SIR/RSNA/CIRSE Joint Medical Simulation Task Force strategic plan: executive summary

A Managerial Approach towards Modeling the Different Strains of the COVID-19 Virus Based on the Spatial GeoCity Model

This study proposes a modification of the GeoCity model previously developed by the authors, detailing the age structure of the population, personal schedule on weekdays and working days, and individual health characteristics of the agents. This made it possible to build a more realistic model of the functioning of the city and its residents. The developed model made it possible to simulate the spread of three types of strain of the COVID-19 virus, and to analyze the adequacy of this model in the case of unhindered spread of the virus among city residents. Calculations based on the proposed model show that SARS-CoV 2 spreads mainly from contacts in workplaces and transport, and schoolchildren and preschool children are the recipients, not the initiators of the epidemic. The simulations showed that fluctuations in the dynamics of various indicators of the spread of SARS-CoV 2 were associated with the difference in the daily schedule on weekdays and weekends. The results of the calculations showed that the daily schedules of people strongly influence the spread of SARS-CoV 2. Under assumptions of the model, the results show that for the more contagious "rapid" strains of SARS-CoV 2 (omicron), immunocompetent people become a significant source of infection. For the less contagious "slow strains" (alpha) of SARS-CoV 2, the most active source of infection is immunocompromised individuals (pregnant women). The more contagious, or "fast" strain of the SARS-CoV 2 virus (omicron), spreads faster in public transport. For less contagious, or "slow" strains of the virus (alpha), the greatest infection occurs due to work and educational contacts.

Quality improvement: understanding the adoption and diffusion of digital technologies related to surgical performance

Purpose

Quantifying the performance level of surgeons with digital virtual reality (VR) simulators can help ensure that quality requirements in healthcare are met. In order to better understand integration amongst quality principles, practices and technologies in the adoption and diffusion of VR simulators, the authors applied a technological innovation system (TIS) framework. The purpose of this study is to understand how the adoption and diffusion of VR surgical simulators in a Swedish healthcare context is influenced by various system factors.

Design/methodology/approach

In this study, single-case holistic design based on innovation system theory was used to analyse the adoption of digital quality technologies related to surgical performance in Swedish hospitals. The case employs a mixed methods approach triangulating data longitudinally from published documents and expert interviews.

Findings

Adoption of digital technologies regarding surgical performance is restricted by system factors relating to inconsistent normative and regulatory requirements for quantified performance criteria to judge surgical expertise. Addressing these systems' weaknesses with evidence-based training programmes can have a significant impact on the further development of the innovation system and can ultimately affect healthcare reliability and quality.

Originality/value

This paper explores quality management (QM) challenges in the context of digital transformation in healthcare. The paper attempts to fill the gap for TIS studies in a healthcare context and highlight the role of innovation function strength along the value chain and in relation to technology cycles to increase the understanding of adoption of digital technologies relating to surgical performance.

Review of Simulation Training in Interventional Radiology

Simulation training has evolved and is now able to offer numerous training opportunities to supplement the practice of and overcome some of the shortcomings of the traditional Master-Apprentice model currently used in medical training. Simulation training provides new opportunities to practice skills used in clinical procedures, crisis management scenarios, and everyday clinical practice in a risk-free environment. Procedural and nonprocedural skills used in interventional radiology can be taught with the use of simulation devices and technologies. This review will inform the reader of which clinical skills can be trained with simulation, the types of commercially available simulators and their educational validity, and the assessment tools used to evaluate simulation training.

Conventional Medical Education and the History of Simulation in Radiology

Simulation is a promising method for improving clinician performance, enhancing team training, increasing patient safety, and preventing errors. Training scenarios to enrich medical student and resident education, and apply toward competency assessment, recertification, and credentialing are important applications of simulation in radiology. This review will describe simulation training for procedural skills, interpretive and noninterpretive skills, team-based training and crisis management, professionalism and communication skills, as well as hybrid and in situ applications of simulation training. A brief overview of current simulation equipment and software and the barriers and strategies for implementation are described. Finally, methods of measuring competency and assessment are described, so that the interested reader can successfully implement simulation training into their practice.

Using simulation for interventional radiology training

Debate on the existence of innate skills has all but evaporated in the light of evidence that it is only the hours spent in deliberate practice that correlate with even the most elite levels of expertise. A range of simple to advanced technologies stands to address some of the many challenges to effective training of 21st century, procedural medicine. Simulation could train and assess behaviours remotely from patients, in complete safety, reducing the risks of inexperienced trainees learning critical tasks in patients while contributing to certification and revalidation. Understanding the strengths and limitations of these devices, determining and improving their effectiveness and identifying their roles, as well as those of individuals and teams, represents a cornerstone of successful adoption into the interventional radiology curriculum. This requires a simulation strategy that includes standards for simulator documentation.

Impact of simulation-based learning on medication error rates in critically ill patients

PURPOSE

To compare medication administration error rates before and after the provision of educational sessions using either traditional didactic lecture or simulation-based training.

METHODS

A single-center, parallel, controlled, prospective study conducted in adult coronary critical care (CCU) and medical intensive care units (MICU). Twenty-four nurses were observed administering medications. Documentation included drug name, dose, route, time and technique during observation and active medication orders in the patient's chart. A direct observation method was completed at baseline and repeated twice after the interventions. Data obtained during observation were analyzed for medication administration error rates. Interventions were two types of educational sessions with content developed from baseline medication administration error data: simulation-based training for CCU nurses and a didactic lecture for MICU nurses. Quizzes completed before and after the interventions were used to assess knowledge.

RESULTS

A total of 880 doses (402 CCU, 478 MICU) were observed. After the simulation-based educational intervention in the CCU, medication administration error rates decreased from 30.8 to 4.0% (p < 0.001) in the initial post-intervention observation and were sustained in the final post-intervention observation (30.8 to 6.2%; p < 0.001). The error rate in the MICU after the didactic lecture intervention was not significantly different from the baseline and increased in the final post-intervention observation from 20.8 to 36.7% (p = 0.002). Mean quiz scores were significantly improved after education sessions in both ICUs.

CONCLUSIONS

Simulation-based learning provides a significant advantage to patient care through the reduction of medication administration errors compared to lecture style education.