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Abstract Objectives Patients are classified according to the severity of their condition and graded according to the diagnosis and treatment capacity of medical institutions. This study aims to correctly assign patients to medical institutions for treatment and develop patient allocation and medical resource expansion schemes among hospitals in the medical network. Methods Illness severity, hospital level, allocation matching benefit, distance traveled, and emergency medical resource fairness were considered. A multi-objective planning method was used to construct a patient allocation model during major epidemics. A simulation study was carried out in two scenarios to test the proposed method. Results (1) The single-objective model obtains an unbalanced solution in contrast to the multi-objective model. The proposed model considers multi-objective problems and balances the degree of patient allocation matching, distance traveled, and fairness. (2) The non-hierarchical model has crowded resources, and the hierarchical model assigns patients to matched medical institutions. (3) In the “demand exceeds supply” situation, the patient allocation model identified additional resources needed by each hospital. Conclusion Results verify the maneuverability and effectiveness of the proposed model. It can generate schemes for specific patient allocation and medical resource amplification and can serve as a quantitative decision-making tool in the context of major epidemics.

The goal of a phase I dose-finding trial is to determine the dose level of a new drug with acceptable toxicity. The optimal dose level is determined by sequentially allocating patients to increasing dose levels while monitoring any safety concerns. In practice, multiple toxicity types may be of interest and with varying degrees of importance of each toxicity type. To address this, scoring systems have been developed and conventional adaptive designs, such as the continual reassessment method (CRM), have accordingly been modified to handle them. In this article, we consider how to model the dose-finding problem under the multiarmed bandit framework, which naturally embeds the tradeoff between exploring the toxicity of dose levels and exploiting the current information to optimize benefit. We then propose a Bayesian multiarmed bandit design, dubbed quasi-likelihood optimistic bandit (QLOB), which has desirable operating characteristics, including allocation of patients to the dose level which has an estimated toxicity score closest to the target level and is relatively less explored. In extensive simulation studies, it is demonstrated that QLOB outperformed toxicity-score-based designs, such as quasi-CRM (QCRM), and general Bayesian optimal interval (gBOIN) in most scenarios considered; and performed much better than the conventional CRM and "3 + 3" designs with respect to dose recommendation and patient allocation. In addition, our design is shown to be robust against misspecification of the relevant hyper-parameter, and to have improved performance as the number of enrolled patients increases. [ABSTRACT FROM AUTHOR]

Objectives: Patients are classified according to the severity of their condition and graded according to the diagnosis and treatment capacity of medical institutions. This study aims to correctly assign patients to medical institutions for treatment and develop patient allocation and medical resource expansion schemes among hospitals in the medical network. Methods: Illness severity, hospital level, allocation matching benefit, distance traveled, and emergency medical resource fairness were considered. A multi-objective planning method was used to construct a patient allocation model during major epidemics. A simulation study was carried out in two scenarios to test the proposed method. Results: (1) The single-objective model obtains an unbalanced solution in contrast to the multi-objective model. The proposed model considers multi-objective problems and balances the degree of patient allocation matching, distance traveled, and fairness. (2) The non-hierarchical model has crowded resources, and the hierarchical model assigns patients to matched medical institutions. (3) In the "demand exceeds supply" situation, the patient allocation model identified additional resources needed by each hospital. Conclusion: Results verify the maneuverability and effectiveness of the proposed model. It can generate schemes for specific patient allocation and medical resource amplification and can serve as a quantitative decision-making tool in the context of major epidemics. [ABSTRACT FROM AUTHOR]

Umbrella trials are an innovative trial design where different treatments are matched with subtypes of a disease, with the matching typically based on a set of biomarkers. Consequently, when patients can be positive for more than one biomarker, they may be eligible for multiple treatment arms. In practice, different approaches could be applied to allocate patients who are positive for multiple biomarkers to treatments. However, to date there has been little exploration of how these approaches compare statistically. We conduct a simulation study to compare five approaches to handling treatment allocation in the presence of multiple biomarkers – equal randomisation; randomisation with fixed probability of allocation to control; Bayesian adaptive randomisation (BAR); constrained randomisation; and hierarchy of biomarkers. We evaluate these approaches under different scenarios in the context of a hypothetical phase II biomarker‐guided umbrella trial. We define the pairings representing the pre‐trial expectations on efficacy as linked pairs, and the other biomarker‐treatment pairings as unlinked. The hierarchy and BAR approaches have the highest power to detect a treatment‐biomarker linked interaction. However, the hierarchy procedure performs poorly if the pre‐specified treatment‐biomarker pairings are incorrect. The BAR method allocates a higher proportion of patients who are positive for multiple biomarkers to promising treatments when an unlinked interaction is present. In most scenarios, the constrained randomisation approach best balances allocation to all treatment arms. Pre‐specification of an approach to deal with treatment allocation in the presence of multiple biomarkers is important, especially when overlapping subgroups are likely. [ABSTRACT FROM AUTHOR]

Background & Aims: In France, liver grafts that have been refused at least 5 times can be “rescued” and allocated to a centre which chooses a recipient from its own waiting list, outside the patient-based allocation framework. We explored whether these “rescued” grafts were associated with worse graft/patient survival, as well as assessing their effect on survival benefit. Methods: Among 7,895 candidates, 5,218 were transplanted between 2009 and 2014 (336 centre-allocated). We compared recipient/graft survival between patient allocation and centre allocation, considering a selection bias and the distribution of centre-allocation recipients among the transplant teams. We used a propensity score approach and a weighted Cox model using the inverse probability of treatment weighting method. We also explored the survival benefit associated with centre-allocation grafts. Results: There was a significantly higher risk of graft loss/death in the centre allocation group compared to the patient allocation group (hazard ratio 1.13; 95% CI 1.05–1.22). However, this difference was no longer significant for teams that performed more than 7% of the centre-allocation transplantations. Moreover, receiving a centre-allocation graft, compared to remaining on the waiting list and possibly later receiving a patient-allocation graft, did not convey a poorer survival benefit (hazard ratio 0.80; 95% CI 0.60–1.08). Conclusions: In centres which transplanted most of the centre-allocation grafts, using grafts repeatedly refused for top-listed candidates was not detrimental. Given the organ shortage, our findings should encourage policy makers to restrict centre-allocation grafts to targeted centres. Lay summary: “Centre allocation” (CA) made it possible to save 6 out of 100 available liver grafts that had been refused at least 5 times for use in the top-listed candidates on the national waiting list. In this series, the largest on this topic, we showed that, in centres which transplanted most of the CA grafts, using grafts repeatedly refused for top-listed candidates did not appear to be detrimental. In the context of organ shortage, our results, which could be of interest for any country using this CA strategy, should encourage policy makers to reassess some aspects of graft allocation by restricting CA grafts to targeted centres, fostering the “best” matching between grafts and candidates on the waiting list.

Dose selection is one of the most difficult and crucial decisions to make during drug development. As a consequence, the dose-finding trial is a major milestone in the drug development plan and should be properly designed. This article will review the most recent methodologies for optimizing the design of dose-finding studies: all of them are based on the modeling of the dose–response curve, which is now the gold standard approach for analyzing dose-finding studies instead of the traditional ANOVA/multiple testing approach. We will address the optimization of both fixed and adaptive designs and briefly outline new methodologies currently under investigation, based on utility functions. [ABSTRACT FROM AUTHOR]

Adaptive designs for multi-armed clinical trials have become increasingly popular recently because of their potential to shorten development times and to increase patient response. However, developing response-adaptive designs that offer patient-benefit while ensuring the resulting trial provides a statistically rigorous and unbiased comparison of the different treatments included is highly challenging. In this paper, the theory of Multi-Armed Bandit Problems is used to define near optimal adaptive designs in the context of a clinical trial with a normally distributed endpoint with known variance. We report the operating characteristics (type I error, power, bias) and patient-benefit of these approaches and alternative designs using simulation studies based on an ongoing trial. These results are then compared to those recently published in the context of Bernoulli endpoints. Many limitations and advantages are similar in both cases but there are also important differences, specially with respect to type I error control. This paper proposes a simulation-based testing procedure to correct for the observed type I error inflation that bandit-based and adaptive rules can induce. [ABSTRACT FROM AUTHOR]

Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In that context, patient allocation to treatments is determined by randomization probabilities that change based on the accrued response data in order to achieve experimental goals. RAR has received abundant theoretical attention from the biostatistical literature since the 1930's and has been the subject of numerous debates. In the last decade, it has received renewed consideration from the applied and methodological communities, driven by well-known practical examples and its widespread use in machine learning. Papers on the subject present different views on its usefulness, and these are not easy to reconcile. This work aims to address this gap by providing a unified, broad and fresh review of methodological and practical issues to consider when debating the use of RAR in clinical trials.

This paper introduces mathematical models that support dynamic fair balancing of COVID-19 patients over hospitals in a region and across regions. Patient flow is captured in an infinite server queueing network. The dynamic fair balancing model within a region is a load balancing model incorporating a forecast of the bed occupancy, while across regions, it is a stochastic program taking into account scenarios of the future bed surpluses or shortages. Our dynamic fair balancing models yield decision rules for patient allocation to hospitals within the region and reallocation across regions based on safety levels and forecast bed surplus or bed shortage for each hospital or region. Input for the model is an accurate real-time forecast of the number of COVID-19 patients hospitalised in the ward and the Intensive Care Unit (ICU) of the hospitals based on the predicted inflow of patients, their Length of Stay and patient transfer probabilities among ward and ICU. The required data is obtained from the hospitals' data warehouses and regional infection data as recorded in the Netherlands. The algorithm is evaluated in Dutch regions for allocation of COVID-19 patients to hospitals within the region and reallocation across regions using data from the second COVID-19 peak., (© 2022 The Authors.)

Multi-armed bandit problems (MABPs) are a special type of optimal control problem well suited to model resource allocation under uncertainty in a wide variety of contexts. Since the first publication of the optimal solution of the classic MABP by a dynamic index rule, the bandit literature quickly diversified and emerged as an active research topic. Across this literature, the use of bandit models to optimally design clinical trials became a typical motivating application, yet little of the resulting theory has ever been used in the actual design and analysis of clinical trials. To this end, we review two MABP decision-theoretic approaches to the optimal allocation of treatments in a clinical trial: the infinite-horizon Bayesian Bernoulli MABP and the finite-horizon variant. These models possess distinct theoretical properties and lead to separate allocation rules in a clinical trial design context. We evaluate their performance compared to other allocation rules, including fixed randomization. Our results indicate that bandit approaches offer significant advantages, in terms of assigning more patients to better treatments, and severe limitations, in terms of their resulting statistical power. We propose a novel bandit-based patient allocation rule that overcomes the issue of low power, thus removing a potential barrier for their use in practice. [ABSTRACT FROM AUTHOR]

The current infectious disease outbreak, a novel acute respiratory syndrome [SARS]-CoV-2, is one of the greatest public health concerns that the humanity has been struggling since the end of 2019. Although, dedicating the majority of hospital-based resources is an effective method to deal with the upsurge in the number of infected individuals, its drastic impact on routine healthcare services cannot be underestimated. In this study, the proposed multi-objective, multi-period linear programming model optimizes the distribution decision of infected patients and the evacuation rate of non-infected patients simultaneously. Moreover, the presented model determines the number of new COVID-19 intensive care units, which are established by using existing hospital-based resources. Three objectives are considered: (1) minimization of total distance travelled by infected patients, (2) minimization of the maximum evacuation rate of non-infected patients and (3) minimization of the infectious risk of healthcare professionals. A case study is performed for the European side of Istanbul, Turkey. The effect of the uncertain length of the stay of infected patients is demonstrated via sensitivity analyses., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Copyright of Der Urologe A is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

fernandez r., tran d.t., johnson m. & jones s. (2010) Journal of Nursing Management 18, 265–274 Interdisciplinary communication in general medical and surgical wards using two different models of nursing care delivery Aim To compare two models of care on nurses’ perception of interdisciplinary communication in general medical and surgical wards. Background Effective interdisciplinary collaboration remains the cornerstone of efficient and successful functioning of health care teams and contributes substantially to patient safety. Methods  In May 2007, participants were recruited from a tertiary teaching hospital in Australia. The multifaceted Shared Care in Nursing (SCN) model of nursing care involved team work, leadership and professional development. In the Patient Allocation (PA) model one nurse was responsible for the care of a discrete group of patients. Differences in interdisciplinary communication were assessed at the 6-month follow-up. Results Completed questionnaires were returned by 125 participants. At the 6-month follow-up, there was a significant reduction in scores in the SCN group in the subscales relating to communication openness ( P = 0.03) and communication accuracy ( P = 0.02) when compared with baseline values. There were no significant differences in the two groups at the 6-month follow-up in any of the other subscales. Conclusions There is a need for effective training programmes to assist nurses in working together within a nursing team and an interdisciplinary ward team. The SCN and the PA models of care have been found by nurses to support most aspects of interdisciplinary and intradisciplinary communication. The applicability of both models of care to wards with a varying skill mix of nurses is suggested. Further studies of larger samples with varying compositions of skill mix and varying models of care are required. Implications for nursing management Nurse managers can use varying models of care to support interdisciplinary communication and enhance patient safety. [ABSTRACT FROM AUTHOR]

Copyright of Der Unfallchirurg is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Dengue fever is a mosquito-borne disease that has rapidly spread throughout the last few decades. Most preventive mechanisms to deal with the disease focus on the eradication of the vector mosquito and vaccination campaigns. However, appropriate mechanisms of response are indispensable to face the consequent events when an outbreak takes place. This study applied single and multiple objective linear programming models to optimize the allocation of patients and additional resources during an epidemic dengue fever outbreak, minimizing the summation of the distance travelled by all patients. An empirical study was set in Ciudad del Este, Paraguay. Data provided by a privately run health insurance cooperative was used to verify the applicability of the models in this study. The results can be used by analysts and decision makers to solve patient allocation problems for providing essential medical care during an epidemic dengue fever outbreak. [ABSTRACT FROM AUTHOR]

Assuming control over the allocation of patients to treatment conditions is a fundamental element of any comparative clinical trial. There are three critical considerations investigators must balance in choosing an allocation scheme: reducing bias in patient allocation, producing balanced patient groups across treatment arms, and reducing the likelihood of errors attributable to chance variation. The authors review the principles of three key approaches to the allocation of patients to conditions within clinical trials, and their respective advantages with regard to these critical considerations. These allocation methods include randomization, stratification, and patient-treatment matching. Randomization is fundamental to most clinical trials. Stratification is an advanced step in a systematic program of research investigating the efficacy and effectiveness of an intervention. If the trial has less than 100 per arm and there is a known prognostic factor, stratification is the best choice to ensure equal allocation across groups. Treatment matching (tailoring) attempts to match the most appropriate treatment to a specific patient based on a priori hypotheses. Two techniques used for exploring treatment matching are: patient typologies (patient profiling), and aptitude-treatment interactions. Additional details pertaining to the rationale for selecting among these various approaches to patient allocation is provided, and their methodology is summarized with specific consideration for their application within clinical trials of headache treatment.(Headache2005;45:419-428) [ABSTRACT FROM AUTHOR]

Background: During the COVID-19 pandemic, Central COVID-19 Coordination Centers (CCCCs) have been established at several hospitals across Germany with the intention to assist local health care professionals in efficiently referring patients with suspected or confirmed SARS-CoV-2 infection to regional hospitals and therefore to prevent the collapse of local health system structures. In addition, these centers coordinate interhospital transfers of patients with COVID-19 and provide or arrange specialized telemedical consultations., Objective: This study describes the establishment and management of a CCCC at a German university hospital., Methods: We performed economic analyses (cost, cost-effectiveness, use, and utility) according to the CHEERS (Consolidated Health Economic Evaluation Reporting Standards) criteria. Additionally, we conducted a systematic review to identify publications on similar institutions worldwide. The 2 months with the highest local incidence of COVID-19 cases (December 2020 and January 2021) were considered., Results: During this time, 17.3 requests per day were made to the CCCC regarding admission or transfer of patients with COVID-19. The majority of requests were made by emergency medical services (601/1068, 56.3%), patients with an average age of 71.8 (SD 17.2) years were involved, and for 737 of 1068 cases (69%), SARS-CoV-2 had already been detected by a positive polymerase chain reaction test. In 59.8% (639/1068) of the concerned patients, further treatment by a general practitioner or outpatient presentation in a hospital could be initiated after appropriate advice, 27.2% (291/1068) of patients were admitted to normal wards, and 12.9% (138/1068) were directly transmitted to an intensive care unit. The operating costs of the CCCC amounted to more than €52,000 (US $60,031) per month. Of the 334 patients with detected SARS-CoV-2 who were referred via EMS or outpatient physicians, 302 (90.4%) were triaged and announced in advance by the CCCC. No other published economic analysis of COVID-19 coordination or management institutions at hospitals could be found., Conclusions: Despite the high cost of the CCCC, we were able to show that it is a beneficial concept to both the providing hospital and the public health system. However, the most important benefits of the CCCC are that it prevents hospitals from being overrun by patients and that it avoids situations in which physicians must weigh one patient's life against another's., (©Nikolas Schopow, Georg Osterhoff, Nikolaus von Dercks, Felix Girrbach, Christoph Josten, Sebastian Stehr, Pierre Hepp. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 18.11.2021.)

With the COVID-19 pandemic, emergency rooms are faced with major challenges because they act as the interface between outpatient and inpatient care. The dynamics of the pandemic forced emergency care at the University Hospital Münster to extensively adjust their processes, which had to be carried out in the shortest time possible. This included the establishment of an outpatient coronavirus test center and a medical student-operated telephone hotline. Inside the hospital, new isolation capacities in the emergency room and a dedicated COVID-19 ward were set up. The patient flow was reorganized using flow diagrams for both the outpatient and inpatient areas. The general and special emergency management was optimized for the efficient treatment of COVID-19-positive patients and the staff were trained in the use of protective equipment. This report of our experience is intended to support other emergency departments in their preparation for the COVID-19 pandemic.

Background & Aims: In France, liver grafts that have been refused at least 5 times can be "rescued" and allocated to a centre which chooses a recipient from its own waiting list, outside the patient-based allocation framework. We explored whether these "rescued" grafts were associated with worse graft/patient survival, as well as assessing their effect on survival benefit., Methods: Among 7,895 candidates, 5,218 were transplanted between 2009 and 2014 (336 centre-allocated). We compared recipient/graft survival between patient allocation and centre allocation, considering a selection bias and the distribution of centre-allocation recipients among the transplant teams. We used a propensity score approach and a weighted Cox model using the inverse probability of treatment weighting method. We also explored the survival benefit associated with centre-allocation grafts., Results: There was a significantly higher risk of graft loss/death in the centre allocation group compared to the patient allocation group (hazard ratio 1.13; 95% CI 1.05-1.22). However, this difference was no longer significant for teams that performed more than 7% of the centre-allocation transplantations. Moreover, receiving a centre-allocation graft, compared to remaining on the waiting list and possibly later receiving a patient-allocation graft, did not convey a poorer survival benefit (hazard ratio 0.80; 95% CI 0.60-1.08)., Conclusions: In centres which transplanted most of the centre-allocation grafts, using grafts repeatedly refused for top-listed candidates was not detrimental. Given the organ shortage, our findings should encourage policy makers to restrict centre-allocation grafts to targeted centres., Lay Summary: "Centre allocation" (CA) made it possible to save 6 out of 100 available liver grafts that had been refused at least 5 times for use in the top-listed candidates on the national waiting list. In this series, the largest on this topic, we showed that, in centres which transplanted most of the CA grafts, using grafts repeatedly refused for top-listed candidates did not appear to be detrimental. In the context of organ shortage, our results, which could be of interest for any country using this CA strategy, should encourage policy makers to reassess some aspects of graft allocation by restricting CA grafts to targeted centres, fostering the "best" matching between grafts and candidates on the waiting list., Competing Interests: Please refer to the accompanying ICMJE disclosure forms for further details., (© 2020 The Author(s).)