Human and Organisational Factors as a Risk Management Strategy in an Aviation Maintenance Company

Aviation has for many years been one of the leading industries in addressing human and organisational factors (HOF) within its different sectors – flight operations, air traffic control, ground operations, maintenance, etc. In particular aviation has led the way in mandating a range of measures that address HOF issues – reporting systems, shift-handover procedures, etc (e.g. UK CAA, 2003). A key element of the regulation has been the mandating of initial and continuation training in HOF for virtually all personnel working in aviation maintenance. By contrast the development of practice and regulation of risk and safety management in aviation has lagged behind process (Gambetti et al., 2012) and power (Leva et al., 2012). industries ICAO published its requirements for Safety Management in 2009 (ICAO, 2009) and these are still being translated into regulations by local aviation authorities. For example the EASA regulations require implementation of Safety Management Systems (SMS) in airlines by 2013. As a consequence of this historical sequence – the development of HOF regulations prior to safety management regulations – organisations are facing the challenge of integrating two programmes with related objectives developed to meet the requirements of different regulations. HOF training in aviation maintenance, in the European context, normally comprises of a two day initial training classroom based workshop supplemented by a one-day continuation training workshop every 2 years. The continuation training typically comprises a refresher of key HOF concepts and information about company specific challenges. E-learning and blended learning are sometimes used for continuation training, but their acceptance by the local aviation authorities is variable. This paper reports an initiative to integrate HOF continuation training within a risk management context in an aviation maintenance company

Development of an observational protocol for reducing and mitigating workload and the risk of Retained Foreign Objects

Retained Foreign Objects are an uncommon but costly problem in today's healthcare. It regards the outcome after an unintended item is left behind in a patient after an invasive procedure. This paper presents the development of an observational protocol used for surgical observations in the FOR_RaM Project. The FOR_RaM or Foreign Object Retention - Reduction and Mitigation project aims to analyze and understand the problem of retained foreign objects in surgery and maternity settings in Ireland, develop hospital specific foreign object management processes and implementation roadmaps, with a focus on reducing and mitigating the risk of foreign object retention. This paper discusses the methodology used for developing an observational protocol as part of a socio-technical multi-methods approach in order to gain a better understanding of the existing practices that take place in these settings, including workload, operational processes and collaboration. Emphasis is placed on the observational template development and design, observational tasks, critical points, procedures and protocols followed throughout. This observation protocol has facilitated the collection of critical data and been successful in identifying good practices and potential areas for improvement

The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study

AIM: The SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery. METHODS: This was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4 weeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin. RESULTS: Overall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4 weeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, P = 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, P < 0.001). After adjustment, delay was not associated with a lower rate of complete resection (OR 1.18, 95% CI 0.90-1.55, P = 0.224), which was consistent in elective patients only (OR 0.94, 95% CI 0.69-1.27, P = 0.672). Longer delays were not associated with poorer outcomes. CONCLUSION: One in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease

Effect of remote ischaemic conditioning on clinical outcomes in patients with acute myocardial infarction (CONDI-2/ERIC-PPCI): a single-blind randomised controlled trial.

BACKGROUND: Remote ischaemic conditioning with transient ischaemia and reperfusion applied to the arm has been shown to reduce myocardial infarct size in patients with ST-elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI). We investigated whether remote ischaemic conditioning could reduce the incidence of cardiac death and hospitalisation for heart failure at 12 months. METHODS: We did an international investigator-initiated, prospective, single-blind, randomised controlled trial (CONDI-2/ERIC-PPCI) at 33 centres across the UK, Denmark, Spain, and Serbia. Patients (age >18 years) with suspected STEMI and who were eligible for PPCI were randomly allocated (1:1, stratified by centre with a permuted block method) to receive standard treatment (including a sham simulated remote ischaemic conditioning intervention at UK sites only) or remote ischaemic conditioning treatment (intermittent ischaemia and reperfusion applied to the arm through four cycles of 5-min inflation and 5-min deflation of an automated cuff device) before PPCI. Investigators responsible for data collection and outcome assessment were masked to treatment allocation. The primary combined endpoint was cardiac death or hospitalisation for heart failure at 12 months in the intention-to-treat population. This trial is registered with ClinicalTrials.gov (NCT02342522) and is completed. FINDINGS: Between Nov 6, 2013, and March 31, 2018, 5401 patients were randomly allocated to either the control group (n=2701) or the remote ischaemic conditioning group (n=2700). After exclusion of patients upon hospital arrival or loss to follow-up, 2569 patients in the control group and 2546 in the intervention group were included in the intention-to-treat analysis. At 12 months post-PPCI, the Kaplan-Meier-estimated frequencies of cardiac death or hospitalisation for heart failure (the primary endpoint) were 220 (8·6%) patients in the control group and 239 (9·4%) in the remote ischaemic conditioning group (hazard ratio 1·10 [95% CI 0·91-1·32], p=0·32 for intervention versus control). No important unexpected adverse events or side effects of remote ischaemic conditioning were observed. INTERPRETATION: Remote ischaemic conditioning does not improve clinical outcomes (cardiac death or hospitalisation for heart failure) at 12 months in patients with STEMI undergoing PPCI. FUNDING: British Heart Foundation, University College London Hospitals/University College London Biomedical Research Centre, Danish Innovation Foundation, Novo Nordisk Foundation, TrygFonden

Investigating Human Factors in Biotechnology and Pharmaceutical Manufacturing industries

The aim of this paper is to present the work carried out in a European Commission-funded project to adapt an existing Aircraft Maintenance Human Factors (HF) training program (STAMINA) to the Biotechnology and Pharmaceutical Manufacturing industries (BPM). The STAMINA approach has been successfully built into a global human factors training business recognized internationally as a quality benchmark. The Biotechnology and Pharmaceutical Industries have a similar need for human factors training, but with a less elaborated research and regulatory framework. The goal of the project is to provide training to increase and support operational safety, product quality and process reliability within the BPM industry. This paper presents the findings from the first two stages of the project: (a) literature review and (b) research findings

Appreciative Inquiry, a LightHouse on a Foggy Path: Healthcare Teams Share Success Stories

As a psychologist I believed I had a good understanding of group dynamics and organisational change principles. As my project began, I was optimistic about being able to support multidisciplinary colleagues and act as a catalyst for change. I was to learn some hard lessons about the complexity of ?wicked? healthcare systems (and the limits of my influence) but I also learned about the value of Appreciative Inquiry (AI). Amid a stalled implementation process, widespread disillusionment and change-fatigue, AI proved effective in prompting diverse groups to engage in positively framed narratives. It enabled new teams to ?build bridges? over organisational and discipline boundaries ? towards a shared vision of a desired future

European, Mediterranean & Middle Eastern Conference on Information Systems 2011

The aim of this paper is to present the work carried out in a European Commission-funded project to adapt an existing Aircraft Maintenance Human Factors (HF) training program (STAMINA) to the Biotechnology and Pharmaceutical Manufacturing industries (BPM). The STAMINA approach has been successfully built into a global human factors training business recognized internationally as a quality benchmark. The Biotechnology and Pharmaceutical Industries have a similar need for human factors training, but with a less elaborated research and regulatory framework. The goal of the project is to provide training to increase and support operational safety, product quality and process reliability within the BPM industry. This paper presents the findings from the first two stages of the project: (a) literature review and (b) research findings

A risk governance framework for healthcare decision support systems based on socio-technical analysis

We are developing an Artificial Intelligence (AI) risk governance framework based on human factors and AI governance principles to make automated healthcare decision-support safer and more accountable. Today, the healthcare system is facing a huge overload in reporting, which has made manual processing and comprehensive decision-making impossible. Emerging advances in AI and especially Natural Language Processing seem an attractive answer to human limitations in processing high volumes of reports. However, there are known risks to automation, including the risk in change of deploying AI itself into organisations, emotions, and ethics, which are rarely taken into consideration when making AI-based decisions. To explore this, we will first construct a Decision Support System (DSS) tool based on a knowledge graph extracted from real-world healthcare reports. Then, the tool will be deployed in a controlled manner in a hospital and its operation will be analysed using an established socio-technical methodology developed by the Centre for Innovative Human Systems in Trinity College Dublin over 25 years of research. We will contribute by integrating computer science with organizational psychology and the use of human factors methods to identify the impact of AI-based healthcare DSS, their associated risks, and the ethical and legal challenges. We hypothesize that collaborating with the organisational psychologists to consider the global system of human decision-making and AI-based DSS will help in minimizing the AI-based decision-making risk in a way that ensures fairness, accountability, and transparency. This study will be carried out with our partner hospital, St. James in Dublin

A risk governance framework for healthcare decision support systems based on socio-technical analysis

We are developing an Artificial Intelligence (AI) risk governance framework based on human factors and AI governance principles to make automated healthcare decision-support safer and more accountable. Today, the healthcare system is facing a huge overload in reporting, which has made manual processing and comprehensive decision-making impossible. Emerging advances in AI and especially Natural Language Processing seem an attractive answer to human limitations in processing high volumes of reports. However, there are known risks to automation, including the risk in change of deploying AI itself into organisations, emotions, and ethics, which are rarely taken into consideration when making AI-based decisions. To explore this, we will first construct a Decision Support System (DSS) tool based on a knowledge graph extracted from real-world healthcare reports. Then, the tool will be deployed in a controlled manner in a hospital and its operation will be analysed using an established socio-technical methodology developed by the Centre for Innovative Human Systems in Trinity College Dublin over 25 years of research. We will contribute by integrating computer science with organizational psychology and the use of human factors methods to identify the impact of AI-based healthcare DSS, their associated risks, and the ethical and legal challenges. We hypothesize that collaborating with the organisational psychologists to consider the global system of human decision-making and AI-based DSS will help in minimizing the AI-based decision-making risk in a way that ensures fairness, accountability, and transparency. This study will be carried out with our partner hospital, St. James in Dublin