Problem definition
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Inpatient beds are usually grouped into several wards, and each ward is assigned to serve patients from certain “primary” specialties. However, when a patient waits excessively ...long before a primary bed becomes available, hospital managers have the option to assign her to a nonprimary bed. although it is undesirable. Deciding when to use such “overflow” is difficult in real time and under uncertainty.
Relevance
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To aid the decision making, we model hospital inpatient flow as a multiclass, multipool parallel-server queueing system and formulate the overflow decision problem as a discrete-time, infinite-horizon average cost Markov decision process (MDP). The MDP incorporates many realistic and important features such as patient arrival and discharge patterns depending on time of day.
Methodology
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To overcome the curse-of-dimensionality of this formulated MDP, we resort to approximate dynamic programming (ADP). A critical part in designing an ADP algorithm is to choose appropriate basis functions to approximate the relative value function. Using a novel combination of fluid control and single-pool approximation, we develop analytical forms to approximate the relative value functions at midnight, which then guides the choice of the basis functions for all other times of day.
Results
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We demonstrate, via numerical experiments in realistic hospital settings, that our proposed ADP algorithm is remarkably effective in finding good overflow policies. These ADP policies can significantly improve system performance over some commonly used overflow strategies—for example, in a baseline scenario, the ADP policy achieves a congestion level similar to that achieved by a complete bed sharing policy, while reduces the overflow proportion by 20%.
Managerial implications
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We quantify the trade-off between the overflow proportion and congestion from implementing ADP policies under a variety of system conditions and generate useful insights. The plotted efficient frontiers allow managers to observe various performance measures in different parameter regimes, and the ADP policies provide managers with operational strategies to achieve the desired performance.
Service systems often feature multiple classes of customers with different service needs and multiple pools of servers with different skillsets. How to efficiently match customers of different ...classes with servers of different skillsets is of great importance to the management of these systems. In this survey, we review works on skill-based routing in queues. We first summarize key insights on routing/scheduling policies developed in the literature. We then discuss complications brought by modern service operations management problems, particularly healthcare systems. These complications stimulate a growing body of literature on new modeling and analysis tools. Lastly, we provide additional numerical experiments to highlight the complex nature of a routing problem motivated from hospital patient-flow management, and provide some useful intuition to develop good skill-based routing policies in practice. Our goal is to provide a brief overview of the skill-based routing research landscape and to help generate interesting research ideas.
Dexamethasone (Dex), as a pretreatment agent, is widely used to attenuate the side effects of chemotherapy in breast cancer treatment. However, whether and how Dex affects breast cancer metastasis ...remain to be furtherly understood. In this study, we established several mouse breast cancer metastatic models to study the effect of Dex in vitro and in vivo. Transwell, Western Blot and RNA interference were applied to study the molecular mechanism of Dex in promoting breast cancer cell migration. Meanwhile, the effect of Dex on lung metastasis of breast cancer in Dex combined with PTX chemotherapy was discussed. Our results confirmed that Dex could promote breast cancer cell metastasis both in vitro and in vivo. Mechanistic studies revealed that this pro-metastatic effect of Dex was mediated by the GR-PI3K-SGK1-CTGF pathway in tumor cells. Ligation of Dex and glucocorticoid receptor (GR) on tumor cells activated the PI3K signaling pathway and upregulated serum glucocorticoid-inducible kinase 1 (SGK1) expression, and then increased the expression of connective tissue growth factor (CTGF) through Nedd4l-Smad2. Moreover, Dex was the leading factor for lung metastasis in a standard regimen for breast cancer treatment with paclitaxel and Dex. Importantly, targeting SGK1 with the inhibitor GSK650394 remarkably reduced lung metastasis in this regimen. Our present data provide new insights into Dex-induced breast cancer metastasis and indicate that SGK1 could be a candidate target for the treatment of breast cancer metastasis.
Advanced machine learning models have received wide attention in assisting medical decision making due to the greater accuracy they can achieve. However, their limited interpretability imposes ...barriers for practitioners to adopt them. Recent advancements in interpretable machine learning tools allow us to look inside the black box of advanced prediction methods to extract interpretable models while maintaining similar prediction accuracy, but few studies have investigated the specific hospital readmission prediction problem with this spirit.
Our goal is to develop a machine-learning (ML) algorithm that can predict 30- and 90- day hospital readmissions as accurately as black box algorithms while providing medically interpretable insights into readmission risk factors. Leveraging a state-of-art interpretable ML model, we use a two-step Extracted Regression Tree approach to achieve this goal. In the first step, we train a black box prediction algorithm. In the second step, we extract a regression tree from the output of the black box algorithm that allows direct interpretation of medically relevant risk factors. We use data from a large teaching hospital in Asia to learn the ML model and verify our two-step approach.
The two-step method can obtain similar prediction performance as the best black box model, such as Neural Networks, measured by three metrics: accuracy, the Area Under the Curve (AUC) and the Area Under the Precision-Recall Curve (AUPRC), while maintaining interpretability. Further, to examine whether the prediction results match the known medical insights (i.e., the model is truly interpretable and produces reasonable results), we show that key readmission risk factors extracted by the two-step approach are consistent with those found in the medical literature.
The proposed two-step approach yields meaningful prediction results that are both accurate and interpretable. This study suggests a viable means to improve the trust of machine learning based models in clinical practice for predicting readmissions through the two-step approach.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Dedicated clinics can be established in an influenza pandemic to isolate people and potentially reduce opportunities for influenza transmission. However, their operation requires resources and their ...existence may attract the worried-well. In this study, we quantify the impact of opening dedicated influenza clinics during a pandemic based on an agent-based simulation model across a time-varying social network of households, workplaces, schools, community locations, and health facilities in the state of Georgia. We calculate performance measures, including peak prevalence and total attack rate, while accounting for clinic operations, including timing and location. We find that opening clinics can reduce disease spread and hospitalizations even when visited by the worried-well, open for limited weeks, or open in limited locations, and especially when the clinics are in operation during times of highest prevalence. Specifically, peak prevalence, total attack rate, and hospitalization reduced 0.07-0.32%, 0.40-1.51%, 0.02-0.09%, respectively, by operating clinics for the pandemic duration.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
Background
Inflammatory bowel diseases (IBD) are chronic relapsing–remitting inflammatory diseases of the gastrointestinal tract that are typically categorized into two subtypes: Crohn's ...disease (CD) and ulcerative colitis (UC). Although MSCs therapy has achieved encouraging outcomes in IBD therapy, objective responses are limited in colon fibrosis stenosis owing to the complicated microenvironment of CD and MSCs heterogeneity of quality. Here, we chose IFN-γ and kynurenic acid (KYNA) to overcome the low response and heterogeneity of human adipose-derived MSCs (hADSCs) to treat IBD and expand the therapeutic effects based on the excellent ability of IFN-γ and KYNA to promote indoleamine 2,3-dioxygenase-1 (IDO-1) signaling, providing a potential protocol to treat IBD and fibrosis disease.
Methods
hADSCs were isolated, cultured, and identified from human abdominal adipose tissue. The CD pathology-like acute colitis and chronic colon fibrosis rat model was induced by 2,4,6-trinitrobenzen sulfonic acid (TNBS). hADSCs were pretreated in vitro with IFN-γ and KYNA and then were transplanted intravenously at day 1 and 3 of TNBS administration in colitis along with at day 1, 15, and 29 of TNBS administration in chronic colonic fibrosis. Therapeutic efficacy was evaluated by body weights, disease activity index, pathological staining, real-time PCR, Western blot, and flow cytometry. For knockout of IDO-1, hADSCs were transfected with IDO-1-targeting small gRNA carried on a CRISPR-Cas9-lentivirus vector.
Results
hADSCs treated with IFN-γ and KYNA significantly upregulated the expression and secretion of IDO-1, which has effectively ameliorated CD pathology-like colitis injury and fibrosis. Notably, the ability of hADSCs with IDO-1 knockout to treat colitis was significantly impaired and diminished the protective effects of the primed hADSCs with IFN-γ and KYNA.
Conclusion
Inflammatory cytokines IFN-γ- and KYNA-treated hADSCs more effectively alleviate TNBS-induced colitis and colonic fibrosis through an IDO-1-dependent manner. Primed hADSCs are a promising new strategy to improve the therapeutic efficacy of MSCs and worth further research.
Abstract
We study a discrete‐time queueing network with blocking that is primarily motivated by outpatient network management. To tackle the curse of dimensionality in performance analysis, we ...develop a refined mean‐field approximation that deals with changing population size, a nonconventional feature that makes the analysis challenging within the existing literature. We explicitly quantify the convergence rate for this approximation as with being the system size. Not only is this convergence better than the convergence proven in prior work, but our approximation shows a significant improvement in performance prediction accuracy when the system size is small, compared to the conventional (unrefined) mean‐field approximation. This accuracy makes our approximation appealing to support decision‐making in practice.
During the 2009 H1N1 influenza pandemic, concerns arose about the potential negative effects of mass public gatherings and travel on the course of the pandemic. Better understanding the potential ...effects of temporal changes in social mixing patterns could help public officials determine if and when to cancel large public gatherings or enforce regional travel restrictions, advisories, or surveillance during an epidemic.
We develop a computer simulation model using detailed data from the state of Georgia to explore how various changes in social mixing and contact patterns, representing mass gatherings and holiday traveling, may affect the course of an influenza pandemic. Various scenarios with different combinations of the length of the mass gatherings or traveling period (range: 0.5 to 5 days), the proportion of the population attending the mass gathering events or on travel (range: 1% to 50%), and the initial reproduction numbers R0 (1.3, 1.5, 1.8) are explored.
Mass gatherings that occur within 10 days before the epidemic peak can result in as high as a 10% relative increase in the peak prevalence and the total attack rate, and may have even worse impacts on local communities and travelers' families. Holiday traveling can lead to a second epidemic peak under certain scenarios. Conversely, mass traveling or gatherings may have little effect when occurring much earlier or later than the epidemic peak, e.g., more than 40 days earlier or 20 days later than the peak when the initial R0 = 1.5.
Our results suggest that monitoring, postponing, or cancelling large public gatherings may be warranted close to the epidemic peak but not earlier or later during the epidemic. Influenza activity should also be closely monitored for a potential second peak if holiday traveling occurs when prevalence is high.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Malignant ascites frequently occur in patients with advanced ovarian cancer at initial diagnosis, and in almost all cases of relapse, they are closely related to poor prognosis, chemoresistance, and ...metastasis. To date, effective management strategies have been limited. In this study, we aimed to investigate the effects of oncolytic adenovirus (OV) on malignant ascites in a mouse model of advanced ovarian cancer. The results suggested that OV conferred an effective ability to reduce ascites development and prolong overall survival. Further analysis of the ascitic immune microenvironment revealed that OV treatment promoted T cell infiltration, activation, and differentiation into the effector phenotype; reprogrammed macrophages toward the M1-like phenotype; and increased the ratios of both CD8+ T cells to CD4+ T cells and M1 to M2 macrophages. However, immunosuppressive factors such as PD-1, LAG-3, and Tregs emerged after treatment. Combination therapy including OV, CSF-1R inhibitor PLX3397, and anti-PD-1 remarkably delayed the progression of ascites, and combination therapy induced a greater extent of T cell infiltration, proliferation, and activation. This study provides experimental and theoretical evidence for oncolytic virus-based treatment of malignant ascites, which may further contribute to advanced ovarian cancer therapy.
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Effective management of ascites in ovarian cancer has been lacking. Here, we found that oncolytic adenovirus could treat ascites of ovarian cancer by reprogramming the ascitic immune microenvironment. Our study provides experimental and theoretical evidence for oncolytic virus-based treatment of malignant ascites, which may further contribute to advanced ovarian cancer therapy.
When having access to demand forecasts, a crucial question is how to effectively use this information to make better resource allocation decisions, especially during demand surges like the COVID-19 ...pandemic. Despite the emergence of various advanced prediction models for hospital resources, there has been a lack of prescriptive solutions for hospital managers seeking concrete decision support, for example, guidance on whether to allocate beds from other specialties to meet the surge demand from COVID-19 patients by postponing elective surgeries. In their paper “Optimal Routing under Demand Surge: the Value of Future Arrival Rate,” the authors present a systematic framework to incorporate future demand into routing decisions in parallel server systems with partial flexibility and quantify the benefits of doing so. They propose a simple and interpretable two-stage index-based policy that explicitly incorporates demand forecasts into real-time routing decisions. Their analytical and numerical results demonstrate the policy’s effectiveness, even in the presence of large prediction errors.
Motivated by the growing availability of advanced demand forecast tools, we study how to use future demand information in designing routing strategies in queueing systems under demand surges. We consider a parallel server system operating in a nonstationary environment with general time-varying arrival rates. Servers are cross-trained to help nonprimary customer classes during demand surges. However, such flexibility comes with various operational costs, such as a loss of efficiency and inconvenience in coordination. We characterize how to incorporate the future arrival information into the routing policy to balance the tradeoff between various costs and quantify the benefit of doing so. Based on transient fluid control analysis, we develop a two-stage index-based look-ahead policy that explicitly takes the overflow costs and future arrival rates into account. The policy has an interpretable structure, is easy to implement and is adaptive when the future arrival information is inaccurate. In the special case of the N-model, we prove that this policy is asymptotically optimal even in the presence of certain prediction errors in the demand forecast. We substantiate our theoretical analysis with extensive numerical experiments, showing that our policy achieves superior performance compared with other benchmark policies (i) in complicated parallel server systems and (ii) when the demand forecast is imperfect with various forms of prediction errors.
Funding: This work was supported by the National Science Foundation Civil, Mechanical, and Manufacturing Innovation Grant 1944209.
Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.0282 .
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