Background And Aims: A study was done to create and run a discrete event simulation in the outpatient department (OPD) of a tertiary care cancer hospital in North India to project and optimize resource deployment.
Methods: The OPD process & workflow as per the expected load at tertiary care cancer hospital were finalized with various stakeholders in a focused group discussion. The finalized OPD process & workflow along with the OPD Building plans were utilized to develop a discrete event simulation model for the OPD at a tertiary care cancer hospital using a discrete event simulator. The simulation model thus developed was tested with incremental patient loads in 5 different scenarios/"What if" situations (Scenario 1-5). The data regarding initial patient load and resources deployed was taken from on-ground observations at the tertiary care cancer hospital.
Results: It was found that rooms and doctors were over-utilized and support staff utilization remained low. This was implemented with a lesser waiting time for patients. No additional support staff was provided thus improving utilization of existing staff and saving on resources. The simulations enabled us to deploy resources just when it was required, which ensured optimal utilization and better efficiency. The peak census helped us to determine the capacity of the waiting area in different scenarios with incremental patient load and resource deployment.
Conclusion: The simulation software was very helpful, as "what if scenarios" could be created and the system tested, without disturbing the normal functioning of OPD. This enabled decision-making before making on-ground changes which saved a lot of time and money. Also, the processes of the old system were reengineered to fit the needs of changing times.
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http://dx.doi.org/10.1002/hsr2.627 | DOI Listing |
Traditional clustering and visualization approaches in human genetics often operate under frameworks that assume inherent, discrete groupings . These methods can inadvertently simplify multifaceted relationships, functioning to entrench the idea of typological groups . We introduce a network-based pipeline and visualization tool grounded in relational thinking , which constructs networks from a variety of genetic similarity metrics.
View Article and Find Full Text PDFArch Virol
January 2025
Department of Virology, National Institute of Health (NIH), 45500, Park Rd, Chak Shahzad, Islamabad, Pakistan.
Pakistan has experienced a total of six COVID-19 waves throughout the pandemic, each driven by distinct SARS-CoV-2 lineages. This study explores the introduction of Omicron lineage BA.4 into Pakistan, which contributed to the sixth wave between June and September 2022.
View Article and Find Full Text PDFMed Decis Making
January 2025
Department of Health Policy, Stanford University School of Medicine, Stanford, CA, USA.
The nonparametric sampling method is generic and can sample times to an event from any discrete (or discretizable) hazard without requiring any parametric assumption.The method is showcased with 5 commonly used distributions in discrete-event simulation models.The method produced very similar expected times to events, as well as their probability distribution, compared with analytical results.
View Article and Find Full Text PDFChaos
January 2025
School of Automation and Electrical Engineering, Linyi University, Linyi 276005, China.
This paper mainly focuses on investigating the discrete event dynamic decision-making process with two noncooperative intelligent agents, defined as event dynamic games (EDGs). We introduce a novel state space model and analyze the existence of its equilibrium solution. Additionally, we apply principles of network evolution to address the challenge of event dynamic game network modeling.
View Article and Find Full Text PDFCancer Causes Control
January 2025
North Valley Breast Clinic, 1335 Buenaventura Blvd, Suite 204, Redding, CA, 96001, USA.
Objectives: Automated breast ultrasound imaging (ABUS) results in a reduction in breast cancer stage at diagnosis beyond that seen with mammographic screening in women with increased breast density or who are at a high risk of breast cancer. It is unknown if the addition of ABUS to mammography or ABUS imaging alone, in this population, is a cost-effective screening strategy.
Methods: A discrete event simulation (Monte Carlo) model was developed to assess the costs of screening, diagnostic evaluation, biopsy, and breast cancer treatment.
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