Stress-testing the resilience of the Austrian healthcare system using agent-based simulation.
Ontology highlight
ABSTRACT:
SUBMITTER: Kaleta M
PROVIDER: S-EPMC9308034 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Background COVID-19 pandemic is presenting serious challenges to the world’s healthcare systems. The high communicability of the COVID-19 necessitates robust medical preparedness and vigilance. Objective To report on the simulation-based training and test preparedness activities to prepare healthcare workers (HCWs) for effective and safe handling of patients with COVID-19. Methodology Two activities were conducted: simulation-based training to all HCWs and a full-scale unannounced simulation-based disaster exercise at King Fahad Medical City (KFMC). The online module was designed to enhance the knowledge on COVID-19. This module was available to all KFMC staff. The five hands-on practical part of the course was available to frontliner HCWs. The unannounced undercover simulated patients’ full-scale COVID-19 simulation-based disaster exercise took place in the emergency department over 3?hours. Six scenarios were executed to test the existing plan in providing care of suspected COVID-19 cases. Results 2620 HCWs took the online module, 17 courses were conducted and 337 frontliner HCWs were trained. 94% of learners were satisfied and recommended the activity to others. The overall compliance rate of the full-scale COVID-19 disaster drill with infection control guidelines was 90%. Post-drill debriefing sessions recommended reinforcing PPE training, ensuring availability of different sizes of PPEs and developing an algorithm to transfer patients to designated quarantine areas. Conclusion Simulation-based training and preparedness testing activities are vital in identifying gaps to apply corrective actions immediately. In the presence of a highly hazardous contagious disease like COVID-19, such exercises are a necessity to any healthcare institution.
| S-EPMC7316112 | biostudies-literature
Project description:Healthcare systems are urged to build facilities that support safe and efficient delivery of care. Literature demonstrates that the built environment impacts patient safety. Design decisions made early in the planning process may introduce flaws into the system, known as latent safety threats (LSTs). Simulation-based clinical systems testing (SbCST) has successfully been incorporated in the post-construction evaluation process in order to identify LSTs prior to patient exposure and promote preparedness, easing the transition into newly built facilities. As the application of simulation in healthcare extends into the realm of process and systems testing, there is a need for a standardized approach by which to conduct SbCST in order to effectively evaluate newly built healthcare facilities. This paper describes a systemic approach by which to conduct SbCST and provides documentation and evaluation tools in order to develop, implement, and evaluate a newly built environment to identify LSTs and system inefficiencies prior to patient exposure.
| S-EPMC6676572 | biostudies-literature
Project description:Resilience enables supply chains to reduce their proneness to disruptions and recover faster. Many existing strategies to strengthen the resilience of supply chains are facilitated by the use of digital technology. Blockchain, as one of the promising innovative technologies, enables a transparent, secure, and timely data exchange and automation via smart contracts. In this paper, we discuss the impact of blockchain technology on supply chain risk management and, in particular, on supply chain resilience. We identify potential risk-related blockchain application scenarios and examine their impact on the existing resilience strategies. We explore the impact of the most promising applications with respect to resilience by using an agent-based simulation model of a complex supply network affected by disruptions. The theoretical analysis reveals a promotion of supply chain resilience strategies, especially if smart contracts are used for risk-related collaboration. The simulation study indicates an increase in resilience if the underlying collaboration is based on time-efficient processes: The propagation of disruptions, the network recovery time, and total costs can be substantially reduced. However, depending on the duration of the disruption, negative effects can occur if process efficiency is insufficient. From our investigations, we derive insights for managers who are interested in practical implementation.
| S-EPMC7392885 | biostudies-literature
Project description:There are practically no quantitative tools for understanding how much stress a health care system can absorb before it loses its ability to provide care. We propose to measure the resilience of health care systems with respect to changes in the density of primary care providers. We develop a computational model on a 1-to-1 scale for a countrywide primary care sector based on patient-sharing networks. Nodes represent all primary care providers in a country; links indicate patient flows between them. The removal of providers could cause a cascade of patient displacements, as patients have to find alternative providers. The model is calibrated with nationwide data from Austria that includes almost all primary care contacts over 2 y. We assign 2 properties to every provider: the "CareRank" measures the average number of displacements caused by a provider's removal (systemic risk) as well as the fraction of patients a provider can absorb when others default (systemic benefit). Below a critical number of providers, large-scale cascades of patient displacements occur, and no more providers can be found in a given region. We quantify regional resilience as the maximum fraction of providers that can be removed before cascading events prevent coverage for all patients within a district. We find considerable regional heterogeneity in the critical transition point from resilient to nonresilient behavior. We demonstrate that health care resilience cannot be quantified by physician density alone but must take into account how networked systems respond and restructure in response to shocks. The approach can identify systemically relevant providers.
| S-EPMC6883827 | biostudies-literature
Project description:Healthcare resources have been strained to previously unforeseeable limits as a result of the COVID-19 pandemic of 2020. This has prompted the emergence of critical just-in-time COVID-19 education, including rapid simulation preparedness, evaluation and training across all healthcare sectors. Simulation has been proven to be pivotal for both healthcare provider learning and systems integration in the context of testing and integrating new processes, workflows, and rapid changes to practice (e.g., new cognitive aids, checklists, protocols) and changes to the delivery of clinical care. The individual, team, and systems learnings generated from proactive simulation training is occurring at unprecedented volume and speed in our healthcare system. Establishing a clear process to collect and report simulation outcomes has never been more important for staff and patient safety to reduce preventable harm. Our provincial simulation program in the province of Alberta, Canada (population = 4.37 million; geographic area = 661,848 km2), has rapidly responded to this need by leading the intake, design, development, planning, and co-facilitation of over 400 acute care simulations across our province in both urban and rural Emergency Departments, Intensive Care Units, Operating Rooms, Labor and Delivery Units, Urgent Care Centers, Diagnostic Imaging and In-patient Units over a 5-week period to an estimated 30,000 learners of real frontline team members. Unfortunately, the speed at which the COVID-19 pandemic has emerged in Canada may prevent healthcare sectors in both urban and rural settings to have an opportunity for healthcare teams to participate in just-in-time in situ simulation-based learning prior to a potential surge of COVID-19 patients. Our coordinated approach and infrastructure have enabled organizational learnings and the ability to theme and categorize a mass volume of simulation outcome data, primarily from acute care settings to help all sectors further anticipate and plan. The goal of this paper is to share the unique features and advantages of using a centralized provincial simulation response team, preparedness using learning and systems integration methods, and to share the highest risk and highest frequency outcomes from analyzing a mass volume of COVID-19 simulation data across the largest health authority in Canada.
| S-EPMC7432586 | biostudies-literature
Project description:In recent years, there has been significant interest in developing system dynamics simulation models to analyze complex healthcare problems. However, there is a lack of studies seeking to summarize the available papers in healthcare and present evidence on the effectiveness of system dynamics simulation in this area. The present paper draws on a systematic selection of published literature from 2000 to 2019, in order to form a comprehensive view of current applications of system dynamics methodology that address complex healthcare issues. The results indicate that the application of system dynamics has attracted significant attention from healthcare researchers since 2013. To date, articles on system dynamics have focused on a variety of healthcare topics. The most popular research areas among the reviewed papers included the topics of patient flow, obesity, workforce demand, and HIV/AIDS. Finally, the quality of the included papers was assessed based on a proposed ranking system, and ways to improve the system dynamics models' quality were discussed.
| S-EPMC7460395 | biostudies-literature
Project description:IntroductionCoronavirus Disease-2019 presents risk to both patients and medical teams. Staff-intensive, complex procedures such as extracorporeal membrane oxygenation (ECMO) or extracorporeal cardiopulmonary resuscitation (eCPR) may increase chances of exposure and spread. This investigation aimed to rapidly deploy an in situ Simulation-based Clinical Systems Testing (SbCST) framework to identify Latent Safety Threats (LSTs) related to ECMO/eCPR initiation during a pandemic.MethodsThe adapted SbCST framework tested systems related to ECMO/eCPR initiation in the Neonatal and Pediatric Intensive Care Units. Systems were evaluated in six domains (Resources, Processes/Systems, Facilities, Clinical Performance, Infection Control, and Communication). We conducted three high-fidelity simulations with members from the Neonatal Intensive Care Unit General Surgery, Pediatric Intensive Care Unit Cardiovascular Surgery (CV), and Pediatric Intensive Care Unit General Surgery teams. Content experts evaluated systems issues during simulation, and LSTs were identified during debriefing. Data were analyzed for frequency of LSTs and trends in process gaps.ResultsSixty-six LSTs were identified across three scenarios. Resource issues comprised the largest category (26%), followed by Process/System issues (24%), Infection Control issues (24%), Communication issues (17%), and Facility and Clinical Performance issues (5% each). LSTs informed new team strategies such as the use of a "door/PPE monitor" and "inside/outside" team configuration.ConclusionsThe adapted SbCST framework identified multiple LSTs related to ECMO/eCPR cannulation and infection control guidelines in the setting of Coronavirus Disease-2019. Through SbCSTs, we developed guidelines to conserve PPE and develop optimal workflows to reduce patient/staff exposure in a high-risk procedure. This project may guide other hospitals to adapt SbCSTs strategies to test/adjust rapidly changing guidelines.
| S-EPMC8782104 | biostudies-literature
Project description:BACKGROUND:In recent years, the study of immune response behaviour using bottom up approach, Agent Based Modeling (ABM), has attracted considerable efforts. The ABM approach is a very common technique in the biological domain due to high demand for a large scale analysis tools for the collection and interpretation of information to solve biological problems. Simulating massive multi-agent systems (i.e. simulations containing a large number of agents/entities) requires major computational effort which is only achievable through the use of parallel computing approaches. RESULTS:This paper explores different approaches to parallelising the key component of biological and immune system models within an ABM model: pairwise interactions. The focus of this paper is on the performance and algorithmic design choices of cell interactions in continuous and discrete space where agents/entities are competing to interact with one another within a parallel environment. CONCLUSIONS:Our performance results demonstrate the applicability of these methods to a broader class of biological systems exhibiting typical cell to cell interactions. The advantage and disadvantage of each implementation is discussed showing each can be used as the basis for developing complete immune system models on parallel hardware.
| S-EPMC6905091 | biostudies-literature
Project description:BackgroundApplication of Artificial Intelligence (AI) and the use of agent-based systems in the healthcare system have attracted various researchers to improve the efficiency and utility in the Electronic Health Records (EHR). Nowadays, one of the most important and creative developments is the integration of AI and Blockchain that is, Distributed Ledger Technology (DLT) to enable better and decentralized governance. Privacy and security is a critical piece in EHR implementation and/or adoption. Health records are updated every time a patient visits a doctor as they contain important information about the health and wellbeing of the patient and describes the history of care received during the past and to date. Therefore, such records are critical to research, hospitals, emergency rooms, healthcare laboratories, and even health insurance providers.MethodsIn this article, a platform employing the AI and the use of multi-agent based systems along with the DLT technology for privacy preservation is proposed. The emphasis of security and privacy is highlighted during the process of collecting, managing and distributing EHR data.ResultsThis article aims to ensure privacy, integrity and security metrics of the electronic health records are met when such copies are not only immutable but also distributed. The findings of this work will help guide the development of further techniques using the combination of AI and multi-agent based systems backed by DLT technology for secure and effective handling EHR data. This proposed architecture uses various AI-based intelligent based agents and blockchain for providing privacy and security in EHR. Future enhancement in this work can be the addition of the biometric based systems for improved security.
| S-EPMC7924429 | biostudies-literature
Project description:An appealing tool for study of the complex biological behaviors that can emerge from networks of simple molecular interactions is an agent-based, computational simulation that explicitly tracks small-scale local interactions--following thousands to millions of states through time. For many critical cell processes (e.g. cytokinetic furrow specification, nuclear centration, cytokinesis), the flexible nature of cytoskeletal filaments is likely to be critical. Any computer model that hopes to explain the complex emergent behaviors in these processes therefore needs to encode filament flexibility in a realistic manner. Here I present a numerically convenient and biophysically realistic method for modeling cytoskeletal filament flexibility in silico. Each cytoskeletal filament is represented by a series of rigid segments linked end-to-end in series with a variable attachment point for the translational elastic element. This connection scheme allows an empirically tuning, for a wide range of segment sizes, viscosities, and time-steps, that endows any filament species with the experimentally observed (or theoretically expected) static force deflection, relaxation time-constant, and thermal writhing motions. I additionally employ a unique pair of elastic elements--one representing the axial and the other the bending rigidity- that formulate the restoring force in terms of single time-step constraint resolution. This method is highly local -adjacent rigid segments of a filament only interact with one another through constraint forces-and is thus well-suited to simulations in which arbitrary additional forces (e.g. those representing interactions of a filament with other bodies or cross-links / entanglements between filaments) may be present. Implementation in code is straightforward; Java source code is available at www.celldynamics.org.
| S-EPMC2654463 | biostudies-literature