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1.
PLoS One ; 17(11): e0277340, 2022.
Article in English | MEDLINE | ID: covidwho-2109331

ABSTRACT

Due to the high prevalence of patients attending with urinary tract infection (UTI) symptoms, the use of flow-cytometry as a rapid screening tool to avoid unnecessary cultures is becoming a widely used system in clinical practice. However, the recommended cut-points applied in flow-cytometry systems differ substantially among authors, making it difficult to obtain reliable conclusions. Here, we present FlowUTI, a shiny web-application created to establish optimal cut-off values in flow-cytometry for different UTI markers, such as bacterial or leukocyte counts, in urine from patients with UTI symptoms. This application provides a user-friendly graphical interface to perform robust statistical analysis without a specific training. Two datasets are analyzed in this manuscript: one composed of 204 urine samples from neonates and infants (≤3 months old) attended in the emergency department with suspected UTI; and the second dataset including 1174 urines samples from an elderly population attended at the primary care level. The source code is available on GitHub (https://github.com/GuillermoMG-HUVR/Microbiology-applications/tree/FlowUTI/FlowUTI). The web application can be executed locally from the R console. Alternatively, it can be freely accessed at https://covidiario.shinyapps.io/flowuti/. FlowUTI provides an easy-to-use environment for evaluating the efficiency of the urinary screening process with flow-cytometry, reducing the computational burden associated with this kind of analysis.


Subject(s)
Urinary Tract Infections , Aged , Infant , Infant, Newborn , Humans , Flow Cytometry , Urinary Tract Infections/microbiology , Urinalysis , Leukocyte Count , Software
2.
Int J Med Inform ; 168: 104897, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2082412

ABSTRACT

BACKGROUND: The burden on healthcare systems is mounting continuously owing to population growth and aging, overuse of medical services, and the recent COVID-19 pandemic. This overload is also causing reduced healthcare quality and outcomes. One solution gaining momentum is the integration of intelligent self-assessment tools, known as symptom-checkers, into healthcare-providers' systems. To the best of our knowledge, no study so far has investigated the data-gathering capabilities of these tools, which represent a crucial resource for simulating doctors' skills in medical-interviews. OBJECTIVES: The goal of this study was to evaluate the data-gathering function of currently available chatbot symptom-checkers. METHODS: We evaluated 8 symptom-checkers using 28 clinical vignettes from the repository of MSD-Manual case studies. The mean number of predefined pertinent findings for each case was 31.8 ± 6.8. The vignettes were entered into the platforms by 3 medical students who simulated the role of the patient. For each conversation, we obtained the number of pertinent findings retrieved and the number of questions asked. We then calculated the recall-rates (pertinent-findings retrieved out of all predefined pertinent-findings), and efficiency-rates (pertinent-findings retrieved out of the number of questions asked) of data-gathering, and compared them between the platforms. RESULTS: The overall recall rate for all symptom-checkers was 0.32(2,280/7,112;95 %CI 0.31-0.33) for all pertinent findings, 0.37(1,110/2,992;95 %CI 0.35-0.39) for present findings, and 0.28(1140/4120;95 %CI 0.26-0.29) for absent findings. Among the symptom-checkers, Kahun platform had the highest recall rate with 0.51(450/889;95 %CI 0.47-0.54). Out of 4,877 questions asked overall, 2,280 findings were gathered, yielding an efficiency rate of 0.46(95 %CI 0.45-0.48) across all platforms. Kahun was the most efficient tool 0.74 (95 %CI 0.70-0.77) without a statistically significant difference from Your.MD 0.69(95 %CI 0.65-0.73). CONCLUSION: The data-gathering performance of currently available symptom checkers is questionable. From among the tools available, Kahun demonstrated the best overall performance.


Subject(s)
COVID-19 , Humans , COVID-19/diagnosis , COVID-19/epidemiology , Pandemics , Quality of Health Care , Software
3.
Sensors (Basel) ; 22(20)2022 Oct 18.
Article in English | MEDLINE | ID: covidwho-2071713

ABSTRACT

This paper presents an improved IoT-based system designed to help teachers handle lessons in the classroom in line with COVID-19 restrictions. The system counts the number of people in the classroom as well as their distribution within the classroom. The proposed IoT system consists of three parts: a Gate node, IoT nodes, and server. The Gate node, installed at the door, can provide information about the number of persons entering or leaving the room using door crossing detection. The Arduino-based module NodeMCU was used as an IoT node and sets of ultrasonic distance sensors were used to obtain information about seat occupancy. The system server runs locally on a Raspberry Pi and the teacher can connect to it using a web application from the computer in the classroom or a smartphone. The teacher is able to set up and change the settings of the system through its GUI. A simple algorithm was designed to check the distance between occupied seats and evaluate the accordance with imposed restrictions. This system can provide high privacy, unlike camera-based systems.


Subject(s)
COVID-19 , Humans , Privacy , Smartphone , Software , Algorithms
4.
Sensors (Basel) ; 22(19)2022 Sep 26.
Article in English | MEDLINE | ID: covidwho-2066347

ABSTRACT

Online learning has made it possible to attend programming classes regardless of the constraint that all students should be gathered in a classroom. However, it has also made it easier for students to cheat on assignments. Therefore, we need a system to deal with cheating on assignments. This study presents a Watcher system, an automated cloud-based software platform for impartial and convenient online programming hands-on education. The primary features of Watcher are as follows. First, Watcher offers a web-based integrated development environment (Web-IDE) that allows students to start programming immediately without the need for additional installation and configuration. Second, Watcher collects and monitors the coding activity of students automatically in real-time. As Watcher provides the history of the coding activity to instructors in log files, the instructors can investigate suspicious coding activities such as plagiarism, even for a short source code. Third, Watcher provides facilities to remotely manage and evaluate students' hands-on programming assignments. We evaluated Watcher in a Unix system programming class for 96 students. The results showed that Watcher improves the quality of the coding experience for students through Web-IDE, and it offers instructors valuable data that can be used to analyze the various coding activities of individual students.


Subject(s)
Education, Distance , Fitness Trackers , Cloud Computing , Humans , Software , Students
5.
Bioinformatics ; 38(22): 5124-5125, 2022 Nov 15.
Article in English | MEDLINE | ID: covidwho-2062861

ABSTRACT

MOTIVATION: The ability to automatically conduct quality control checks on phylogenetic analyses is becoming more important with the increase in genetic sequencing and the use of real-time pipelines e.g. in the SARS-CoV-2 era. Implementations of real-time phylogenetic analyses require automated testing to make sure that problems in the data are caught automatically within analysis pipelines and in a timely manner. Here, we present Phytest (version 1.1) a tool for automating quality control checks on sequences, trees and metadata during phylogenetic analyses. RESULTS: Phytest is a phylogenetic analysis testing program that easily integrates into existing phylogenetic pipelines. We demonstrate the utility of Phytest with real-world examples. AVAILABILITY AND IMPLEMENTATION: Phytest source code is available on GitHub (https://github.com/phytest-devs/phytest) and can be installed via PyPI with the command 'pip install phytest'. Extensive documentation can be found at https://phytest-devs.github.io/phytest/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Phylogeny , SARS-CoV-2/genetics , Software , Quality Control
6.
Nucleic Acids Res ; 50(D1): D106-D110, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-2062938

ABSTRACT

The European Nucleotide Archive (ENA, https://www.ebi.ac.uk/ena), maintained at the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) provides freely accessible services, both for deposition of, and access to, open nucleotide sequencing data. Open scientific data are of paramount importance to the scientific community and contribute daily to the acceleration of scientific advance. Here, we outline the major updates to ENA's services and infrastructure that have been delivered over the past year.


Subject(s)
Computational Biology , Databases, Nucleic Acid , Nucleotides/genetics , Software , High-Throughput Nucleotide Sequencing , Humans , Internet , Molecular Sequence Annotation , Nucleotides/classification
7.
Nucleic Acids Res ; 50(D1): D777-D784, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-2062936

ABSTRACT

GMrepo (data repository for Gut Microbiota) is a database of curated and consistently annotated human gut metagenomes. Its main purposes are to increase the reusability and accessibility of human gut metagenomic data, and enable cross-project and phenotype comparisons. To achieve these goals, we performed manual curation on the meta-data and organized the datasets in a phenotype-centric manner. GMrepo v2 contains 353 projects and 71,642 runs/samples, which are significantly increased from the previous version. Among these runs/samples, 45,111 and 26,531 were obtained by 16S rRNA amplicon and whole-genome metagenomics sequencing, respectively. We also increased the number of phenotypes from 92 to 133. In addition, we introduced disease-marker identification and cross-project/phenotype comparison. We first identified disease markers between two phenotypes (e.g. health versus diseases) on a per-project basis for selected projects. We then compared the identified markers for each phenotype pair across datasets to facilitate the identification of consistent microbial markers across datasets. Finally, we provided a marker-centric view to allow users to check if a marker has different trends in different diseases. So far, GMrepo includes 592 marker taxa (350 species and 242 genera) for 47 phenotype pairs, identified from 83 selected projects. GMrepo v2 is freely available at: https://gmrepo.humangut.info.


Subject(s)
Databases, Genetic , Intestinal Neoplasms/microbiology , Metagenome , Microbiota , Biomarkers/blood , Datasets as Topic , Gastrointestinal Microbiome/genetics , High-Throughput Nucleotide Sequencing , Humans , Internet , Intestinal Neoplasms/blood , Intestinal Neoplasms/genetics , Intestinal Neoplasms/pathology , Molecular Sequence Annotation , Phenotype , RNA, Ribosomal, 16S , Software
8.
Int J Mol Sci ; 23(19)2022 Sep 30.
Article in English | MEDLINE | ID: covidwho-2058905

ABSTRACT

This Special Issue was intended as a dissemination forum where the major results pursued by the EXSCALATE4CoV project (E4C, https://www [...].


Subject(s)
Computing Methodologies , Pandemics , Pandemics/prevention & control , Software
9.
PLoS One ; 17(9): e0274414, 2022.
Article in English | MEDLINE | ID: covidwho-2039413

ABSTRACT

The most important information about microorganisms might be their accurate genome sequence. Using current Next Generation Sequencing methods, sequencing data can be generated at an unprecedented pace. However, we still lack tools for the automated and accurate reference-based genotyping of viral sequencing reads. This paper presents our pipeline designed to reconstruct the dominant consensus genome of viral samples and analyze their within-host variability. We benchmarked our approach on numerous datasets and showed that the consensus genome of samples could be obtained reliably without further manual data curation. Our pipeline can be a valuable tool for fast identifying viral samples. The pipeline is publicly available on the project's GitHub page (https://github.com/laczkol/QVG).


Subject(s)
High-Throughput Nucleotide Sequencing , Software , Genome , Genotype , High-Throughput Nucleotide Sequencing/methods
10.
BMC Biol ; 20(1): 183, 2022 08 23.
Article in English | MEDLINE | ID: covidwho-2038744

ABSTRACT

BACKGROUND: Efficient tools allowing the extraction of 2D surfaces from 3D-microscopy data are essential for studies aiming to decipher the complex cellular choreography through which epithelium morphogenesis takes place during development. Most existing methods allow for the extraction of a single and smooth manifold of sufficiently high signal intensity and contrast, and usually fail when the surface of interest has a rough topography or when its localization is hampered by other surrounding structures of higher contrast. Multiple surface segmentation entails laborious manual annotations of the various surfaces separately. RESULTS: As automating this task is critical in studies involving tissue-tissue or tissue-matrix interaction, we developed the Zellige software, which allows the extraction of a non-prescribed number of surfaces of varying inclination, contrast, and texture from a 3D image. The tool requires the adjustment of a small set of control parameters, for which we provide an intuitive interface implemented as a Fiji plugin. CONCLUSIONS: As a proof of principle of the versatility of Zellige, we demonstrate its performance and robustness on synthetic images and on four different types of biological samples, covering a wide range of biological contexts.


Subject(s)
Algorithms , Microscopy , Image Processing, Computer-Assisted/methods , Imaging, Three-Dimensional/methods , Microscopy/methods , Software
11.
BMC Med Inform Decis Mak ; 22(1): 237, 2022 09 09.
Article in English | MEDLINE | ID: covidwho-2038728

ABSTRACT

BACKGROUND: Effective deployment of AI tools in primary health care requires the engagement of practitioners in the development and testing of these tools, and a match between the resulting AI tools and clinical/system needs in primary health care. To set the stage for these developments, we must gain a more in-depth understanding of the views of practitioners and decision-makers about the use of AI in primary health care. The objective of this study was to identify key issues regarding the use of AI tools in primary health care by exploring the views of primary health care and digital health stakeholders. METHODS: This study utilized a descriptive qualitative approach, including thematic data analysis. Fourteen in-depth interviews were conducted with primary health care and digital health stakeholders in Ontario. NVivo software was utilized in the coding of the interviews. RESULTS: Five main interconnected themes emerged: (1) Mismatch Between Envisioned Uses and Current Reality-denoting the importance of potential applications of AI in primary health care practice, with a recognition of the current reality characterized by a lack of available tools; (2) Mechanics of AI Don't Matter: Just Another Tool in the Toolbox- reflecting an interest in what value AI tools could bring to practice, rather than concern with the mechanics of the AI tools themselves; (3) AI in Practice: A Double-Edged Sword-the possible benefits of AI use in primary health care contrasted with fundamental concern about the possible threats posed by AI in terms of clinical skills and capacity, mistakes, and loss of control; (4) The Non-Starters: A Guarded Stance Regarding AI Adoption in Primary Health Care-broader concerns centred on the ethical, legal, and social implications of AI use in primary health care; and (5) Necessary Elements: Facilitators of AI in Primary Health Care-elements required to support the uptake of AI tools, including co-creation, availability and use of high quality data, and the need for evaluation. CONCLUSION: The use of AI in primary health care may have a positive impact, but many factors need to be considered regarding its implementation. This study may help to inform the development and deployment of AI tools in primary health care.


Subject(s)
Artificial Intelligence , Software , Clinical Competence , Data Accuracy , Humans , Primary Health Care
12.
Int J Environ Res Public Health ; 19(18)2022 Sep 10.
Article in English | MEDLINE | ID: covidwho-2032933

ABSTRACT

Impacted by the COVID-19 epidemic, the human sub-health in national high-tech zones (hereinafter referred to as high-tech zones) has become more prominent. It is critical for the mental sub-health group in the high-tech zone to relieve the anxiety and tension caused by the pressure of life and work. This paper uses SketchUp virtual engine (Unity 2019) software, and 3D roaming technology to carry out the ecological landscape transformation design of the Baotzixi ecological corridor in the East Lake High-tech Zone, to construct a 3D roaming landscape scene and measure its therapeutic effect by inviting subjects to participate in an interactive experience experiment on the ErgoLAB platform. The results illustrate that: (1) the thermogram trend shows that the more attractive the 3D roaming landscape scene is, the stronger the subjects' interest is; (2) the participants have a positive emotional arousal state in the immersive experience of the 3D roaming landscape scene after the modification design; and (3) the mean skin conductance (SC) fluctuation variance of the subjects is 5.819%, indicating that the healing effect is significant in the state of positive emotional arousal. The research results show that there is a connection between the subjects and the 3D roaming landscape scene after the transformation design of "high interest, emotional arousal and significant healing".


Subject(s)
COVID-19 , COVID-19/epidemiology , Humans , Lakes , Software , Technology
13.
Stud Health Technol Inform ; 296: 17-24, 2022 Aug 17.
Article in English | MEDLINE | ID: covidwho-2022596

ABSTRACT

In Germany, the current COVID-19 cases are managed and reported by the local health authorities. The workload of their employees during the pandemic is high, especially in periods of high infection numbers. In this work a decision support toolkit for local health authorities is introduced. A demonstrator web application was developed with the R Shiny framework and is publicly accessible online. It contains five separate tools based on statistical models for specific use cases and corresponding questions of COVID-19 cases and their contacts. The underlying statistical methods have been implemented in a new open-source R package. The toolkit has the potential to support local health authorities' employees in their daily work. A simulated-based validation of the statistical models and a usability evaluation of the demonstrator application in a user study will be carried out in the future.


Subject(s)
COVID-19 , Esters , Humans , Models, Statistical , Pandemics , Software
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 986-990, 2022 07.
Article in English | MEDLINE | ID: covidwho-2018741

ABSTRACT

More than 500 millions of people were affected by the COVID-19 pandemic and in Peru there is an increasing the high numbers of cumulative cases; as well as the hospitalized people, where more than 20 % require mechanical ventilation. This condition with other respiratory diseases cause patients to remain connected to a mechanical ventilator until they regain the ability to perform this vital function on their own. Some prototypes with characteristics equivalent to a high-end mechanical ventilator have been developed. And therefore, this paper presents the design and simulation of an algorithm for the pressure-controlled pulmonary ventilation mode of the mechanical ventilator. The functional design of the algorithm uses the linear multi compartment mathematical model to simulate the respiratory system. Finally the results respond adequately under multiple scenarios, including variations of the ventilator and pulmonary parameters, where the algorithm presents encouraging results in the mechanical ventilator simulation. Clinical relevance - The algorithm presented in this study will allow to have better knowledge for a treatment and eventual clinical diagnosis in health centers, especially in eventual variants and outbreaks of COVID-19.


Subject(s)
COVID-19 , Algorithms , COVID-19/therapy , Humans , Pandemics , Software , Ventilators, Mechanical
15.
Bioinformatics ; 38(20): 4843-4845, 2022 10 14.
Article in English | MEDLINE | ID: covidwho-2017734

ABSTRACT

SUMMARY: Reliable and integrated data are prerequisites for effective research on the recent coronavirus disease 2019 (COVID-19) pandemic. The CovidGraph project integrates and connects heterogeneous COVID-19 data in a knowledge graph, referred to as 'CovidGraph'. It provides easy access to multiple data sources through a single point of entry and enables flexible data exploration. AVAILABILITY AND IMPLEMENTATION: More information on CovidGraph is available from the project website: https://healthecco.org/covidgraph/. Source code and documentation are provided on GitHub: https://github.com/covidgraph. SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.


Subject(s)
COVID-19 , COVID-19/epidemiology , Humans , Information Storage and Retrieval , Software
16.
Bioinformatics ; 38(20): 4720-4726, 2022 10 14.
Article in English | MEDLINE | ID: covidwho-2008507

ABSTRACT

MOTIVATION: Single cell RNA-Sequencing (scRNA-seq) has rapidly gained popularity over the last few years for profiling the transcriptomes of thousands to millions of single cells. This technology is now being used to analyse experiments with complex designs including biological replication. One question that can be asked from single cell experiments, which has been difficult to directly address with bulk RNA-seq data, is whether the cell type proportions are different between two or more experimental conditions. As well as gene expression changes, the relative depletion or enrichment of a particular cell type can be the functional consequence of disease or treatment. However, cell type proportion estimates from scRNA-seq data are variable and statistical methods that can correctly account for different sources of variability are needed to confidently identify statistically significant shifts in cell type composition between experimental conditions. RESULTS: We have developed propeller, a robust and flexible method that leverages biological replication to find statistically significant differences in cell type proportions between groups. Using simulated cell type proportions data, we show that propeller performs well under a variety of scenarios. We applied propeller to test for significant changes in cell type proportions related to human heart development, ageing and COVID-19 disease severity. AVAILABILITY AND IMPLEMENTATION: The propeller method is publicly available in the open source speckle R package (https://github.com/phipsonlab/speckle). All the analysis code for the article is available at the associated analysis website: https://phipsonlab.github.io/propeller-paper-analysis/. The speckle package, analysis scripts and datasets have been deposited at https://doi.org/10.5281/zenodo.7009042. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Subject(s)
COVID-19 , Single-Cell Analysis , Gene Expression Profiling , Humans , RNA , Sequence Analysis, RNA , Software
17.
Comput Intell Neurosci ; 2022: 7352160, 2022.
Article in English | MEDLINE | ID: covidwho-2001958

ABSTRACT

In recent years, studies have been conducted to quantify the relationship between microeconomic and macroeconomic development. Macroeconomics is the orientation of microeconomic development. Existing research hopes to quantify the relationship between macroeconomics and micro-firms, rather than just focusing on economic indicators. And some empirical studies try to use the relationship between them to discuss its usefulness for micro-firm decision-making. This article focuses on applying and developing aggregate earnings in connecting microenterprise earnings and macroeconomic development. To achieve this goal, this research did a comprehensive bibliometric analysis on macro-accounting on the two most influential databases, namely, Web of Science and Scopus. It used the information visualization software VOSviewer to draw knowledge maps to sort research lines. We also analyzed the research hotspots of macro-accounting in recent years according to the year scale and combined it with the neural network PSO-LSTM model to predict their future development. It turns out that the research on aggregate earnings related to economic growth has become a research hotspot in recent years. Scopus research and development potential is better than Web of Science in this field.


Subject(s)
Bibliometrics , Economic Development , Databases, Factual , Knowledge , Software
18.
PLoS Comput Biol ; 18(8): e1010409, 2022 08.
Article in English | MEDLINE | ID: covidwho-2002267

ABSTRACT

Accurate simulation of complex biological processes is an essential component of developing and validating new technologies and inference approaches. As an effort to help contain the COVID-19 pandemic, large numbers of SARS-CoV-2 genomes have been sequenced from most regions in the world. More than 5.5 million viral sequences are publicly available as of November 2021. Many studies estimate viral genealogies from these sequences, as these can provide valuable information about the spread of the pandemic across time and space. Additionally such data are a rich source of information about molecular evolutionary processes including natural selection, for example allowing the identification of new variants with transmissibility and immunity evasion advantages. To our knowledge, there is no framework that is both efficient and flexible enough to simulate the pandemic to approximate world-scale scenarios and generate viral genealogies of millions of samples. Here, we introduce a new fast simulator VGsim which addresses the problem of simulation genealogies under epidemiological models. The simulation process is split into two phases. During the forward run the algorithm generates a chain of population-level events reflecting the dynamics of the pandemic using an hierarchical version of the Gillespie algorithm. During the backward run a coalescent-like approach generates a tree genealogy of samples conditioning on the population-level events chain generated during the forward run. Our software can model complex population structure, epistasis and immunity escape.


Subject(s)
COVID-19 , Pandemics , COVID-19/epidemiology , Computer Simulation , Humans , SARS-CoV-2/genetics , Software
19.
Philos Trans R Soc Lond B Biol Sci ; 377(1861): 20210242, 2022 10 10.
Article in English | MEDLINE | ID: covidwho-2001544

ABSTRACT

Recent advances in Bayesian phylogenetics offer substantial computational savings to accommodate increased genomic sampling that challenges traditional inference methods. In this review, we begin with a brief summary of the Bayesian phylogenetic framework, and then conceptualize a variety of methods to improve posterior approximations via Markov chain Monte Carlo (MCMC) sampling. Specifically, we discuss methods to improve the speed of likelihood calculations, reduce MCMC burn-in, and generate better MCMC proposals. We apply several of these techniques to study the evolution of HIV virulence along a 1536-tip phylogeny and estimate the internal node heights of a 1000-tip SARS-CoV-2 phylogenetic tree in order to illustrate the speed-up of such analyses using current state-of-the-art approaches. We conclude our review with a discussion of promising alternatives to MCMC that approximate the phylogenetic posterior. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.


Subject(s)
COVID-19 , Software , Algorithms , Bayes Theorem , Humans , Markov Chains , Monte Carlo Method , Phylogeny , SARS-CoV-2/genetics
20.
Appl Health Econ Health Policy ; 20(5): 731-742, 2022 09.
Article in English | MEDLINE | ID: covidwho-2000145

ABSTRACT

BACKGROUND: Improved multiple sclerosis (MS) diagnosis and increased availability of intravenous disease-modifying treatments can lead to overburdening of infusion centres. This study was focused on developing a decision-support tool to help infusion centres plan their operations. METHODS: A discrete event simulation model ('ENTIMOS') was developed using Simul8 software in collaboration with clinical experts. Model inputs included treatment-specific clinical parameters, resources such as infusion chairs and nursing staff, and costs, while model outputs included patient throughput, waiting time, queue size, resource utilisation, and costs. The model was parameterised using characteristics of the Charing Cross Hospital Infusion Centre in London, UK, where 12 infusion chairs were deployed for 170 non-MS and 860 MS patients as of March 2021. The number of MS patients was projected to increase by seven new patients per week. RESULTS: The model-estimated waiting time for an infusion is, on average, 8 days beyond clinical recommendation in the first year of simulation. Without corrective action, the delay in receiving due treatment is anticipated to reach 30 days on average at 30 months from the start of simulation. Such system compromise can be prevented either by adding one infusion chair annually or switching 7% of existing patients or 24% of new patients to alternative MS treatments not requiring infusion. CONCLUSION: ENTIMOS is a flexible model of patient flow and care delivery in infusion centres serving MS patients. It allows users to simulate specific local settings and therefore identify measures that are necessary to avoid clinically significant treatment delay resulting in suboptimal care.


Subject(s)
Multiple Sclerosis , Computer Simulation , Hospitals , Humans , Multiple Sclerosis/drug therapy , Software
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