Human Risk Factors Research Articles

On the Measuring Capabilities of the Human Factor Risk when Designing Restoration Work on Objects of Immovable Cultural Heritage

On the Measuring Capabilities of the Human Factor Risk when Designing Restoration Work on Objects of Immovable Cultural Heritage

On the development and measurement of human factors complex network for maritime accidents: A case of ship groundings

Human factors from organizational aspects to unsafe behaviors of the crew on board form a directed complex network reflecting the evolution of maritime accidents. The present study aims to develop a human factors complex network contributing to maritime accidents. First, a maritime accident scenario is defined based on 104 ship grounding accident reports, then the human factors contributing to ship groundings are identified and structured within HFACS using grounding theory. Second, chi-square tests and odd ratio analysis are employed to capture the interrelationships among these human factors within HFACS, which are then represented as nodes, edges, and edge weights in network. The topological parameters including degree, clustering coefficients, and node importance are measured in the end for the human factors complex network. The results indicate that the factor “Inadequate safety management by the shipping company” belonged to Organizational influences has the highest influence on the factors “Not taken safe ship speeds”, “Improper lashing and stowage”, “Failure to implement the planned route”, and “Failure to keep navigational AIDS in normally open operation’ that belonged to Unsafe acts, while these unsafe acts have the highest PageRank value, affirming their direct role in causing maritime accidents. Results from this study are valuable for understanding of the network propagation mechanism of human factor risks in maritime accidents, and can also offer guidance to shipping companies, maritime authorities, and relevant stakeholders in establishing and optimizing human factor barriers in such accidents.

Dynamic assessment method for human factor risk of manned deep submergence operation system based on SPAR-H and SD

Due to the complex and changeable contextual environment, the human factor risk of the manned deep submergence operating system shows dynamic characteristics. Compared with the traditional static human reliability analysis (HRA) method, dynamic HRA method can better simulate the dynamic characteristics and the nonlinear information feedback mechanism of the operating system. This paper proposed a dynamic risk assessment model based on System Dynamics and SPAR-H. The cognitive load was introduced into the Performance Shaping Factor (PSF) network to make it more suitable for the task and environment of manned deep submergence. In addition, in order to measure the compensation effect of PSF on the work efficiency of oceanauts, eight compensation functions were constructed between PSFs and work efficiency. Finally, key risk tasks and sensitive PSFs were identified. Taking the 12 h manned deep diving mission as an example, the dynamic quantification of human error probability and work efficiency of oceanauts was simulated. The results indicate that the dynamic simulation results of the constructed risk assessment model are consistent with the actual situation, and can effectively predict the changes of dynamic risk.

Analysis on coupling dynamic effect of human errors in aviation safety

Human factors have increasingly been the leading cause of aircraft accidents. In most cases, human factors are not working alone, instead they are coupled with complex environment, mechanical factors, physiological and psychological factors of pilots, and organizational management, all of which form a complex aviation safety system. It is vital to investigate the coupling impact of human errors to avoid the occurrence of aviation accidents. In view that the Human Factors Analysis and Classification System (HFACS) provides a hierarchical classification principle of human errors in aviation accidents, and the System Dynamics (SD) approach is helpful to describe the risk evolution process, this paper establishes a hybrid HFACS-SD model by employing the HFACS and the SD approach to reveal the aviation human factors risk evolution mechanism, in which the HFACS is first used to capture the causal factors of human errors risk, and a coupling SD model is then built to describe the evolution of aviation human factors risk supported by historical data. The eigenvalue elasticity analysis is taken to identify critical loops and parameters that have a substantial impact on the system structural behavior, and the influence of parameters and loops is assessed. Simulation results show that the evolution trend of the accident rate can be replicated by the proposed HFACS-SD model, and the structural dominance analysis can efficiently identify critical loops and parameters. Simulation results further show that, with the recommended safety enhancement measures, the stability of the aviation system is increased, and thus lowering the overall accident rate.

A consensus model-based risk matrix for human error factors risk analysis in medical devices by considering risk acceptability

Medical devices related event caused by potential human error factors has been one of the leading life-threatening factors. These factors may emerge in multi-hierarchy because of the multi-dimensional organizational structure in the healthcare system. The Human Factor Analysis and Classification System (HFACS), a systematic human error analysis tool, can be integrated with the risk matrix to identify human error factors and prioritize risk in medical devices. Meanwhile, integrating the consensus model into the risk matrix can yield highly recognized risk analysis results. Nevertheless, most consensus models did not consider the heterogenous risk acceptability of experts. Therefore, this paper first proposed a consensus model-based risk matrix to address the HFACS-based human error factor risk analysis problem considering the experts’ risk acceptability. In the proposed method, a novel feedback mechanism considering risk acceptability is constructed to guide experts to modify their risk evaluation information and reach a consensus, where experts' risk acceptability model is established to reflect their willingness to adjust risk evaluation information. Especially, a novel consensus measurement is constructed based on the risk rating results yielded by the risk matrix. Finally, a case of risk analysis of human error factors of ventilators is studied to validate the proposed method.

The integrated prospect theory with consensus model for risk analysis of human error factors in the clinical use of medical devices

The risk analysis of human error factors is one of the most significant procedures for preventing and reducing risk in the clinical use of medical devices. Human Factor Analysis and Classification System (HFACS) framework, a systematic human error analysis tool, is widely used for human error factors risk analysis. However, the traditional HFACS framework is insufficient to deal with the scenario with complex and uncertain risk information and conflicting opinions among experts caused by their heterogeneous risk preferences and diverse knowledge. To address these limitations, this paper integrated the prospect theory with the consensus model under Interval Type-2 Fuzzy Sets (IT2FSs) environment for addressing the HFACS-based human error factors risk analysis problem. This integrated method enabled the HFACS to yield highly acceptable risk analysis results considering experts’ heterogeneous risk preferences. Specifically, the IT2FSs are utilized to represent highly complex and uncertain risk assessment information of human error factors. Secondly, the prospect theory is applied to model the heterogeneous risk preferences of experts and eliminate their impact on risk evaluation results. After obtaining the risk evaluation matrix by prospect theory, the consensual risk evaluation matrix is yielded by a consensus model that can balance the group aim of reaching a consensus and the individual aim of keeping original risk evaluation information as much as possible. Then, the risk ranking of human error factors is determined based on the distance of IT2FSs. Finally, a case study of the clinical use of ventilators, including sensitivity analysis and comparative analysis, is presented to illustrate the efficiency of the proposed method.

Формирование ответственности в структуре надежности студентов – будущих сотрудников Государственной противопожарной службы МЧС

We consider the current issue of responsibility formation in the structure of personal reliability as a personal quality of students – future employees of State Fire Service of EMERCOM of Russia. The “responsibility” is considered as a necessary component of personal reliability, which underlies the professionally important and personal qualities of an employee of State Fire Service of EMERCOM of Russia. In modern social and economic conditions, when society’s need for reliable specialists increases, the issue of responsibility formation remains highly relevant. Russian society is achieving ever higher achievements in the fields of technology, information, production, etc., which ensure labor efficiency and simplify the lives of citizens. However, at the same time, human factor risks increases. The article discusses in detail the research on the problem of responsibility formation in the structure of personal reliability of law enforcement specialists in the works of domestic and foreign scientists. The close connection between the success of the future specialist and his reliability is revealed. A qualitative solution to the problem of psychological support in the formation of responsibility allows to effectively carry out the necessary measures for the assessment and training of students. The main goal facing a modern specialist is to properly perform the duties assigned to him, to be reliable, therefore, the development and construction of an optimal process for the formation of responsibility is a strategic task of psychology. The formation of responsibility as a personal quality of students – future employees of EMERCOM of Russia is an important stage in their development as professionals who show the necessary activity, are able to effectively apply their knowledge and skills in the profession, as well as improve the level of both professional and social competencies.

Human factors analysis and risk assessment during the spread of the Covid-19 epidemic: A case study of the 2019 Wuhan outbreak in China

Since the discovery of novel coronavirus pneumonia (Covid-19) in Wuhan, China, in December 2019, it has spread to other Chinese provinces and continents in just one month, becoming a "public health emergency of international concern." The undesired behaviors of the public and patients during the Covid-19 epidemic cannot be ignored, but few scholars have studied them. In this study, we firstly adopted a qualitative analysis method based on a theoretical paradigm to to summarize the human factors in the spread of the COVID-19 epidemic, and defined the concept of "human factors of the epidemic." Then, we analyzed the distribution characteristics of "human factors of epidemic" at each stage by using statistical analysis, and constructed a human factors model of epidemic evolution. Finally, a multi-subject risk assessment model was constructed using a fuzzy Bayesian network analysis method to quantify the human factors risk in the spread of the COVID-19 epidemic. The results of the study are as follows. (1) The human factors of the COVID-19 epidemic mainly focused on five aspects, including cognitive bias, defective design, management bias, environmental defects, and intentional violations. (2) There were differences in the human factors at different stages of the spread of the COVID-19 epidemic. In the outbreak stage, human factors of the COVID-19 epidemic showed complex trends, with factors such as lack of knowledge and low awareness still prevailing on the one hand, and factors such as lack of capacity, overtly agree but covertly oppose, dereliction of duty, concealment and misreporting, lack of resources, protection defects, design defects, escape/fleeing, and public gathering on the other hand also being more prominent. (3) The risk of the spread of the COVID-19 epidemic due to undesired human factors in the subjects involved was high (p = 0.641) under conventional intervention scenarios. Risk factors such as low awareness, poor decision making, lack of resources, lack of awareness, system deficiencies, public agglomeration, inadequate protection, misreporting, and dereliction of duty had relatively large sensitivity factors and were key human factors for the spread of the epidemic in Wuhan. Finally, targeted recommendations are proposed based on the evolutionary pattern and risk level of the human factors of the COVID-19 epidemic.

Risk management of human factors in airports screening process

Screening process at airports is considered as a critical element for airport security. The screening checkpoints are a component of the airport infrastructure that constitutes a contribution to the security of nations. Regarding the human factor in screening checkpoints, there is an opportunity to consider their specificities within the scope of risk management. This study aimed to develop a consistent tool (risk matrix) which serves as a guide for airports to assess and map human factors in the screening checkpoints. The research method involved four phases, namely: Phase 1: Criterion-conceptual design of the Risk Matrix Modeling; Phase 2: Instrument Construction; Phase 3: Instrument validation; and Phase 4: Practical application of the instrument in a real airport operational context. The risk matrix structure comprises four methodological steps (context, risks, assessment and response) and presents criteria that were not used for this purpose, such as residual risks analysis and financial impact dosimetry linked to administrative measures arising from quality control actions. The tool consists of eleven correlated spreadsheets and is a useful model for mapping human factors (HF-AVSEC) under the security scope. The main results from this study are: (i) the elaboration of a conceptual model for the human factors risk matrix in AVSEC; (ii) the establishment of criteria for the human factors risk matrix; (iii) the development of a practical instrument modeling for AVSEC human factor management (HFM-AVSEC); (iv) validation of the risk matrix criteria by an expert panel working in Class AP-3 and Class AP-2 airports; (v) the application of the matrix in two Brazilian airports (Class AP-2 and Class AP-3), constituting a pioneering, practical and effective contribution to the AVSEC in terms of risk management at airports. The proposed matrix can be replicated by organizations in the aviation industry and by other entities structured as complex and socio-technical systems.

A dynamic human-factor risk model to analyze safety in sociotechnical systems

The performance of sociotechnical elements varies owing to a wide range of endogenous and exogenous influencing factors. These are called uncoupled variability as per Safety-II. The uncoupled variability has drawn rare attention, despite its vital importance in major accidents analysis as per Safety-I and Safety-II paradigms. Accordingly, as the first attempt, this study proposes a systematic model to analyze performance variability in human, organizational, and technology-oriented functions caused by various variability shaping factors (VSFs). The model contains three main phases. First, a FRAM (Functional Resonance Analysis Method) - driven Human-Organization-Technology Taxonomy is developed. Subsequently, Dempster - Shafer Evidence theory is employed to elicit knowledge under epistemic uncertainty. The proposed causation model is integrated into Dynamic Bayesian Networks to support decision-making under aleatory uncertainty. Finally, a criticality matrix is developed to evaluate the performance of the system functions to support decision-making. The proposed model is built considering the advanced canonical probabilistic approaches (e.g., Noisy Max and Leaky models) that address the critical challenges of incomplete and imprecise data. The proposed dynamic model would help better understand, analyze, and improve the safety performance of complex sociotechnical systems.

Safety archetypes identification and behavior simulation for nuclear power plant operation human reliability improvement

Safe operation is the foundation and guarantee of the sustainable development of the nuclear power industry. With the continuous improvement of digital nuclear power plant (NPP) design and construction, fewer and fewer accidents are caused solely by mechanical failure. Human factors (HFs) have become the most important cause of NPP accidents. By combing insights from the framework of Human Factor Analysis and Classification System (HFACS) and introducing System Dynamics approach, this paper proposes a human reliability model which identifies four safety archetypes illustrating HF risk mechanisms in NPP operation, captures the PSF interrelationship and breaks through traditional independent PSF analysis. This model focuses on the path dependence characteristics of HF risk accumulation process in a form of S-shaped risk-time curve, and the derived scenario simulations provide suggestions for related stakeholders in nuclear power field to evaluate their decision-making on human reliability improvement better in a medium-and-long term vision.

EEG-TNet: An End-To-End Brain Computer Interface Framework for Mental Workload Estimation.

The mental workload (MWL) of different occupational groups' workers is the main and direct factor of unsafe behavior, which may cause serious accidents. One of the new and useful technologies to estimate MWL is the Brain computer interface (BCI) based on EEG signals, which is regarded as the gold standard of cognitive status. However, estimation systems involving handcrafted EEG features are time-consuming and unsuitable to apply in real-time. The purpose of this study was to propose an end-to-end BCI framework for MWL estimation. First, a new automated data preprocessing method was proposed to remove the artifact without human interference. Then a new neural network structure named EEG-TNet was designed to extract both the temporal and frequency information from the original EEG. Furthermore, two types of experiments and ablation studies were performed to prove the effectiveness of this model. In the subject-dependent experiment, the estimation accuracy of dual-task estimation (No task vs. TASK) and triple-task estimation (Lo vs. Mi vs. Hi) reached 99.82 and 99.21%, respectively. In contrast, the accuracy of different tasks reached 82.78 and 66.83% in subject-independent experiments. Additionally, the ablation studies proved that preprocessing method and network structure had significant contributions to estimation MWL. The proposed method is convenient without any human intervention and outperforms other related studies, which becomes an effective way to reduce human factor risks.

Automated well control: A step change in safety

In the oil and gas industry, the operation of drilling is a process which has been manually controlled. Well control is a safety critical function, and it is used to enable safe management of pressures and fluids encountered whilst drilling. Losing well control resulting in an uncontrolled flow of reservoir fluids to surface and subsequent blowout, or fuel fed fire, can occur. The process of controlling this major accident hazard has also been manually controlled and is therefore subject to significant human factors' issues. Each year multiple blowouts and several fatality events happen due to a loss of well control. Traditionally, well control has been completely reliant on a human to identify and react to an influx. However, the human condition means the driller can be distracted, or suddenly influenced by extraneous factors. Overreliance on humans in well control poses a danger to the safety in well operations, because of the inherent and constant exposure to human factors risk. Tasks that require sustained periods of cognitive awareness and reaction can be best performed by an automated process. Other industries have successfully implemented automation to reduce the risks and produce more consistent and efficient outcomes; however, automation of well control has not yet been fully applied. The oil and gas industry can improve from a personnel safety, environmental and reputational perspective. As in other industries, automating the function of well control represents a significant improvement in process safety for drilling operations. With the rapid advancement of technologies associated with simulators and cyber-rigs over the past 20 years, several new technologies have emerged. One of them is a technology which enables automated well control whilst in drilling mode. The Automated Well Control system has been designed to fully automate influx detection and shut-in sequences. Once the system detects the influx, it performs a series of automated operations by taking control of the drilling rig equipment. The drill string is spaced out, top drive and mud pumps are stopped, and the BOP is closed. The system is currently designed for the drilling phase, which also covers initial tripping off bottom and back-reaming, and has been substantially tested against drilling simulators with real drillers. Additionally, a full field trials has been conducted using a conventional land rig which demonstrated the effectiveness of the standard system, proving up the functionality under different operational requirements. A technology qualification exercise was conducted, and the system has received a Technology Qualification Certificate for cyber and traditional rigs. To date, there are around 100 potential modules that can be developed using the same technology to cover every aspect of well construction and decommissioning operations. Existing systems in use on rigs, such as Managed Pressure Drilling (MPD) and Early Kick Detection Systems (EKDS), can also benefit from linking directly with the Automated Well Control system to facilitate a fast and effective re-establishment of the secondary barrier. The Automated Well Control and a MPD systems have recently been combined to create the first integrated package to deliver both pressure control and well control in an efficient and less error prone manner. A full rig trial was successfully performed to demonstrate and verify the integration and functionality of both systems. The Automated Well Control technology referred to above has been patented by the UK Patent Office, which recognises its ability to detect the presence of a fluid influx condition in a wellbore, make a decision against criteria to shut-in, and then automatically initiate a well control protocol that results in the safe shut-in of the well. The paper aims to describe the Automated Well Control technology and explain why the system was designed and how it works, highlighting the benefits of automating this safety critical process.

Elektronik seyir cihazlarının deniz kazalarına etkileri

In recent years, maritime-related organizations and companies have moved to a risk-based approach. To determine the risks, it is necessary to understand comprehensively why accidents occur and how it develops. The most effective measures need to be identified to implement the accident prevention measures successfully. According to the results of scientific studies conducted in the past, 80% of human factors risks were effective in marine accidents. Nowadays, maritime technologies are the most effective method for reducing the risks of human factors. However, the use of electronic navigation devices has not eliminated accidents. In this study, the accident reports for collision and grounding due to the electronic navigation devices' risk was evaluated using Human Factors Analysis and Classification System (HFACS) method. As a result of the study, more than half of the visible (active) causes of accidents have been identified as operating failure factors in electronic navigation equipment. Recommendations to prevent the occurrence of accident factors have been made.

Human Factor Risk Modeling for Shipyard Operation by Mapping Fuzzy Fault Tree into Bayesian Network.

The operational activities conducted in a shipyard are exposed to high risk associated with human factors. To investigate human factors involved in shipyard operational accidents, a double-nested model was proposed in the present study. The modified human factor analysis classification system (HFACS) was applied to identify the human factors involved in the accidents, the results of which were then converted into diverse components of a fault tree and, as a result, a single-level nested model was established. For the development of a double-nested model, the structured fault tree was mapped into a Bayesian network (BN), which can be simulated with the obtained prior probabilities of parent nodes and the conditional probability table by fuzzy theory and expert elicitation. Finally, the developed BN model is simulated for various scenarios to analyze the identified human factors by means of structural analysis, path dependencies and sensitivity analysis. The general interpretation of these analysis verify the effectiveness of the proposed methodology to evaluate the human factor risks involved in operational accidents in a shipyard.

Human-Factor Risk Mitigation in Outdoor Climbing Areas: Survey of Existing Policies in Regulated Climbing Areas.

Popular climbing areas in the United States are frequently on public lands under administrative purview of land management entities. During climbing, climbers may be subject to injury. This study aimed to describe existing climbing risk mitigation tactics used to protect climbers by oversight agencies responsible for these public climbing areas and to identify strategies perceived by the oversight agency to be successful. We identified publicly managed US bouldering, sport, or traditional climbing areas through a state-by-state search of Mountain Project. We assessed climbing-related visitation, injury, rescue, and risk mitigation strategies using a 10-item survey targeting land-use managers, rangers, outdoor recreation planners, and park managers. Quantitative analysis included univariate and multivariate analysis. Qualitative analyses of survey responses with thematic grouping of mitigation interventions were performed. One hundred fifty-seven publicly managed US climbing areas were contacted, and 76 (48%) provided data. Thirty-six (47%) of those that provided data stated that programs are in place to reduce climbing-related injury. There were no associations between demographic variables and the presence of risk mitigation strategies. Four themes of climbing risk mitigation strategies emerged: coordination with climbing coalitions (25% of respondents), permitting (22% of respondents), publication of accident reports (22% of respondents), and preventative search and rescue (17% of respondents). Nearly half of survey respondents reported having climbing risk mitigation programs. There is opportunity to assess the efficacy of risk mitigation strategies through intra-area and inter-area assessments. A centralized climbing injury database may prove useful for assessing the efficacy of and need for risk mitigation techniques.

Human factor risk management procedures applied in the case of open pit mine

Risk management aims to provide a controlled work environment to ensure the safe operation of the high-risk systems. It is a dynamic process which work in a continual state of change. The issue of human factor risk and rules and regulations in open pit mine is a main focus of this paper in order to develop predictive models of behavior of workers in relation to compliance with the procedures and rules. Presented survey was conducted in open pit coal mine, as high-risk system, involving 476 mineworkers. The survey was in the form of a questionnaire, consisting of approximately 45 questions. It aimed to find out the opinions of the mining workforce about risk attitude generally as well as about safety rules and regulations. The first goal was to determine factor with the biggest influence on risk of human factor. The second goal was creation model for prediction behavior of mining workers. The survey also aimed to examine: (a) major human risk factors at a specific open-pit mine site (b) mine workers' opinions about policies and procedures; (c) the manner in which mine safety rules and regulations are perceived and understood; (d) the frequency of deviation from rules and regulations; (e) attitudes related to risk taking and their interaction with rules and regulations; and (f) anticipate the behavior of mining workers with respect to compliance with policies and procedures. In addition, a case study is presented, related to failure of mining equipment, the SchRs 630 bucket wheel excavator, which had occurred due to excessive loads caused by a human factor, such as inadequate operation of the equipment. The goal here was to further relate the effects of the human factor to the structural integrity, by providing a reliable numerical model which could be used to determine the critical locations where failures could occur, and by combining this approach with the previously described risk analysis methodology, structure safety can be improved.

Risk factors for human anthrax outbreak in Kiruhura District, Southwestern Uganda: a population-based case control study

INTRODUCTION: In 2018, Uganda experienced recurrent outbreaks of anthrax in both humans and livestock. We aimed to determine risk factors for human anthrax outbreak among residents of Kazo County, Kiruhura District, southwestern Uganda. METHODS: we conducted an unmatched case control study during March-April 2019. We defined a case as having had anthrax infection reported to be diagnosed by a healthcare worker in a resident of Kazo County between May 1st, 2018 and June 1st, 2018. A control was a resident in the nearest neighboring household who had not been diagnosed with anthrax between May 1st and June 1st and who had no symptoms suggestive of anthrax in May 2018. We obtained participants’ socio-demographic, clinical and exposure characteristics using a structured questionnaire. We performed logistic regression to identify risk factors forhuman anthrax. RESULTS: we recruited 101 participants (28 cases and 73 controls) with median age of 34 (IQR; 26–47) years; most (61.4%) were female. The overall attack rate was 1.9%, while the most common clinical manifestations were ulcers (96.4%) and fever (96.4%). The risk factors for contracting human anthrax were: slaughtering of anthrax infected animals (aOR=5.74; 95%CI: 1.39–23.8), consuming of anthrax infected meat (aOR=6.4; 95%CI: 1.53– 6.7) and being male (aOR=12.8; 95%CI: 3.31-49.1). CONCLUSION: the point source outbreak in humans was predominantly of the cutaneous form as a result of contact with anthrax infected meat. We recommend community sensitization on safe disposal of carcasses, avoiding slaughtering/handling carcasses, and carrying out routine livestock vaccinations against anthrax in Uganda to avert similar outbreaks in future.

Making the implicit explicit: A visual model for lowering the risk of implicit bias of mental/behavioural disorders on safety and quality of care.

Persons with mental illness and/or addictions have poorer health outcomes than the general population. Lower quality of healthcare has been identified as an important factor. A main contributor to lower quality of care for people with mental illnesses and/or addictions may be the cognitive implicit bias of mental versus physical care when assessing and categorizing a patient’s clinical presentation. The objective of this article is to highlight how this implicit cognitive bias of mental versus physical care can result in human factor risks to quality of care. We provide three specific case examples of where these quality concerns arise. We also propose the use of a new visual tool to help educate and create awareness of this implicit-bias-based risk and quality care problem.

Design and Implementation of an Analgesia, Sedation, and Paralysis Order Set to Enhance Compliance of pro re nata Medication Orders with Joint Commission Medication Management Standards in a Pediatric ICU

The use of pro re nata (PRN) medication orders increases nursing flexibility and efficiency of bedside patient care. However, misuse and/or ambiguity of PRN medication orders may increase the propensity for medication errors. The Joint Commission has Medication Management (MM) standards to mitigate such risks. This quality improvement study with a pre-post design aimed to increase compliance of PRN sedative and analgesic orders with use of failure mode and effects analysis (FMEA) and human factors risk assessment methodologies in a pediatric ICU (PICU). Staff education and a PICU analgesia, sedation, and paralysis order set, with predefined PRN orders, were implemented to enhance PRN medication compliance with Joint Commission MM standards. The primary goal was to achieve and maintain a weekly average compliance of ≥ 90%. Proportions of compliant PRN analgesic and sedative orders before and after interventions were compared. Weekly average PRN orders compliance increased from 62.0% ± 9.2% to 77.7% ± 10.1% after staff education was implemented (p = 0.013). After order set implementation, weekly average compliance further increased to 93.2% ± 3.6% (p < 0.0001) and remained > 90% until the end of the study period. Interdisciplinary synthesis using FMEA and human factors risk assessment is effective for identifying system failure modes associated with Joint Commission MM standard noncompliance. Implementation of an order set with forced functionality to include order information compliant with Joint Commission MM standards can enhance and maintain Joint Commission-compliant PRN medication orders.