Human factors in anaesthesia: a narrative review

Reducing the time to activation of the emergency call system in operating theatres: effect of installing vertical red line indicators

BACKGROUND

The 7th National Audit Project of the Royal College of Anaesthetists (NAP7) recommended that an emergency call system be immediately accessible in all anaesthesia locations. It is essential that all theatre team members can rapidly call for help to reduce the risk of patient harm. However, the ability of staff to activate this system in a timely manner can be affected by cluttered or unfamiliar environments and cognitive overload. One proposed strategy to enable rapid identification and activation of emergency call systems is to install a red vertical painted stripe on the wall from the ceiling to the activation button. We investigated the effect of introducing this vertical red line on activation times in operating theatres in the UK and Australia.

METHODS

Operating theatre team members, including anaesthetists, surgeons, anaesthetic nurses, surgical and theatre nurses, operating theatre practitioners, and technicians, were approached without prior warning and asked to simulate activation of an emergency call. Vertical red lines were installed, and data collection repeated in the same operating theatres 4-12 months later.

RESULTS

After installation of vertical red lines, the proportion of activations taking >10 s decreased from 31.9% (30/94) to 13.6% (17/125, P=0.001), and >20 s decreased from 19.1% (18/94) to 4.8% (6/125, P<0.001). The longest duration pre-installation was 120 s, and post-installation 35 s.

CONCLUSIONS

This simple, safe, and inexpensive design intervention should be considered as a design standard in all operating theatres to minimise delays in calling for help.

Application of a Human Factors Systems Approach to Healthcare Control Centres for Managing Patient Flow: A Scoping Review

Over the past decade, healthcare systems have started to establish control centres to manage patient flow, with a view to removing delays and increasing the quality of care. Such centres-here dubbed Healthcare Capacity Command/Coordination Centres (HCCCs)-are a challenge to design and operate. Broad-ranging surveys of HCCCs have been lacking, and design for their human users is only starting to be addressed. In this review we identified 73 papers describing different kinds of HCCCs, classifying them according to whether they describe virtual or physical control centres, the kinds of situations they handle, and the different levels of Rasmussen's [1] risk management framework that they integrate. Most of the papers (71%) describe physical HCCCs established as control centres, whereas 29% of the papers describe virtual HCCCs staffed by stakeholders in separate locations. Principal functions of the HCCCs described are categorised as business as usual (BAU) (48%), surge management (15%), emergency response (18%), and mass casualty management (19%). The organisation layers that the HCCCs incorporate are classified according to the risk management framework; HCCCs managing BAU involve lower levels of the framework, whereas HCCCs handling the more emergent functions involve all levels. Major challenges confronting HCCCs include the dissemination of information about healthcare system status, and the management of perspectives and goals from different parts of the healthcare system. HCCCs that take the form of physical control centres are just starting to be analysed using human factors principles that will make staff more effective and productive at managing patient flow.

Validation of a natural language processing algorithm using national reporting data to improve identification of anesthesia-related ADVerse evENTs: The "ADVENTURE" study

BACKGROUND

Reporting and analysis of adverse events (AE) is associated with improved health system learning, quality outcomes, and patient safety. Manual text analysis is time-consuming, costly, and prone to human errors. We aimed to demonstrate the feasibility of novel machine learning and natural language processing (NLP) approaches for early predictions of adverse events and provide input to direct quality improvement and patient safety initiatives.

METHODS

We used machine learning to analyze 9559 continuously reported AE by clinicians and healthcare systems to the French National Health accreditor (HAS) between January 1, 2009, and December 31, 2020 . We validated the labeling of 135,000 unique de-identified AE reports and determined the associations between different system's root causes and patient consequences. The model was validated by independent expert anesthesiologists.

RESULTS

The machine learning (ML) and Artificial Intelligence (AI) model trained on 9559 AE datasets accurately categorized 8800 (88%) of reported AE. The three most frequent AE types were "difficult orotracheal intubation" (16.9% of AE reports), "medication error" (10.5%), and "post-induction hypotension" (6.9%). The accuracy of the AI model reached 70.9% sensitivity, 96.6% specificity for "difficult intubation", 43.2% sensitivity, and 98.9% specificity for "medication error."

CONCLUSIONS

This unsupervised ML method provides an accurate, automated, AI-supported search algorithm that ranks and helps to understand complex risk patterns and has greater speed, precision, and clarity when compared to manual human data extraction. Machine learning and Natural language processing (NLP) models can effectively be used to process natural language AE reports and augment expert clinician input. This model can support clinical applications and methodological standards and used to better inform and enhance decision-making for improved risk management and patient safety.

TRIAL REGISTRATION

The study was approved by the ethics committee of the French Society of Anesthesiology (IRB 00010254-2020-20) and the CNIL (CNIL: 118 58 95) and the study was registered with ClinicalTrials.gov (NCT: NCT05185479).

Assessing Healthcare Simulation Facilitation: A Scoping Review of Available Tools, Validity Evidence, and Context Suitability for Faculty Development in Low-Resource Settings

SUMMARY STATEMENT

Assessment tools support simulation facilitation skill development by guiding practice, structuring feedback, and promoting reflective learning among educators. This scoping review followed a systematic process to identify facilitation assessment tools used in postlicensure healthcare simulation. Secondary objectives included mapping of the validity evidence to support their use and a critical appraisal of their suitability for simulation faculty development in low-resource settings. Database searching, gray literature searching, and stakeholder engagement identified 11,568 sources for screening, of which 72 met criteria for full text review. Thirty sources met inclusion; 16 unique tools were identified. Tools exclusively originated from simulation practice in high-resource settings and predominantly focused on debriefing. Many tools have limited validity evidence supporting their use. In particular, the validity evidence supporting the extrapolation and implications of assessment is lacking. No current tool has high context suitability for use in low-resource settings.

Machine learning approach for predicting post-intubation hemodynamic instability (PIHI) index values: towards enhanced perioperative anesthesia quality and safety

BACKGROUND

Adequate preoperative evaluation of the post-intubation hemodynamic instability (PIHI) is crucial for accurate risk assessment and efficient anesthesia management. However, the incorporation of this evaluation within a predictive framework have been insufficiently addressed and executed. This study aims to developed a machine learning approach for preoperatively and precisely predicting the PIHI index values.

METHODS

In this retrospective study, the valid features were collected from 23,305 adult surgical patients at Peking Union Medical College Hospital between 2012 and 2020. Three hemodynamic response sequences including systolic pressure, diastolic pressure and heart rate, were utilized to design the post-intubation hemodynamic instability (PIHI) index by computing the integrated coefficient of variation (ICV) values. Different types of machine learning models were constructed to predict the ICV values, leveraging preoperative patient information and initiatory drug infusion. The models were trained and cross-validated based on balanced data using the SMOTETomek technique, and their performance was evaluated according to the mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and R-squared index (R2).

RESULTS

The ICV values were proved to be consistent with the anesthetists' ratings with Spearman correlation coefficient of 0.877 (P < 0.001), affirming its capability to effectively capture the PIHI variations. The extra tree regression model outperformed the other models in predicting the ICV values with the smallest MAE (0.0512, 95% CI: 0.0511-0.0513), RMSE (0.0792, 95% CI: 0.0790-0.0794), and MAPE (0.2086, 95% CI: 0.2077-0.2095) and the largest R2 (0.9047, 95% CI: 0.9043-0.9052). It was found that the features of age and preoperative hemodynamic status were the most important features for accurately predicting the ICV values.

CONCLUSIONS

Our results demonstrate the potential of the machine learning approach in predicting PIHI index values, thereby preoperatively informing anesthetists the possible anesthetic risk and enabling the implementation of individualized and precise anesthesia interventions.

Spanish Society of Anesthesiology, Reanimation and Pain Therapy (SEDAR), Spanish Society of Emergency and Emergency Medicine (SEMES) and Spanish Society of Otolaryngology, Head and Neck Surgery (SEORL-CCC) Guideline for difficult airway management. Part II

The Airway Management section of the Spanish Society of Anesthesiology, Resuscitation, and Pain Therapy (SEDAR), the Spanish Society of Emergency Medicine (SEMES), and the Spanish Society of Otorhinolaryngology and Head and Neck Surgery (SEORL-CCC) present the Guide for the comprehensive management of difficult airway in adult patients. Its principles are focused on the human factors, cognitive processes for decision-making in critical situations, and optimization in the progression of strategies application to preserve adequate alveolar oxygenation in order to enhance safety and the quality of care. The document provides evidence-based recommendations, theoretical-educational tools, and implementation tools, mainly cognitive aids, applicable to airway management in the fields of anesthesiology, critical care, emergencies, and prehospital medicine. For this purpose, an extensive literature search was conducted following PRISMA-R guidelines and was analyzed using the GRADE methodology. Recommendations were formulated according to the GRADE methodology. Recommendations for sections with low-quality evidence were based on expert opinion through consensus reached via a Delphi questionnaire.

Airway management of adult epiglottitis: a systematic review and meta-analysis

Background

Adult epiglottitis is a life-threatening airway emergency where airway protection is the immediate priority. Despite its importance, the optimal approach to airway management remains unclear. We performed a systematic review of the airway management for adult epiglottitis, including meta-analysis of trends over time.

Methods

We systematically searched PubMed, Ovid MEDLINE®, and Embase® for adult epiglottitis studies that described the airway management between 1980 and 2020. The primary outcome was the prevalence of airway intervention. Secondary outcomes were prevalence of tracheal intubation, tracheostomy, and failed intubation. A random-effects model meta-analysis was performed with subgroups defined by decade of study publication. Cases that described the specific method of airway intervention and severity of epiglottitis were included in a separate technique summary.

Results

Fifty-six studies with 10 630 patients were included in the meta-analysis. The overall rate of airway intervention was 15.6% (95% confidence interval [CI] 12.9-18.8%) but the rate decreased from 20% to 10% between 1980 and 2020. The overall rate of tracheal intubation was 10.2% (95% CI 7.1-13.6%) and that of failed intubation was 4.2% (95% CI 1.4-8.0%). The airway technique summary included 128 cases, of which 75 (58.6%) were performed awake and 53 (41.4%) involved general anaesthesia. We identified 32 cases of primary technique failure.

Conclusion

The rate of airway intervention for adult epiglottitis has decreased over four decades to a current level of 10%. Tracheal intubation is a high-risk scenario with a 1 in 25 failure rate. Specific technique selection is most likely influenced by contextual factors including the severity of epiglottitis.

Peri-operative night-time work of anaesthesiologists: A qualitative study of critical issues and proposals

BACKGROUND

Qualitative data on the opinions of anaesthesiologists regarding the impact of peri-operative night-time working conditions on patient safety are lacking.

OBJECTIVES

This study aimed to achieve in-depth understanding of anaesthesiologists' perceptions regarding the impact of night-time working conditions on peri-operative patient safety and actions that may be undertaken to mitigate perceived risks.

DESIGN

Qualitative analysis of responses to two open-ended questions.

SETTING

Online platform questionnaire promoted by the European Society of Anaesthesiology and Intensive Care (ESAIC).

PARTICIPANTS

The survey sample consisted of an international cohort of anaesthesiologists.

MAIN OUTCOME MEASURES

We identified and classified recurrent themes in the responses to questions addressing perceptions regarding (Q1) peri-operative night-time working conditions, which may affect patient safety and (Q2) potential solutions.

RESULTS

We analysed 2112 and 2113 responses to Q1 and Q2, respectively. The most frequently reported themes in relation to Q1 were a perceived reduction in professional performance accompanied by concerns regarding the possible consequences of work with fatigue (27%), and poor working conditions at night-time (35%). The most frequently proposed solutions in response to Q2 were a reduction of working hours and avoidance of 24-h shifts (21%), an increase in human resources (14%) and performance of only urgent or emergency surgeries at night (14%).

CONCLUSION

Overall, the surveyed anaesthesiologists believe that workload-to-staff imbalance and excessive working hours were potential bases for increased peri-operative risk for their patients, partly because of fatigue-related medical errors during night-time work. The performance of nonemergency elective surgical cases at night and lack of facilities were among the reported issues and potential targets for improvement measures. Further studies should investigate whether countermeasures can improve patient safety as well as the quality of life of anaesthesia professionals. Regulations to improve homogeneity, safety, and quality of anaesthesia practice at night seem to be urgently needed.

Airway Management: The Current Role of Videolaryngoscopy

Airway management is usually an uncomplicated and safe intervention; however, when problems arise with the primary airway technique, the clinical situation can rapidly deteriorate, resulting in significant patient harm. Videolaryngoscopy has been shown to improve patient outcomes when compared with direct laryngoscopy, including improved first-pass success at tracheal intubation, reduced difficult laryngeal views, reduced oxygen desaturation, reduced airway trauma, and improved recognition of oesophageal intubation. The shared view that videolaryngoscopy affords may also facilitate superior teaching, training, and multidisciplinary team performance. As such, its recommended role in airway management has evolved from occasional use as a rescue device (when direct laryngoscopy fails) to a first-intention technique that should be incorporated into routine clinical practice, and this is reflected in recently updated guidelines from a number of international airway societies. However, currently, overall videolaryngoscopy usage is not commensurate with its now widespread availability. A number of factors exist that may be preventing its full adoption, including perceived financial costs, inadequacy of education and training, challenges in achieving deliverable decontamination processes, concerns over sustainability, fears over "de-skilling" at direct laryngoscopy, and perceived limitations of videolaryngoscopes. This article reviews the most up-to-date evidence supporting videolaryngoscopy, explores its current scope of utilisation (including specialist techniques), the potential barriers preventing its full adoption, and areas for future advancement and research.

Human factors and ergonomics

Human factors and ergonomics in healthcare is an important discipline that considers both the physical and mental characteristics of healthcare workers, as well as the complex interactions within which organisations exist.

Increasing the margin of patient safety for periodontal and implant treatments: The role of human factors

Early complications following periodontal and dental implant surgeries are typically attributed to technique or poor biological response, ignoring the possibility of the human element. Interestingly, significant experience is not correlated with increased success, whereas evidence supports the impact of clinical behavior on patient outcome. This is the result of errors, much like those scrutinized in other high-risk technical fields, such as aviation. What can be surprising is that those who make these errors are very well acquainted with best practices. Given this, how is it possible for the conscientious practitioner to fail to apply protocols that are nonetheless very well known? Recently, the concepts of human and organizational factors have been translated to medicine, though dentistry has been slow to recognize their potential benefit. This review lists specific human factor behaviors, such as use of checklists and crew resource management, which might improve postsurgical outcome.

Implementing human factors in anaesthesia: guidance for clinicians, departments and hospitals: Guidelines from the Difficult Airway Society and the Association of Anaesthetists: Guidelines from the Difficult Airway Society and the Association of Anaesthetists

Human factors is an evidence-based scientific discipline used in safety critical industries to improve safety and worker well-being. The implementation of human factors strategies in anaesthesia has the potential to reduce the reliance on exceptional personal and team performance to provide safe and high-quality patient care. To encourage the adoption of human factors science in anaesthesia, the Difficult Airway Society and the Association of Anaesthetists established a Working Party, including anaesthetists and operating theatre team members with human factors expertise and/or interest, plus a human factors scientist, an industrial psychologist and an experimental psychologist/implementation scientist. A three-stage Delphi process was used to formulate a set of 12 recommendations: these are described using a 'hierarchy of controls' model and classified into design, barriers, mitigations and education and training strategies. Although most anaesthetic knowledge of human factors concerns non-technical skills, such as teamwork and communication, human factors is a broad-based scientific discipline with many other additional aspects that are just as important. Indeed, the human factors strategies most likely to have the greatest impact are those related to the design of safe working environments, equipment and systems. While our recommendations are primarily provided for anaesthetists and the teams they work with, there are likely to be lessons for others working in healthcare beyond the speciality of anaesthesia.

Simulation-based training in obstetric anesthesia: an update

In this update we explore the current applications of simulation in obstetric anesthesia, describe what is known regarding its impacts on care and consider the different settings in which simulation programs are required. We will introduce practical strategies, such as cognitive aids and communication tools, that can be applied in the obstetric setting and share ways in which a program might apply these tools. Finally, we provide a list of common obstetric emergencies essential for a program's curriculum and common teamwork pitfalls to address within a comprehensive obstetric anesthesia simulation program.