A Hybrid Fuzzy MCDM Approach to Identify the Intervention Priority Level of Covid-19 Patients in the Emergency Department: A Case Study DOI

Armando Perez-Aguilar,

Miguel Ortíz‐Barrios, Pablo Pancardo

et al.

Lecture notes in computer science, Journal Year: 2023, Volume and Issue: unknown, P. 284 - 297

Published: Jan. 1, 2023

Language: Английский

A Comprehensive Review of Patient Scheduling Techniques with Uncertainty DOI
Vaishali Choudhary, Apoorva Shastri,

Shivam Silswal

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

1
A forecasting tool for a hospital to plan inbound transfers of COVID-19 patients from other regions DOI Creative Commons
Mehmet A. Begen, Felipe F. Rodrigues,

Tim Rice

et al.

BMC Public Health, Journal Year: 2024, Volume and Issue: 24(1)

Published: Feb. 16, 2024

Abstract Background In April 2021, the province of Ontario, Canada, was at peak its third wave COVID-19 pandemic. Intensive Care Unit (ICU) capacity in Toronto metropolitan area insufficient to handle local COVID patients. As a result, some patients from were transferred other regions. Methods A spreadsheet-based Monte Carlo simulation tool built help large tertiary hospital plan and make informed decisions about number transfer it could accept hospitals. The model implemented Microsoft Excel enable be widely distributed easily used. estimates probability that each ward will overcapacity percentiles utilization daily for one-week planning horizon. Results used May 2021 February 2022 support ability transfers also ensure adequate inpatient bed human resources response various COVID-related scenarios, such as changes admission rates, managing impact intra-hospital outbreaks balancing with planned activity. Conclusions Coordination between hospitals necessary due high stress on health care system. simple can understand patient improve confidence leaders when making decisions. helpful investigating operational scenarios may preparing future or public emergencies.

Language: Английский

Citations

1
An explainable decision model based on extended belief-rule-based systems to predict admission to the intensive care unit during COVID-19 breakout DOI
Jing Zheng, Long-Hao Yang,

Ying‐Ming Wang

et al.

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 149, P. 110961 - 110961

Published: Oct. 19, 2023

Language: Английский

Citations

2
A Machine Learning Model for Predicting the Risk of Perinatal Mortality in Low-and-Middle-Income Countries: A Case Study DOI
Sebastián Arias-Fonseca, Miguel Ortíz‐Barrios, Alexandros Konios

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 233 - 250

Published: Jan. 1, 2024

Language: Английский

Citations

0
A Hybrid Mathematical-Simulation Approach to Hospital Beds Capacity Optimization for COVID-19 Pandemic Conditions DOI Creative Commons
Reza Maleki, Mohammad R. Taghizadeh, Rohollah Ghasemi

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: June 20, 2024

Abstract The Covid-19 pandemic was an unforeseen threat to human survival, and the efficiency of health sector faced a severe challenge. lack hospital beds one most critical concerns, optimizing capacity considered key issues. Due ageing population occasional occurrence environmental crises, demand for services need improved planning administration are increasing daily. Therefore, optimal allocation resources, particularly number beds, essential criterion medical center’s capacity, can substantially reduce patient waiting time treatment costs improve services. An ideal multi-objective integer programming problem is presented in this study reducing length stay stay. also considers constraints relating circumstances, given Corona's prevalence. Moreover, answer obtained using simulation model, mathematical optimization, simulation-based optimization approach. For purpose, modelling used minimize patients' time, hospitalizations, maintenance existing purchasing new bed. Following that, real-world conditions were introduced into model information acquired from month hospitalization patients during Coronavirus outbreak at Imam Hussein Hospital Tehran. After comparing simulated models, OptQuest technique revealed beds.

Language: Английский

Citations

0
Integrating discrete-event simulation and artificial intelligence for shortening bed waiting times in hospitalization departments during respiratory disease seasons DOI
Miguel Ortíz‐Barrios, Alessio Ishizaka, Maria Barbati

et al.

Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: 194, P. 110405 - 110405

Published: July 25, 2024

Language: Английский

Citations

0
A Case Study on AI to Automate Simulation Modelling DOI

Uchechukwu Obinwanne,

Wenying Feng

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 179 - 186

Published: Oct. 18, 2024

Language: Английский

Citations

0
A Hybrid Mathematical-Simulation Approach to Hospital Beds Capacity Optimization for COVID-19 Pandemic Conditions DOI
Reza Maleki, Mohammad R. Taghizadeh, Rohollah Ghasemi

et al.

Operations Research Forum, Journal Year: 2024, Volume and Issue: 5(4)

Published: Nov. 14, 2024

Language: Английский

Citations

0
Exploring the Impact of AI on Management and Healthcare for Streamlining Operations and Decision‐Making DOI

P. P. Mathur,

Ajay Kumar

Published: Dec. 1, 2024

Artificial intelligence (AI) is essential for streamlining operations and decision-making procedures. Managers can swiftly examine big datasets, spot patterns, make data-driven choices thanks to AI sophisticated algorithms, ML. solutions are used activities like resource allocation, tailored consumer experiences, predictive analytics in industries marketing, finance, human resources. Also, has become a disruptive force healthcare, resulting unparalleled progress. To understand the dynamics of shift across range businesses, this study explores impact on functional areas management including healthcare managerial The research acknowledges broadens application resources, healthcare. revealed key inputs from relating different management. delves into specific AI-driven applications tools that empower managers streamline operations, optimize foster innovation within their organizations.

Language: Английский

Citations

0
Implementation of smart devices in health crisis scenarios: risks and opportunities DOI Creative Commons
Roberto Losada Maestre, Rubén Sánchez Medero

Frontiers in Political Science, Journal Year: 2024, Volume and Issue: 6

Published: Dec. 17, 2024

The scarcity of healthcare resources, particularly during crises, is a reality. AI can help alleviate this deficiency. Tasks such as triage, diagnosis, or determining patient’s life-threatening risk are some the applications we delegate to algorithms. However, limited number real clinical experiences and lack research on its implementation mean that only partially understand risks involved in development. To contribute knowledge both opportunities management solution like presents, analyze case autonomous emergency vehicles. After conducting detailed literature review, adopt an innovative perspective: patient. We believe relationship established between patient technology, emotional connection, determine success implementing driving devices. Therefore, also propose simple solution: endowing technology with anthropomorphic features.

Language: Английский

Citations

0