Perioperative Non-opioid Modalities (PNOM) Questionnaire Implementation and Validation of its Psychometric Properties

Introduction: The opioid epidemic and prescription opioid related deaths continue to grow in the US. Contributing factors to this epidemic include nonrestrictive opioid administration in the perioperative period. Though solutions to the epidemic has been discussed by anesthesia providers, there is no tool to quantiatively evaluate of their beliefs and practices for the perioperative management of pain. Over the past three years, there has been advancement in the science of nurse anesthesia on this front and in 2020 Tucker and Wong completed the development of a quantitative tool aimed at assessing CRNA beliefs and practices with regard to their use of non-opioid modalities for the treatment of perioperative pain, the Perioperative Nonopioid Modalities (PNOM) Questionnaire. Our study administered their innovative tool to a large sample of CRNAs practicing in the US and to performed factor analysis for further validation. Materials and Methods: This study utilized a cross-sectional research design, distributing the PNOM Questionnaire electronically to CRNAs practicing the US. Data was evaluated with exploratory factor analysis (EFA) as well descriptive statistics to evaluate current CRNA practice. Results: After adequate factorablility was established, EFA was conducted and revealed 10 factors or themes. Five factors were deemed reliable with cronbach’s alpha ≥ .70. Three important factors had questionable reliability with cronbach’s alpha ≥ .6, yet \u3c .70 and are discussed. Overall, practicing CRNAs are favorable and knowledgeable regarding the use of non-opioid modalities for the treatment of perioperative pain, however they do demonstrate room for further and/or continued education. The results also demonstrated time constraints and organizational support as limiting factors for the use of non-opioid modalities perioperatively. Conclusion: The underlying constructs impacting beliefs and behaviors of opioid vs. non-opioid administration among CRNAs have significant impacts on non-opioid policy, practice and education

The Effect of High-Fidelity Manikin-Based Human Patient Simulation on Educational Outcomes in Advanced Cardiovascular Life Support Courses

The use of high-fidelity manikin-based simulation has been studied in many healthcare education areas. However, the use of this education technology in the American Heart Association Advanced Cardiovascular Life Support (ACLS) course has not been well examined in the literature, despite this education program being one of the most widely taught standardized medical courses in the United States. This study examined high fidelity manikin-based simulation versus low-fidelity manikin-based simulation in the context of an actual ACLS course. Four outcomes were measured: learning outcomes as judged by an expert rater panel reviewing videos of subjects performing a simulated cardiac arrest event immediately after the conclusion of the course, and three self-reported measures examining confidence with the course material, motivation, and affect. A convenience sample of 34 subjects self assigned to one of two ACLS classes. One class utilized high-fidelity simulation (n=16) while the other used low-fidelity simulation (n=18). While the high-fidelity simulation group had a higher composite score for the video review (M= 220.88 vs. M=193.67), this did not reach a level of significance (p=.122). On item level analysis of the scoring, 7 of 14 items reached levels of significance (p \u3c .05). Although all items reported higher mean scores for the highfidelity simulation group, items that focused on manual tasks or actions in the first one to two minutes of the cardiac arrest event were more likely to be non-significant. Items that focused on actions that occurred later in the event or were expert rater assessments of team leader confidence and knowledge were more likely to be found significant. There was no statistical significance found in any of the self-reported measures examining confidence (p = .850), motivation (p = .899), and affect (p = .215)

Simulation in Nursing: Historical Analysis and Theoretical Modeling in Support of a Targeted Clinical Training Intervention

The use of simulation is widespread in healthcare education, and the potential impact of its use large. This is especially true for nursing education as we look to address problems with obtaining clinical experiences, develop critical thinking skills and create methods to measure the impact of simulation interventions. There is substantial empirical evidence in support of predictive relationships between simulation training interventions and knowledge acquisition. This has been extensively demonstrated with the use of a variety of simulation training modalities from standardized patients to human patient simulators. However, data to support changes in clinical practice and improved patient outcomes are quite limited, including attempts to measure the impact of simulation education on retention and transference of knowledge and skill for more complex healthcare process. Additionally, literature searches reveal that only a handful of authors have engaged in the types of foundational work that any emerging science needs. For example, while pieces of the simulation process have been examined in detail, few have attempted to describe what the process of simulation entails at a macro level. Within the past few years some researchers have begun to ask whether there is a causal or predictive relationship present, but few have explored what these associations may look like structurally and what the evidence for them is. The overall objectives of this current research were to: 1) perform an historical review of simulation in healthcare; 2) use this review to outline a new theoretical model of healthcare simulation; and, 3) conduct a small-scale study aimed at pilot-testing and describing part of that model. Hierarchical Task Analysis (HTA) was used to derive an optimum task set for the standard induction of general anesthesia (OTS-SIGA). New Student Registered Nurse Anesthetists (SRNAs) were trained to this task set, and their adherence to the process steps in the clinical setting was then assessed. We also attempted to measure whether repeating the HTA-derived OTS-SIGA simulation training would have an impact on knowledge and transference of simulation-developed skills to the clinical environment. These measures necessitated the development of associated data collection tools and processes for rater training

Autonomous systems in anesthesia : where do we stand in 2020? A narrative review

As most of us are aware, almost every facet of our society is becoming, for better or worse, progressively more technology-dependent. Technological advancement has made autonomous systems, also known as robots, an integral part of our life in several fields, including medicine. The application of robots in anesthesia could be classified into 3 types of robots. The first ones are pharmacological robots. These robots are based on closed-loop systems that allow better-individualized anesthetic drug titration for optimal homeostasis during general anesthesia and sedation. Recent evidence also demonstrates that autonomous systems could control hemodynamic parameters proficiently outperforming manual control in the operating room. The second type of robot is mechanical. They enable automated motorized reproduction of tasks requiring high manual dexterity level. Such robots have been advocated to be more accurate than humans and, thus, could be safer for the patient. The third type is a cognitive robot also known as decision support system. This type of robot is able to recognize crucial clinical situation that requires human intervention. When these events occur, the system notifies the attending clinician, describes relevant related clinical observations, proposes pertinent therapeutic options and, when allowed by the attending clinician, may even administer treatment. It seems that cognitive robots could increase patients' safety. Robots in anesthesia offer not only the possibility to free the attending clinicians from repetitive tasks but can also reduce mental workload allowing them to focus on tasks that require human intelligence such as analytical and clinical approach, lifesaving decision-making capacity, and interpersonal interaction. Nevertheless, further studies have yet to be done to test the combination of these 3 types of robots to maintain simultaneously the homeostasis of multiple biological variables and to test the safety of such combination on a large-scale population

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

TeamSTEPPS Training and Vital Signs Chart to Improve Situational Monitoring for Clinical Deterioration

Failure to monitor early warning signs of patient deterioration can result in cardiopulmonary arrests and patient death. Implementation of team building programs emphasizing vital sign data, with consistent monitoring and trending have demonstrated positive outcomes in multiple health care environments. This project implemented TeamSTEPPS© education for 23 registered nurse (RN) residents in an acute care medical center. Specific aims included: (a) increased knowledge of team communication techniques; (b) improved attitudes towards vital sign monitoring, especially respiratory rate assessment; and (c) improved attitudes towards early rapid response system activation. The education program included support tools, behavioral-modeling, simulation exercises based on de-identified patient data and debriefing. Paired t-tests evaluated the impact of the intervention on total TeamSTEPPS Teamwork Attitudes Questionnaire (T-TAQ) and V-Scale scores. There were statistically significant increases in T-TAQ and V-Scale scores post intervention (1.78 p =.04 and 1.87 p = .04 respectively). Eta square calculation indicated a large effect size for T-TAQ and V-Scale measures. The TeamSTEPPS simulation-enhanced curriculum was successful in improving RN residents’ attitudes toward teamwork, and vital signs monitoring and surveillance practices

DEVELOPMENT OF AN OPTIMAL PATIENT TRANSFER TASK SET AND SIMULATION-BASED INTERVENTION TO REDUCE MUSCULOSKELETAL INJURY IN HEALTHCARE WORKERS

Introduction: Occupational injury is recognized as a key attrition factor in nursing with musculoskeletal injury the most common cause. Nurses, nurse aides and orderly injury rates are consistently listed in the top ten US occupations in terms of total numbers of injuries with patient transfer a primary etiologic factor. Patient transfer education for trainees as well as employees remains inconsistent and non-standardized. Legislative and policy efforts have not been effective.Methods: Two methods are combined in this paper to approach the problem: hierarchical task analysis and a simulation educational intervention. Hierarchical task analysis has been used to solve industrial process problems for more than three decades and simulation education methods have been used in aviation since the 1920's. The hierarchical task analysis process is used to develop an optimal task set which was used to frame and implement a healthcare simulationtraining intervention.Results: Performance evaluation tools for patient transfer were developed based on the optimum task set. Transfer of simulation training outcomes to the clinical setting was demonstrated on pilot study intervention and control units. The program was implemented in a community hospital with sustained improvement in transfer skill and reduction of injury rates and lost work days.Conclusion: Because patient safety and improved outcomes are linked to adequate levels of nurse staffing, the public health implications of this project are significant. If nursing injury can be avoided using these methods then true progress can be made in arresting the injury epidemic with resultant reduction of nursing workforce losses with consequent healthcare system benefits

A Competence-Based Online Learning Video and In-Situ Simulation to Improve Perioperative Anesthesia Nurse Practitioner Self-Efficacy in Responding to Anesthesia Emergencies

Background: Nurse Practitioners (NPs) are broadly educated to the population-based role in which they practice. Further education in subspecialties is essential as more NPs are working autonomously in highly specialized care areas. Problem: In the Department of Anesthesia at a large urban hospital, perioperative anesthesia NPs lack formal training in the subspecialty of anesthesia, which contributed to a lack of self-efficacy when responding to anesthesia emergencies. Methods: An asynchronous multimodal brief instructional video accompanied by an in-situ simulation of an anesthesia emergency was developed to increase knowledge and confidence in perioperative anesthesia nurse practitioner response to anesthesia emergencies. Results: A total of 8 perioperative anesthesia NPs (73% of the staff) participated in the multimodal educational intervention, and 100% of the participants experienced an increase in knowledge to locate emergency anesthesia equipment, along with increased confidence levels in responding to an anesthesia emergency scenario after watching the video and performing the insitu simulation. Conclusion: Deploying a multimodal educational video along with an in-situ simulation was effective in increasing participant’s self-efficacy when responding to an anesthesia emergency, and was found to be feasible. Inadequate educational resources, poor inclusivity of the NPs in the culture of education, and limited time allotted for education were addressed by providing open access of the video on the internet. In-situ simulation reinforced education through a realistic hands-on scenario and provided repetition with the use of Rapid Cycle Deliberate Practice