Managing healthcare demand and capacity is especially difficult in the context of the COVID-19 pandemic, where limited intensive care resources can be overwhelmed by a large number of cases requiring admission in a short space of time. If patients are unable to access this specialist resource, then death is a likely outcome. In appreciating these ‘capacity-dependent’ deaths, this paper reports on the clinically-led development of a stochastic discrete event simulation model designed to capture the key dynamics of the intensive care admissions process for COVID-19 patients. With application to a large public hospital in England during an early stage of the pandemic, the purpose of this study was to estimate the extent to which such capacity-dependent deaths can be mitigated through demand-side initiatives involving non-pharmaceutical interventions and supply-side measures to increase surge capacity. Based on information available at the time, results suggest that total capacity-dependent deaths can be reduced by 75% through a combination of increasing capacity from 45 to 100 beds, reducing length of stay by 25%, and flattening the peak demand to 26 admissions per day. Accounting for the additional ‘capacity-independent’ deaths, which occur even when appropriate care is available within the intensive care setting, yields an aggregate reduction in total deaths of 30%. The modelling tool, which is freely available and open source, has since been used to support COVID-19 response planning at a number of healthcare systems within the UK National Health Service.Electronic supplementary materialThe online version of this article (10.1007/s10729-020-09511-7) contains supplementary material, which is available to authorized users.

The novel coronavirus 2019-nCoV first appeared in December 2019 in Wuhan, China. While most of the initial cases were linked to the Huanan Seafood Wholesale Market, person-to-person transmission has been verified. Given that a vaccine cannot be developed and deployed for at least a year, preventing further transmission relies upon standard principles of containment, two of which are the isolation of known cases and the quarantine of persons believed at high risk of exposure. This note presents probability models for assessing the effectiveness of case isolation and quarantine within a community during the initial phase of an outbreak with illustrations based on early observations from Wuhan.

In recent years, with the increasing aging of society, the number of patients with chronic heart disease, hypertension and diabetes has increased dramatically. It has guiding significance for the prevention and treatment by long-term monitoring of the physiological signs of patients with chronic diseases, scoring statistical data, and predicting the development trend of users' health. The work used the data collected by WIT120 system to analyze the pre-processed thick data based on adaptive k-means clustering method under the MapReduce framework, and the GM (1,1) grey model was used to predict the future health status of users. The simulation results have verified the effectiveness of the proposed algorithm.

In clinical applications, single modality images do not provide sufficient diagnostic information. Therefore, it is necessary to combine the advantages or complementarities of different modalities of images. In this paper, we propose an efficient medical image fusion system based on discrete wavelet transform and binary crow search optimization (BCSO) algorithm. Here, we consider two different patterns of images as the input of the system and the output is the fused image. In this approach, at first, to enhance the image, we apply a median filter which is used to remove the noise present in the input image. Then, we apply a discrete wavelet transform on both the input modalities. Then, the approximation coefficients of modality 1 and detailed coefficients of modality 2 are combined. Similarly, approximation coefficients of modality 2 and detailed coefficients of modality 1 are combined. Finally, we fuse the two modality information using novel fusion rule. The fusion rule parameters are optimally selected using binary crow search optimization (BCSO) algorithm. To evaluate the performance of the proposed method, we used different quality metrics such as structural similarity index measure (SSIM), Fusion Factor (FF), and entropy. The presented model shows superior results with 6.63 of entropy, 0.849 of SSIM and 5.9 of FF.

The aims of this study are to analyze the efficiency of players comprising the healthcare supply chain comparatively, and to investigate the relationship between these different types of efficiency, eventually seeking the way to optimize a healthcare supply chain. To achieve the aims, the players comprising a healthcare supply chain are classified either into manufacturers, distributors, wholesalers and retailers based on industry, or into the healthcare system and the non-healthcare system based on the system. The subjects are medical institutions comprising a healthcare supply chain. They have been selected as "The Healthcare Supply Chain Top 25" by Gartner for 5years in a row from 2012 to 2016. The bootstrap data envelopment analysis (DEA) and correlation analysis are used. The results are as follows. Players with relatively high competitive efficiency (wholesalers and retailers) can achieve high managerial efficiency even if they pay relatively less attention to supply chain efficiency while players with low competitive efficiency (manufacturers and distributors) should focus on supply chain efficiency to maintain managerial efficiency. In the healthcare system, no significant correlation was observed between managerial efficiency, supply chain efficiency and competitive efficiency. However, the analysis showed that there was a significant positive correlation between the managerial efficiency and the competitive efficiency in the non-healthcare system. This study expands the field of the healthcare supply chain management efficiency.

Quality and affordable healthcare is an important aspect in people's lives, particularly as they age. The rising elderly population in the United States (U.S.), with increasing number of chronic diseases, implies continuing healthcare later in life and the need for programs, such as U.S. Medicare, to help with associated medical expenses. Unfortunately, due to healthcare fraud, these programs are being adversely affected draining resources and reducing quality and accessibility of necessary healthcare services. The detection of fraud is critical in being able to identify and, subsequently, stop these perpetrators. The application of machine learning methods and data mining strategies can be leveraged to improve current fraud detection processes and reduce the resources needed to find and investigate possible fraudulent activities. In this paper, we employ an approach to predict a physician's expected specialty based on the type and number of procedures performed. From this approach, we generate a baseline model, comparing Logistic Regression and Multinomial Naive Bayes, in order to test and assess several new approaches to improve the detection of U.S. Medicare Part B provider fraud. Our results indicate that our proposed improvement strategies (specialty grouping, class removal, and class isolation), applied to different medical specialties, have mixed results over the selected Logistic Regression baseline model's fraud detection performance. Through our work, we demonstrate that improvements to current detection methods can be effective in identifying potential fraud.

Performance measurement plays an important role in the successful design and reform of regional healthcare management systems. In this study, we propose a hybrid data envelopment analysis (DEA) and game theory model for measuring the performance and productivity in the healthcare centers. The input and output variables associated with the efficiency of the healthcare centers are identified by reviewing the relevant literature, and then used in conjunction with the internal organizational data. The selected indicators and collected data are then weighted and prioritized with the help of experts in the field. A case study is presented to demonstrate the applicability and efficacy of the proposed model. The results reveal useful information and insights on the efficiency levels of the regional healthcare centers in the case study.

We study the impact of specialization on the operational efficiency of a multi-hospital system. The mixed outcomes of recently increasing hospital mergers and system re-configuration initiatives have raised the importance of studying such organizational changes from all the relevant perspectives. We consider two configuration scenarios for a multi-hospital system. The first scenario assumes that all the hospitals in the system are general, which implies they can provide care to all types of patients. In the alternative configuration, we specialize each hospital in certain level of care, which means they serve only specific types of patients. By considering an extensive number of possible settings for a multi-hospital system, we characterize the situations in which one scenario outperforms the other in terms of extending access of patients to care. Our results show that whenever the percent of patients with shorter length of stay in the system increases, specialization of healthcare services can maximize the accessibility of care. Also, if the patient load is balanced between all hospitals in the system, it seems more likely that all hospitals benefit from specialization. We conclude that the strategic decision of designing a multi-hospital system requires careful consideration of patient mix among arrivals, relative length of stay of patients, and distribution of patient load between hospitals.

In workforce scheduling for emergency medical services, it is important to ensure sufficient coverage at all time. Thus, planning has to consider unpredictable employee absences. To hedge against this type of uncertainty, on-call duties can be assigned to employees. In practice, these are often assigned ex-post based on the regular schedule. Little literature on workforce scheduling for ambulances or the planning of on-call duties exists. We present new set covering based integer programming formulations for cyclic stint-based staff scheduling with on-call duties. It is desirable for employees to work on consecutive days, called a stint, with a subsequent recovery period. On-call duties can be individually scheduled in-between two stints. Our model formulations integrate different cycle times for regular and on-call duties. A simple schedule that repeats quickly is devised for regular duties, while the on-call duty schedule rotates after each cycle to ensure fairness. The proposed models are applied to a local German emergency medical services provider. Using our stint-based model formulations, the planning complexity has been greatly reduced and reasonably large problem instances can be solved to optimality. Employee preferences, such as fairness, less work on weekends and longer recovery times, were taken into account to a high degree.

A challenge facing all ubiquitous clinic recommendation systems is that patients often have difficulty articulating their requirements. To overcome this problem, a ubiquitous clinic recommendation mechanism was designed in this study by mining the clinic preferences of patients. Their preferences were defined using the weights in the ubiquitous clinic recommendation mechanism. An integer nonlinear programming problem was solved to tune the values of the weights on a rolling basis. In addition, since it may take a long time to adjust the values of weights to their asymptotic values, the back propagation network (BPN)-response surface method (RSM) method is applied to estimate the asymptotic values of weights. The proposed methodology was tested in a regional study. Experimental results indicated that the ubiquitous clinic recommendation system outperformed several existing methods in improving the successful recommendation rate.