A patient flow simulator for healthcare management education

Simulation and analysis of patient flow can contribute to the safe and efficient functioning of a healthcare system, yet it is rarely incorporated into routine healthcare management, partially due to the technical training required. This paper introduces a free and open source patient flow simulation software tool that enables training and experimentation with healthcare management decisions and their impact on patient flow. Users manage their simulated hospital with a simple web-based graphical interface. The model is a stochastic discrete event simulation in which patients are transferred between wards of a hospital according to their treatment needs. Entry to each ward is managed by queues, with different policies for queue management and patient prioritisation per ward. Users can manage a simulated hospital, distribute resources between wards and decide how those resources should be prioritised. Simulation results are immediately available for analysis in-browser, including performance against targets, patient flow networks and ward occupancy. The patient flow simulator, freely available at https://khp-informatics.github.io/patient-flow-simulator, is an interactive educational tool that allows healthcare students and professionals to learn important concepts of patient flow and healthcare management

Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records

Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from current knowledge. We constructed a knowledge graph containing four types of node: drugs, protein targets, indications and adverse reactions. Using this graph, we developed a machine learning algorithm based on a simple enrichment test and first demonstrated this method performs extremely well at classifying known causes of adverse reactions (AUC 0.92). A cross validation scheme in which 10% of drug-adverse reaction edges were systematically deleted per fold showed that the method correctly predicts 68% of the deleted edges on average. Next, a subset of adverse reactions that could be reliably detected in anonymised electronic health records from South London and Maudsley NHS Foundation Trust were used to validate predictions from the model that are not currently known in public databases. High-confidence predictions were validated in electronic records significantly more frequently than random models, and outperformed standard methods (logistic regression, decision trees and support vector machines). This approach has the potential to improve patient safety by predicting adverse reactions that were not observed during randomised trials

Using Computer Simulation for Reducing the Appointment Lead-Time in a Public Pediatric Outpatient Department

Pediatric outpatient departments aim to provide a pleasant, effective and continuing care to children. However, a problem in these units is the long waiting time for children to receive an appointment. Prolonged appointment lead-time remains a global challenge since it results in delayed diagnosis and treatment causing increased morbidity and dissatisfaction. Additionally, it leads to an increased number of hospitalization and emergency department visits which augments the financial burden faced by healthcare systems. Despite these considerations, the studies directly concentrating on the reduction of appointment lead-time in these departments are largely limited. Therefore, this paper proposes the application of Discrete-event Simulation (DES) approach to evaluate potential improvement strategies aiming at reducing average appointment lead-time. Initially, the outpatient department is characterized to effectively identify the main activities, process variables, interactions, and system constraints. After data collection, input analysis is conducted through intra-variable independence, homogeneity and goodness-of-fit tests followed by the creation of a simulation model representing the real pediatric outpatient department. Then, Mann-Whitney tests are used to prove whether the model was statistically comparable with the real-world system. After this, the outpatient department performance is assessed in terms of average appointment lead-time and resource utilization. Finally, three improvement scenarios are assessed technically and financially, to determine if they are viable for implementation. A case study of a mixed-patient type environment in a public pediatric outpatient department has been explored to validate the proposed methodology. Statistical tests demonstrate that appointment lead-time in pediatric outpatient departments may be meaningfully minimized using this approach. © 2019, Springer Nature Switzerland AG

Identification of novel modifiers of Aβ toxicity by transcriptomic analysis in the fruitfly.

The strongest risk factor for developing Alzheimer's Disease (AD) is age. Here, we study the relationship between ageing and AD using a systems biology approach that employs a Drosophila (fruitfly) model of AD in which the flies overexpress the human Aβ42 peptide. We identified 712 genes that are differentially expressed between control and Aβ-expressing flies. We further divided these genes according to how they change over the animal's lifetime and discovered that the AD-related gene expression signature is age-independent. We have identified a number of differentially expressed pathways that are likely to play an important role in the disease, including oxidative stress and innate immunity. In particular, we uncovered two new modifiers of the Aβ phenotype, namely Sod3 and PGRP-SC1b

ACE-inhibitors and Angiotensin-2 Receptor Blockers are not associated with severe SARS-COVID19 infection in a multi-site UK acute Hospital Trust

Aims: The SARS‐CoV‐2 virus binds to the angiotensin‐converting enzyme 2 (ACE2) receptor for cell entry. It has been suggested that angiotensin‐converting enzyme inhibitors (ACEi) and angiotensin II receptor blockers (ARB), which are commonly used in patients with hypertension or diabetes and may raise tissue ACE2 levels, could increase the risk of severe COVID‐19 infection. Methods and results: We evaluated this hypothesis in a consecutive cohort of 1200 acute inpatients with COVID‐19 at two hospitals with a multi‐ethnic catchment population in London (UK). The mean age was 68 ± 17 years (57% male) and 74% of patients had at least one comorbidity. Overall, 415 patients (34.6%) reached the primary endpoint of death or transfer to a critical care unit for organ support within 21 days of symptom onset. A total of 399 patients (33.3%) were taking ACEi or ARB. Patients on ACEi/ARB were significantly older and had more comorbidities. The odds ratio for the primary endpoint in patients on ACEi and ARB, after adjustment for age, sex and co‐morbidities, was 0.63 (95% confidence interval 0.47–0.84, P < 0.01). Conclusions: There was no evidence for increased severity of COVID‐19 in hospitalised patients on chronic treatment with ACEi or ARB. A trend towards a beneficial effect of ACEi/ARB requires further evaluation in larger meta‐analyses and randomised clinical trials

Integrating Lean Six Sigma and discrete-event simulation for shortening the appointment lead-time in gynecobstetrics departments: a case study

Long waiting time to appointment may be a worry for pregnant women, particularly those who need perinatology consultation since it could increase anxiety and, in a worst case scenario, lead to an increase in fetal, infant, and maternal mortality. Treatment costs may also increase since pregnant women with diverse pathologies can develop more severe complications. As a step towards improving this process, we propose a methodological approach to reduce the appointment lead-time in outpatient gynecobstetrics departments. This framework involves combining the Six Sigma method to identify defects in the appointment scheduling process with a discrete-event simulation (DES) to evaluate the potential success of removing such defects in simulation before we resort to changing the real-world healthcare system. To do these, we initially characterize the gynecobstetrics department using a SIPOC diagram. Then, six sigma performance metrics are calculated to evaluate how well the department meets the government target in relation to the appointment lead-time. Afterwards, a cause-and-effect analysis is undertaken to identify potential causes of appointment lead-time variation. These causes are later validated through ANOVA, regression analysis, and DES. Improvement scenarios are next designed and pretested through computer simulation models. Finally, control plans are deployed to maintain the results achieved through the implementation of the DES-Six sigma approach. The aforementioned framework was validated in a public gynecobstetrics outpatient department. The results revealed that mean waiting time decreased from 6.9 days to 4.1 days while variance passed from 2.46 days2 to 1.53 days2

Exoplanet Atmosphere Measurements from Transmission Spectroscopy and other Planet-Star Combined Light Observations

It is possible to learn a great deal about exoplanet atmospheres even when we cannot spatially resolve the planets from their host stars. In this chapter, we overview the basic techniques used to characterize transiting exoplanets - transmission spectroscopy, emission and reflection spectroscopy, and full-orbit phase curve observations. We discuss practical considerations, including current and future observing facilities and best practices for measuring precise spectra. We also highlight major observational results on the chemistry, climate, and cloud properties of exoplanets.Comment: Accepted review chapter; Handbook of Exoplanets, eds. Hans J. Deeg and Juan Antonio Belmonte (Springer-Verlag). 22 pages, 6 figure

Ageing, Muscle Power and Physical Function: A Systematic Review and Implications for Pragmatic Training Interventions.

BACKGROUND: The physiological impairments most strongly associated with functional performance in older people are logically the most efficient therapeutic targets for exercise training interventions aimed at improving function and maintaining independence in later life. OBJECTIVES: The objectives of this review were to (1) systematically review the relationship between muscle power and functional performance in older people; (2) systematically review the effect of power training (PT) interventions on functional performance in older people; and (3) identify components of successful PT interventions relevant to pragmatic trials by scoping the literature. METHODS: Our approach involved three stages. First, we systematically reviewed evidence on the relationship between muscle power, muscle strength and functional performance and, second, we systematically reviewed PT intervention studies that included both muscle power and at least one index of functional performance as outcome measures. Finally, taking a strong pragmatic perspective, we conducted a scoping review of the PT evidence to identify the successful components of training interventions needed to provide a minimally effective training dose to improve physical function. RESULTS: Evidence from 44 studies revealed a positive association between muscle power and indices of physical function, and that muscle power is a marginally superior predictor of functional performance than muscle strength. Nine studies revealed maximal angular velocity of movement, an important component of muscle power, to be positively associated with functional performance and a better predictor of functional performance than muscle strength. We identified 31 PT studies, characterised by small sample sizes and incomplete reporting of interventions, resulting in less than one-in-five studies judged as having a low risk of bias. Thirteen studies compared traditional resistance training with PT, with ten studies reporting the superiority of PT for either muscle power or functional performance. Further studies demonstrated the efficacy of various methods of resistance and functional task PT on muscle power and functional performance, including low-load PT and low-volume interventions. CONCLUSIONS: Maximal intended movement velocity, low training load, simple training methods, low-volume training and low-frequency training were revealed as components offering potential for the development of a pragmatic intervention. Additionally, the research area is dominated by short-term interventions producing short-term gains with little consideration of the long-term maintenance of functional performance. We believe the area would benefit from larger and higher-quality studies and consideration of optimal long-term strategies to develop and maintain muscle power and physical function over years rather than weeks

First Observation of τ→3πηντ\tau\to 3\pi\eta\nu_{\tau} and τ→f1πντ\tau\to f_{1}\pi\nu_{\tau} Decays

We have observed new channels for $\tau$ decays with an $\eta$ in the final state. We study 3-prong tau decays, using the $\eta\to\gamma\gamma$ and \eta\to 3\piz decay modes and 1-prong decays with two \piz's using the $\eta\to\gamma\gamma$ channel. The measured branching fractions are \B(\tau^{-}\to \pi^{-}\pi^{-}\pi^{+}\eta\nu_{\tau}) =(3.4^{+0.6}_{-0.5}\pm0.6)\times10^{-4} and \B(\tau^{-}\to \pi^{-}2\piz\eta\nu_{\tau} =(1.4\pm0.6\pm0.3)\times10^{-4}. We observe clear evidence for $f_1\to\eta\pi\pi$ substructure and measure \B(\tau^{-}\to f_1\pi^{-}\nu_{\tau})=(5.8^{+1.4}_{-1.3}\pm1.8)\times10^{-4}. We have also searched for $\eta'(958)$ production and obtain 90% CL upper limits \B(\tau^{-}\to \pi^{-}\eta'\nu_\tau)<7.4\times10^{-5} and \B(\tau^{-}\to \pi^{-}\piz\eta'\nu_\tau)<8.0\times10^{-5}.Comment: 11 page postscript file, postscript file also available through http://w4.lns.cornell.edu/public/CLN