Evaluation of score-based tertiary triage policies during the COVID-19 pandemic: simulation study with real-world intensive care data.

Objective: The explicit prohibition of discontinuing intensive care unit (ICU) treatment that has already begun by the newly established German Triage Act in favor of new patients with better prognoses (tertiary triage) under crisis conditions may prevent saving as many patients as possible and therefore may violate the international well-accepted premise of undertaking the "best for the most" patients. During the COVID-19 pandemic, authorities set up lockdown measures and infection-prevention strategies to avoid an overburdened health-care system. In cases of situational overload of ICU resources, when transporting options are exhausted, the question of a tertiary triage of patients arises.

The AI ethics of digital COVID-19 diagnosis and their legal, medical, technological, and operational managerial implications.

The COVID-19 pandemic has given rise to a broad range of research from fields alongside and beyond the core concerns of infectiology, epidemiology, and immunology. One significant subset of this work centers on machine learning-based approaches to supporting medical decision-making around COVID-19 diagnosis. To date, various challenges, including IT issues, have meant that, notwithstanding this strand of research on digital diagnosis of COVID-19, the actual use of these methods in medical facilities remains incipient at best, despite their potential to relieve pressure on scarce medical resources, prevent instances of infection, and help manage the difficulties and unpredictabilities surrounding the emergence of new mutations.

Simulation of the mortality after different ex ante (secondary) and ex post (tertiary) triage methods in people with disabilities and pre-existing diseases.

The significant increase in patients during the COVID-19 pandemic presented the healthcare system with a variety of challenges. The intensive care unit is one of the areas particularly affected in this context. Only through extensive infection control measures as well as an enormous logistical effort was it possible to treat all patients requiring intensive care in Germany even during peak phases of the pandemic, and to prevent triage even in regions with high patient pressure and simultaneously low capacities.

Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways.

The Covid-19 pandemic has pushed many hospitals to their capacity limits. Therefore, a triage of patients has been discussed controversially primarily through an ethical perspective. The term triage contains many aspects such as urgency of treatment, severity of the disease and pre-existing conditions, access to critical care, or the classification of patients regarding subsequent clinical pathways starting from the emergency department.

[Simulation of mortality after different ex-ante and ex-post-triage methods in people with disabilities and comorbidities].

The significant increase in patients during the COVID-19 pandemic presented the healthcare system with a variety of challenges. The intensive care unit is one of the areas particularly affected in this context. Only through extensive infection control measures as well as an enormous logistical effort was it possible to treat all patients requiring intensive care in Germany even during peak phases of the pandemic, and to prevent triage even in regions with high patient pressure and simultaneously low capacities.

Airborne pollen grain detection from partially labelled data utilising semi-supervised learning.

Airborne pollen monitoring has been conducted for more than a century now, as knowledge of the quantity and periodicity of airborne pollen has diverse use cases, like reconstructing historic climates and tracking current climate change, forensic applications, and up to warning those affected by pollen-induced respiratory allergies. Hence, related work on automation of pollen classification already exists. In contrast, detection of pollen is still conducted manually, and it is the gold standard for accuracy.

A Simulation-Based Cost-Effectiveness Analysis of Severe Acute Respiratory Syndrome Coronavirus 2 Infection Prevention Strategies for Visitors of Healthcare Institutions.

Objectives: The aim is to quantitatively evaluate different infection prevention strategies in the context of hospital visitor management during pandemics and to provide a decision support system for strategic and operational decisions based on this evaluation.

Methods: A simulation-based cost-effectiveness analysis is applied to the data of a university hospital in Southern Germany and published COVID-19 research. The performance of different hospital visitor management strategies is evaluated by several decision-theoretic methods with varying objective functions.

Detecting Airborne Pollen Using an Automatic, Real-Time Monitoring System: Evidence from Two Sites.

Airborne pollen monitoring has been an arduous task, making ecological applications and allergy management virtually disconnected from everyday practice. Over the last decade, intensive research has been conducted worldwide to automate this task and to obtain real-time measurements. The aim of this study was to evaluate such an automated biomonitoring system vs.

Homogeneity and Best Practice Analyses in Hospital Performance Management: An Analytical Framework.

Performance modeling of hospitals using data envelopment analysis (DEA) has received steadily increasing attention in the literature. As part of the traditional DEA framework, hospitals are generally assumed to be functionally similar and therefore homogenous. Accordingly, any identified inefficiency is supposedly due to the inefficient use of inputs to produce outputs.

Balancing control and autonomy in master surgery scheduling: Benefits of ICU quotas for recovery units.

When scheduling surgeries in the operating theater, not only the resources within the operating theater have to be considered but also those in downstream units, e.g., the intensive care unit and regular bed wards of each medical specialty.

Towards automatic airborne pollen monitoring: From commercial devices to operational by mitigating class-imbalance in a deep learning approach.

Allergic diseases have been the epidemic of the century among chronic diseases. Particularly for pollen allergies, and in the context of climate change, as airborne pollen seasons have been shifting earlier and abundances have been becoming higher, pollen monitoring plays an important role in generating high-risk allergy alerts. However, this task requires labour-intensive and time-consuming manual classification via optical microscopy.

Managing admission and discharge processes in intensive care units.

The intensive care unit (ICU) is one of the most crucial and expensive resources in a health care system. While high fixed costs usually lead to tight capacities, shortages have severe consequences. Thus, various challenging issues exist: When should an ICU admit or reject arriving patients in general? Should ICUs always be able to admit critical patients or rather focus on high utilization? On an operational level, both admission control of arriving patients and demand-driven early discharge of currently residing patients are decision variables and should be considered simultaneously.

Simulation-based evaluation of operating room management policies.

Background: Since operating rooms are a major bottleneck resource and an important revenue driver in hospitals, it is important to use these resources efficiently. Studies estimate that between 60 and 70% of hospital admissions are due to surgeries. Furthermore, staffing cannot be changed daily to respond to changing demands.

Determinants of the utilization of allergy management measures among hay fever sufferers: a theory-based cross-sectional study.

Background: The quality of life of chronically ill individuals, such as hay fever sufferers, is significantly dependent on their health behavior. This survey aimed to explain the health-related behavior of allergic individuals using the protection motivation theory (PMT) and the transtheoretical model (TTM).

Methods: The influencing variables stated by PMT were operationalized based on data from semistructured pilot interviews and a pretest with 12 individuals from the target population.

Defining biomarkers to predict symptoms in subjects with and without allergy under natural pollen exposure.

Background: Pollen exposure induces local and systemic allergic immune responses in sensitized individuals, but nonsensitized individuals also are exposed to pollen. The kinetics of symptom expression under natural pollen exposure have never been systematically studied, especially in subjects without allergy.

Objective: We monitored the humoral immune response under natural pollen exposure to potentially uncover nasal biomarkers for in-season symptom severity and identify protective factors.

Automated Classification of Airborne Pollen using Neural Networks.

Pollen allergies are considered as a global epidemic nowadays, as they influence more than a quarter of the worldwide population, with this percentage expected to rapidly increase because of ongoing climate change. To date, alerts on high-risk allergenic pollen exposure have been provided only via forecasting models and conventional monitoring methods that are laborious. The aim of this study is to develop and evaluate our own pollen classification model based on deep neural networks.

Analyzing economies of scale and scope in hospitals by use of case mix planning.

This study analyzes the effect of economies of scale and scope on the optimal case mix of a hospital or hospital system. With respect to the ideal volume and patient composition, the goal is to evaluate (i) the impact of changes in the efficiency of resource use with increasing scale, and (ii) to determine the potential effects of spreading fixed costs over a greater number of patients. The problem is formulated as a non-linear mixed integer program.

Hospital physicians can't get no long-term satisfaction - an indicator for fairness in preference fulfillment on duty schedules.

Physicians are a scarce resource in hospitals. In order to minimize physician attrition, schedulers incorporate individual physician preferences when creating the physicians' duty roster. The manual creation of a roster is very time-consuming and often produces suboptimal results.

Correction to: The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals.

The original version of this article unfortunately contained errors. The first column of Tables 5 and 6 in the Appendix section should contain the year of publication instead of the reference number in brackets. The reference citations were then placed in the second column.

The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals.

The healthcare sector in general and hospitals in particular represent a main application area for Data Envelopment Analysis (DEA). This paper reviews 262 papers of DEA applications in healthcare with special focus on hospitals and therefore closes a gap of over ten years that were not covered by existing review articles. Apart from providing descriptive statistics of the papers, we are the first to examine the research purposes of the publications.

Operations research in intensive care unit management: a literature review.

The intensive care unit (ICU) is a crucial and expensive resource largely affected by uncertainty and variability. Insufficient ICU capacity causes many negative effects not only in the ICU itself, but also in other connected departments along the patient care path. Operations research/management science (OR/MS) plays an important role in identifying ways to manage ICU capacities efficiently and in ensuring desired levels of service quality.

Case mix planning in hospitals: a review and future agenda.

The case mix planning problem deals with choosing the ideal composition and volume of patients in a hospital. With many countries having recently changed to systems where hospitals are reimbursed for patients according to their diagnosis, case mix planning has become an important tool in strategic and tactical hospital planning. Selecting patients in such a payment system can have a significant impact on a hospital's revenue.

Long term staff scheduling of physicians with different experience levels in hospitals using column generation.

We present a strategic model to solve the long-term staffing problem of physicians in hospitals using flexible shifts. The objective is to minimize the total number of staff subject to several labor agreements. A wide range of legal restrictions and facility-specific staffing policies are considered.

Flexible shift scheduling of physicians.

This research addresses a shift scheduling problem in which physicians at a German university hospital are assigned to demand periods over a planning horizon that can extend up to several weeks. When performing the scheduling it is necessary to take into account a variety of legal and institutional constraints that are imposed by a national labor agreement, which governs all physicians in German university hospitals. Currently, most medical departments develop their staff schedules manually at great cost and time.