Resource planning strategies for healthcare systems during a pandemic

Risk analysis and assessment method for infectious diseases based on information entropy theory

Infectious diseases risk is directly related to human life safety. After the COVID-19 pandemic, people have paid unprecedented attention to the risk of infectious diseases. Compared with treatment after the outbreak of the epidemic, identifying the influencing factors of infectious disease risk and quantitatively analyzing and assessing infectious disease risk before the outbreak of the epidemic plays an equally important role. This article focuses on the risk of irregular outbreaks of infectious diseases. On the one hand, a method based on information gain is proposed to calculate the weight of environmental factors directly related to infectious disease risk, to clarify the correlation between environmental factors and infectious disease risk. On the other hand, the risk calculation method based on risk weight number is proposed to calculate the risk level of different infectious diseases under the influence of specific environmental factors. Finally, the effectiveness and feasibility of the proposed method are verified through case analysis and discussion. By comparing it with other risk assessment methods, the advantages and disadvantages of the proposed method are demonstrated.

Precision epidemiology at the nexus of mathematics and nanotechnology: Unraveling the dance of viral dynamics

The intersection of mathematical modeling, nanotechnology, and epidemiology marks a paradigm shift in our battle against infectious diseases, aligning with the focus of the journal on the regulation, expression, function, and evolution of genes in diverse biological contexts. This exploration navigates the intricate dance of viral transmission dynamics, highlighting mathematical models as dual tools of insight and precision instruments, a theme relevant to the diverse sections of Gene. In the context of virology, ethical considerations loom large, necessitating robust frameworks to protect individual rights, an aspect essential in infectious disease research. Global collaboration emerges as a critical pillar in our response to emerging infectious diseases, fortified by the predictive prowess of mathematical models enriched by nanotechnology. The synergy of interdisciplinary collaboration, training the next generation to bridge mathematical rigor, biology, and epidemiology, promises accelerated discoveries and robust models that account for real-world complexities, fostering innovation and exploration in the field. In this intricate review, mathematical modeling in viral transmission dynamics and epidemiology serves as a guiding beacon, illuminating the path toward precision interventions, global preparedness, and the collective endeavor to safeguard human health, resonating with the aim of advancing knowledge in gene regulation and expression.

Evaluation of healthcare-related factors influencing mental health of Taiwanese citizens among different age groups

The issue of mental health has gained heightened recognition as a significant public health concern due to its potential to significantly impact various aspects of individuals' lives. Numerous factors may influence mental health, and this study seeks to investigate and compare potential healthcare-related factors that affect the mental health of Taiwanese individuals across different age groups. Data for this study were taken from the Taiwan Social Change Survey (TSCS), conducted in 2021. Descriptive statistics were calculated to compare the three age groups. Then, multiple regression models were constructed with mental health conditions as the dependent variable and demographics and other key healthcare-related components as independent variables, respectively. Results showed that, among the three age groups, the middle-aged adults had the highest BMI, and the older adults had significantly better mental health. As compared with the other age groups, the older adults had significantly better perceptions of fair distribution of healthcare resources, and their trust in the healthcare system was the highest. With regard to searching for online healthcare information, the frequency reported by the older adults was the lowest. The regression model showed that, religious belief, trust in the healthcare system and searching for online healthcare information were significantly associated with mental health of middle-aged adults. In the younger group, searching for online healthcare information was significantly negatively associated with mental health. The study's findings provide insight into how to provide Taiwanese citizens of different age groups with proper and targeted mental health promotion activities.

From numbers to medical knowledge: harnessing combinatorial data patterns to predict COVID-19 resource needs and distinguish patient subsets

Background

The COVID-19 pandemic intensified the use of scarce resources, including extracorporeal membrane oxygenation (ECMO) and mechanical ventilation (MV). The combinatorial features of the immune system may be considered to estimate such needs and facilitate continuous open-ended knowledge discovery.

Materials and methods

Computer-generated distinct data patterns derived from 283 white blood cell counts collected within five days after hospitalization from 97 COVID-19 patients were used to predict patient's use of hospital resources.

Results

Alone, data on separate cell types-such as neutrophils-did not identify patients that required MV/ECMO. However, when structured as multicellular indicators, distinct data patterns displayed by such markers separated patients later needing or not needing MV/ECMO. Patients that eventually required MV/ECMO also revealed increased percentages of neutrophils and decreased percentages of lymphocytes on admission.

Discussion/conclusion

Future use of limited hospital resources may be predicted when combinations of available blood leukocyte-related data are analyzed. New methods could also identify, upon admission, a subset of COVID-19 patients that reveal inflammation. Presented by individuals not previously exposed to MV/ECMO, this inflammation differs from the well-described inflammation induced after exposure to such resources. If shown to be reproducible in other clinical syndromes and populations, it is suggested that the analysis of immunological combinations may inform more and/or uncover novel information even in the absence of pre-established questions.

Two-stage multi-objective optimization for ICU bed allocation under multiple sources of uncertainty

Due to the impact of COVID-19, a significant influx of emergency patients inundated the intensive care unit (ICU), and as a result, the treatment of elective patients was postponed or even cancelled. This paper studies ICU bed allocation for three categories of patients (emergency, elective, and current ICU patients). A two-stage model and an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to obtain ICU bed allocation. In the first stage, bed allocation is examined under uncertainties regarding the number of emergency patients and their length of stay (LOS). In the second stage, in addition to including the emergency patients with uncertainties in the first stage, it also considers uncertainty in the LOS of elective and current ICU patients. The two-stage model aims to minimize the number of required ICU beds and maximize resource utilization while ensuring the admission of the maximum number of patients. To evaluate the effectiveness of the model and algorithm, the improved NSGA-II was compared with two other methods: multi-objective simulated annealing (MOSA) and multi-objective Tabu search (MOTS). Drawing on data from real cases at a hospital in Lyon, France, the NSGA-II, while catering to patient requirements, saves 9.8% and 5.1% of ICU beds compared to MOSA and MOTS. In five different scenarios, comparing these two algorithms, NSGA-II achieved average improvements of 0%, 49%, 11.4%, 9.5%, and 17.1% across the five objectives.

Management of cancer cachexia towards optimizing care delivery and patient outcomes

Cancer cachexia is a complex syndrome characterized by progressive weight loss, muscle mass depletion, and systemic inflammation, profoundly affecting the well-being and treatment outcomes of cancer patients. Effective management of cancer cachexia demands a coordinated, multifaceted approach involving various healthcare disciplines and operational strategies. Streamlining care processes is crucial to ensure timely interventions and support, reducing delays in diagnosis and treatment initiation. Multidisciplinary collaboration is pivotal in creating integrated care plans that address the multifactorial nature of cancer cachexia comprehensively. Data-driven decision-making empowers healthcare teams to identify trends, monitor treatment responses, and tailor care plans to individual needs, ultimately leading to improved patient outcomes. Standardized assessment and monitoring play a vital role in maintaining consistent, high-quality care, facilitating early interventions and treatment adjustments. Implementing patient-centered care fosters trust, enhances treatment adherence, and encourages patients to actively engage in their care journey, thereby improving their overall quality of life. This paper underscores the significance of applying operations management principles to optimize care delivery and enhance patient outcomes in the management of cancer cachexia. It provides valuable insights for healthcare institutions and professionals striving to provide comprehensive and effective care for individuals affected by this challenging condition.

A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis

COVID-19 pandemic has sent millions of people to hospitals worldwide, exhausting on many occasions the capacity of healthcare systems to provide care patients required to survive. Although several epidemiological research works have contributed a variety of models and approaches to anticipate the pandemic spread, very few have tried to translate the output of these models into hospital service requirements, particularly in terms of bed occupancy, a key question for hospital managers. This paper proposes a tool for predicting the current and future occupancy associated with COVID-19 patients of a hospital to help managers make informed decisions to maximize the availability of hospitalization and intensive care unit (ICU) beds and ensure adequate access to services for confirmed COVID-19 patients. The proposed tool uses a discrete event simulation approach that uses archetypes (i.e., empirical models of trajectories) extracted from empirical analysis of actual patient trajectories. Archetypes can be fitted to trajectories observed in different regions or to the particularities of current and forthcoming variants using a rather small amount of data. Numerical experiments on realistic instances demonstrate the accuracy of the tool's predictions and illustrate how it can support managers in their daily decisions concerning the system's capacity and ensure patients the access the resources they require.

Supply chain planning of vaccine and pharmaceutical clusters under uncertainty: The case of COVID-19

As an abrupt epidemic occurs, healthcare systems are shocked by the surge in the number of susceptible patients' demands, and decision-makers mostly rely on their frame of reference for urgent decision-making. Many reports have declared the COVID-19 impediments to trading and global economic growth. This study aims to provide a mathematical model to support pharmaceutical supply chain planning during the COVID-19 epidemic. Additionally, it aims to offer new insights into hospital supply chain problems by unifying cold and non-cold chains and considering a wide range of pharmaceuticals and vaccines. This approach is unprecedented and includes an analysis of various pharmaceutical features such as temperature, shelf life, priority, and clustering. To propose a model for planning the pharmaceutical supply chains, a mixed-integer linear programming (MILP) model is used for a four-echelon supply chain design. This model aims to minimize the costs involved in the pharmaceutical supply chain by maintaining an acceptable service level. Also, this paper considers uncertainty as an intrinsic part of the problem and addresses it through the wait-and-see method. Furthermore, an unexplored unsupervised learning method in the realm of supply chain planning has been used to cluster the pharmaceuticals and the vaccines and its merits and drawbacks are proposed. A case of Tehran hospitals with real data has been used to show the model's capabilities, as well. Based on the obtained results, the proposed approach is able to reach the optimum service level in the COVID conditions while maintaining a reduced cost. The experiment illustrates that the hospitals' adjacency and emergency orders alleviated the service level significantly. The proposed MILP model has proven to be efficient in providing a practical intuition for decision-makers. The clustering technique reduced the size of the problem and the time required to solve the model considerably.

Data quality assessment in emergency medical services: an objective approach

BACKGROUND

In emergency medical services, high quality data are of great importance for patient care. Due to the unique nature of this type of services, the purpose of this study was to assess data quality in emergency medical services using an objective approach.

METHODS

This was a retrospective quantitative study conducted in 2019. The research sample included the emergency medical records of patients who referred to three emergency departments by the pre-hospital emergency care services (n = 384). Initially a checklist was designed based on the data elements of the triage form, pre-hospital emergency care form, and emergency medical records. Then, data completeness, accuracy and timeliness were assessed.

RESULTS

Data completeness in the triage form, pre-hospital emergency care form, and emergency medical records was 52.3%, 70% and 57.3%, respectively. Regarding data accuracy, most of the data elements were consistent. Measuring data timeliness showed that in some cases, paper-based ordering and computer-based data entry was not sequential.

CONCLUSION

Data quality in emergency medical services was not satisfactory and there were some weaknesses in the documentation processes. The results of this study can inform the clinical and administrative staff to pay more attentions to these weaknesses and plan for data quality improvement.

Machine learning based integrated scheduling and rescheduling for elective and emergency patients in the operating theatre

As the only largest source of revenue and cost in a hospital, the operation room (OR) scheduling problem is a hot research topic. Nonetheless, an integrated model is the missing key to managing and improving the efficiency of ORs. This paper presents a fully integrated model regarding three concepts: meditating elective patients and emergency patients together, considering ORs and downstream units, and proposing hierarchical weekly, daily, and rescheduling models. Due to the inherent randomness in emergency patient arrival, a random forest machine learning model and geographical information systems are used to obtain the emergency patient surgery duration and arrival time, respectively. According to the machine learning model in weekly and daily scheduling, initially, fixed capacity is reserved for emergency patients. When an emergency patient arrives, the surgery starts if a reserved OR is available. Otherwise, the first available OR will be dedicated to the patient due to an emergency patient's higher priority than an elective patient. In this case, it is needed to reschedule the OT schedule for the remaining patient. Moreover, the three-phase model guarantees that an emergency patient assigns to an OR within a specific time limit. To solve the models, genetic algorithm and particle swarm optimization are developed and compared. In addition, a real-world case study is undertaken at a hospital. The results of comparing the proposed approach to the hospital's current scheduling show that the three-phase model had a considerable positive effect on the ORs schedule.

Introduction to the special issue on the role of operational research in future epidemics/ pandemics

In this special issue, 23 research papers are published focusing on COVID-19 and operational research solution techniques. First, we detail the process from advertising the call for papers to the point where the best papers are accepted. Then, we provide a summary of each paper focusing on applications, solution techniques and insights for practitioners and policy makers. To provide a holistic view for readers, we have clustered the papers into different groups: transmission, propagation and forecasting, non-pharmaceutical intervention, healthcare network configuration, healthcare resource allocation, hospital operations, vaccine and testing kits, and production and manufacturing. Then, we introduce other possible subjects that can be considered for future research.

Mapping research on healthcare operations and supply chain management: a topic modelling-based literature review

The literature on healthcare operations and supply chain management has seen unprecedented growth over the past two decades. This paper seeks to advance the body of knowledge on this topic by utilising a topic modelling-based literature review to identify the core topics, examine their dynamic changes, and identify opportunities for further research in the area. Based on an analysis of 571 articles published until 25 January 2022, we identify numerous popular topics of research in the area, including patient waiting time, COVID-19 pandemic, Industry 4.0 technologies, sustainability, risk and resilience, climate change, circular economy, humanitarian logistics, behavioural operations, service-ecosystem, and knowledge management. We reviewed current literature around each topic and offered insights into what aspects of each topic have been studied and what are the recent developments and opportunities for more impactful future research. Doing so, this review help advance the contemporary scholarship on healthcare operations and supply chain management and offers resonant insights for researchers, research students, journal editors, and policymakers in the field.