophes frequently result in a scarcity of important medical resources, hence resource allocation must be optimized to enhance the performance of relief operations. The two main requirements for healthcare industrial applications are timeliness and reliability. Therefore, in the architecture of a smart healthcare industry these two criteria should be thought carefully. A well-known approach for the security and timeliness in the intelligent healthcare industry is to utilize hybrid IoT and Cloud technologies. Yet it is not enough to protect their hard deadlines for tight time-sensitive applications utilizing cloud. A potential way to cope with efficiency and latency criteria for strict time-sensitive applications is the deployment of intermediate processing layer IoT that can be linked between healthcare industrial plant and cloud. The purpose of this article is to develop a healthcare Industrial IoT system that include a medical resource allocation scheme for dividing a certain amount of workload between those multiple computing layers which are dependable and time consuming. IOT is integration of microprocessors and controller Workload partitioning can give us important design decisions to specify how many computing resources are needed in cooperation with IoT to develop a local private cloud. Ant lion optimization (ALO) and TABU Look for the right route. The simplest method of deciding the distance to a destination is to choose an OLSR routing protocol depending on the meaning or measure it requires. The method proposed in the distribution and data storage of medical resources is very efficient.

Team-Specific Human Capital and Team Performance: Evidence from Doctors

This paper studies whether team members’ past collaboration creates team-specific human capital and influences current team performance. Using administrative Medicare claims for two heart procedures, I find that shared work experience between the doctor who performs the procedure (“proceduralist”) and the doctors who provide care to the patient during the hospital stay for the procedure (“physicians”) reduces patient mortality rates. A one standard deviation increase in proceduralist-physician shared work experience leads to a 10–14 percent reduction in patient 30-day mortality. Patient medical resource use also declines with shared work experience, even as survival improves. (JEL I10, J24, M12, M54)

Machine Learning Medical Resource Allocation

In the last decade, machine learning has become very interesting, driven by cheaper computing power and costly storage—so that growing numbers of data can be saved, processed and analysed effectively. Enhanced algorithms are designed and used to identify hidden insights and correlations between non-human data elements in broad datasets. These insights help companies to better decide and optimize key indicators of interest. Machine learning is becoming more common because of the agnostic use of learning algorithms. The paper presents a number of machinery and auxiliary tumour processes to assign health resources, and proposes a number of new ways to use these resources at the time of artificial intelligence in order to make human life part of this process and explore the good conditions which are shared by both the medical and computer industries.