A Review of Risk Matrices Used in Acute Hospitals in England

In healthcare, patient safety has received substantial attention and, in turn, a number of approaches to managing safety have been adopted from other high‐risk industries. One of these has been risk assessment, predominantly through the use of risk matrices. However, while other industries have criticized the design and use of these risk matrices, the applicability of such criticism has not been investigated formally in healthcare. This study examines risk matrices as used in acute hospitals in England and the guidance provided for their use. It investigates the applicability of criticisms of risk matrices from outside healthcare through a document analysis of the risk assessment policies, procedures, and strategies used in English hospitals. The findings reveal that there is a large variety of risk matrices used, where the design of some might increase the chance of risk misprioritization. Additionally, findings show that hospitals may provide insufficient guidance on how to use risk matrices as well as what to do in response to the existing criticisms of risk matrices. Consequently, this is likely to lead to variation in the quality of risk assessment and in the subsequent deployment of resources to manage the assessed risk. Finally, the article outlines ways in which hospitals could use risk matrices more effectively.     

A Hybrid Methodology for Modeling Risk of Adverse Events in Complex Health‐Care Settings

In spite of increased attention to quality and efforts to provide safe medical care, adverse events (AEs) are still frequent in clinical practice. Reports from various sources indicate that a substantial number of hospitalized patients suffer treatment‐caused injuries while in the hospital. While risk cannot be entirely eliminated from health‐care activities, an important goal is to develop effective and durable mitigation strategies to render the system “safer.” In order to do this, though, we must develop models that comprehensively and realistically characterize the risk. In the health‐care domain, this can be extremely challenging due to the wide variability in the way that health‐care processes and interventions are executed and also due to the dynamic nature of risk in this particular domain. In this study, we have developed a generic methodology for evaluating dynamic changes in AE risk in acute care hospitals as a function of organizational and nonorganizational factors, using a combination of modeling formalisms. First, a system dynamics (SD) framework is used to demonstrate how organizational‐level and policy‐level contributions to risk evolve over time, and how policies and decisions may affect the general system‐level contribution to AE risk. It also captures the feedback of organizational factors and decisions over time and the nonlinearities in these feedback effects. SD is a popular approach to understanding the behavior of complex social and economic systems. It is a simulation‐based, differential equation modeling tool that is widely used in situations where the formal model is complex and an analytical solution is very difficult to obtain. Second, a Bayesian belief network (BBN) framework is used to represent patient‐level factors and also physician‐level decisions and factors in the management of an individual patient, which contribute to the risk of hospital‐acquired AE. BBNs are networks of probabilities that can capture probabilistic relations between variables and contain historical information about their relationship, and are powerful tools for modeling causes and effects in many domains. The model is intended to support hospital decisions with regard to staffing, length of stay, and investments in safety, which evolve dynamically over time. The methodology has been applied in modeling the two types of common AEs: pressure ulcers and vascular‐catheter‐associated infection, and the models have been validated with eight years of clinical data and use of expert opinion.     

A Resilience-Based Approach to Risk Assessments—Building Resilient Organizations under Arctic Conditions

Reliability and higher levels of safety are thought to be achieved by using systematic approaches to managing risks. The assessment of risks has produced a range of different approaches to assessing these uncertainties, presenting models for how risks affect individuals or organizations. Contemporary risk assessment tools based on this approach have proven difficult for practitioners to use as tools for tactical and operational decision making. This article presents an alternative to these assessments by utilizing a resilience perspective, arguing that complex systems are inclined to variety and uncertainty regarding the results they produce and are therefore prone to systemic failures. A continuous improvement approach is a source of reliability when managing complex systems and is necessary to manage varieties and uncertainties. For an organization to understand how risk events occur, it is necessary to define what is believed to be the equilibrium of the system in time and space. By applying a resilience engineering (RE) perspective to risk assessment, it is possible to manage this complexity by assessing the ability to respond, monitor, learn, and anticipate risks, and in so doing to move away from the flawed frequency and consequences approach. Using a research station network in the Arctic as an example illustrates how an RE approach qualifies assessments by bridging risk assessments with value-creation processes. The article concludes by arguing that a resilience-based risk assessment can improve on current practice, including for organizations located outside the Arctic region.     

Advancing Risk‐Informed Decision Making in Managing Defense Nuclear Waste in the United States: Opportunities and Challenges for Risk Analysis

An omnibus spending bill in 2014 directed the Department of Energy to analyze how effectively Department of Energy (DOE) identifies, programs, and executes its plans to address public health and safety risks that remain as part of DOE's remaining environmental cleanup liabilities. A committee identified two dozen issues and associated recommendations for the DOE, other federal agencies, and the U.S. Congress to consider, as well as other stakeholders such as states and tribal nations. In regard to risk assessment, the committee described a risk review process that uses available data, expert experience, identifies major data gaps, permits input from key stakeholders, and creates an ordered set of risks based on what is known. Probabilistic risk assessments could be a follow‐up from these risk reviews. In regard to risk management, the states, in particular, have become major drivers of how resources are driven. States use different laws, different priorities, and challenge DOE's policies in different ways. Land use decisions vary, technology choices are different, and other notable variations are apparent. The cost differences associated with these differences are marked. The net result is that resources do not necessarily go to the most prominent human health and safety risks, as seen from the national level.     

Who is a Distracted Driver? Associations between Mobile Phone Use while Driving,Domain‐Specific Risk Taking,and Personality

Mobile phone use while driving (MPUWD) is an increasingly common form of distracted driving. Given its widespread prevalence, it is important for researchers to identify factors that may predict who is more likely to engage in this risky behavior. The current study investigates associations between MPUWD risk behaviors, domain‐specific risk perceptions, and broad personality dimensions. An Italian community sample (n = 804) completed a survey regarding MPUWD risk perceptions and engagement in MPUWD, in addition to the HEXACO‐PI‐R, a broad six‐factor personality inventory (honesty‐humility, emotionality, extraversion, agreeableness, conscientiousness, openness to experience), and the DOSPERT, a six‐factor domain‐specific self‐report risk‐taking measure (health/safety, recreational, social, ethical, gambling, and investment). With respect to domain‐specific risk taking, greater frequency of SMS use while driving most strongly was associated with greater risk taking for the health/safety, gambling, and ethical risk domains. Further, greater honesty‐humility and conscientiousness, two traits related to cognitive control and risk behaviors, and to a lesser extent openness to experience, were associated with less frequent MPUWD, and positively associated with MPUWD risk perceptions. With growing public safety concern surrounding MPUWD, understanding associated personality factors is not only important for identifying psychological mechanisms underlying risk behavior, but also for more effective prevention and intervention programs.     

Challenges in Development and Implementation of Health‐Risk‐Based Soil Quality Guidelines: Turkey's Experience

Management of contaminated sites is a critical environmental issue around the world due to the human health risk involved for many sites and scarcity of funding. Moreover, clean‐up costs of all contaminated sites to their background levels with existing engineering technologies may be financially infeasible and demand extended periods of operation time. Given these constraints, to achieve optimal utilization of available funds and prioritization of contaminated sites that need immediate attention, health‐risk‐based soil quality guidelines should be preferred over the traditional soil quality standards. For these reasons, traditional soil quality standards are being replaced by health‐risk‐based ones in many countries and in Turkey as well. The need for health‐risk‐based guidelines is clear, but developing these guidelines and implementation of them in contaminated site management is not a straightforward process. The goal of this study is to highlight the problems that are encountered at various stages of the development process of risk‐based soil quality guidelines for Turkey and how they are dealt with. Utilization of different definitions and methodologies at different countries, existence of inconsistent risk assessment tools, difficulties in accessing relevant documents and reports, and lack of specific data required for Turkey are among these problems. We believe that Turkey's experience may help other countries that are planning to develop health‐risk‐based guidelines achieve their goals in a more efficient manner.     

Improving Flood Risk Communication by Focusing on Prevention‐Focused Motivation

This article proposes an approach to flood risk communication that gives particular emphasis to the distinction between prevention and promotion motivation. According to E. Tory Higgins, the promotion system and the prevention system are assumed to coexist in every person, but one or the other may be temporarily or chronically more accessible. These insights have far‐reaching implications for our understanding of people's reasoning about risks. Flood risk communication framed in terms of prevention involves the notions of chance and harm, woven into a story about particular events that necessitate decisions to be more careful about safety issues and protect one's family and oneself from danger. The article describes how the insights worked out in practice, using a flood risk communication experiment among a sample from the general population in a highly populated river delta of the Netherlands. It had a posttest‐only control group design (n = 2,302). The results showed that risk communication had a large effect on the participants’ responses and that this effect was higher among chronic prevention‐focused people than among others. Any information that increased the fit between a prevention‐framed message and a person's chronic prevention motivation produced stronger situationally induced, prevention‐focused responses. This may significantly improve communication about risks. In contrast, the notion of water city projects, featuring waterside living, had more appeal to promotion‐focused people.     

Rethinking Risk Assessment for Public Utility Safety Regulation

To aid in their safety oversight of large‐scale, potentially dangerous energy and water infrastructure and transportation systems, public utility regulatory agencies increasingly seek to use formal risk assessment models. Yet some of the approaches to risk assessment used by utilities and their regulators may be less useful for this purpose than is supposed. These approaches often do not reflect the current state of the art in risk assessment strategy and methodology. This essay explores why utilities and regulatory agencies might embrace risk assessment techniques that do not sufficiently assess organizational and managerial factors as drivers of risk, nor that adequately represent important uncertainties surrounding risk calculations. Further, it describes why, in the special legal, political, and administrative world of the typical public utility regulator, strategies to identify and mitigate formally specified risks might actually diverge from the regulatory promotion of “safety.” Some improvements are suggested that can be made in risk assessment approaches to support more fully the safety oversight objectives of public regulatory agencies, with examples from “high‐reliability organizations” (HROs) that have successfully merged the management of safety with the management of risk. Finally, given the limitations of their current risk assessments and the lessons from HROs, four specific assurances are suggested that regulatory agencies should seek for themselves and the public as objectives in their safety oversight of public utilities.     

The Use of Individual and Societal Risk Criteria Within the Dutch Flood Safety Policy—Nationwide Estimates of Societal Risk and Policy Applications

The Dutch government is in the process of revising its flood safety policy. The current safety standards for flood defenses in the Netherlands are largely based on the outcomes of cost‐benefit analyses. Loss of life has not been considered separately in the choice for current standards. This article presents the results of a research project that evaluated the potential roles of two risk metrics, individual and societal risk, to support decision making about new flood safety standards. These risk metrics are already used in the Dutch major hazards policy for the evaluation of risks to the public. Individual risk concerns the annual probability of death of a person. Societal risk concerns the probability of an event with many fatalities. Technical aspects of the use of individual and societal risk metrics in flood risk assessments as well as policy implications are discussed. Preliminary estimates of nationwide levels of societal risk are presented. Societal risk levels appear relatively high in the southwestern part of the country where densely populated dike rings are threatened by a combination of river and coastal floods. It was found that cumulation, the simultaneous flooding of multiple dike rings during a single flood event, has significant impact on the national level of societal risk. Options for the application of the individual and societal risk in the new flood safety policy are presented and discussed.     

Designing Graphs that Promote Both Risk Understanding and Behavior Change

Graphs show promise for improving communications about different types of risks, including health risks, financial risks, and climate risks. However, graph designs that are effective at meeting one important risk communication goal (promoting risk‐avoidant behaviors) can at the same time compromise another key goal (improving risk understanding). We developed and tested simple bar graphs aimed at accomplishing these two goals simultaneously. We manipulated two design features in graphs, namely, whether graphs depicted the number of people affected by a risk and those at risk of harm (“foreground+background”) versus only those affected (“foreground‐only”), and the presence versus absence of simple numerical labels above bars. Foreground‐only displays were associated with larger risk perceptions and risk‐avoidant behavior (i.e., willingness to take a drug for heart attack prevention) than foreground+background displays, regardless of the presence of labels. Foreground‐only graphs also hindered risk understanding when labels were not present. However, the presence of labels significantly improved understanding, eliminating the detrimental effect of foreground‐only displays. Labels also led to more positive user evaluations of the graphs, but did not affect risk‐avoidant behavior. Using process modeling we identified mediators (risk perceptions, understanding, user evaluations) that explained the effect of display type on risk‐avoidant behavior. Our findings contribute new evidence to the graph design literature: unlike what was previously feared, we demonstrate that it is possible to design foreground‐only graphs that promote intentions for behavior change without a detrimental effect on risk understanding. Implications for the design of graphical risk communications and decision support are discussed.     

Quantile Uncertainty and Value‐at‐Risk Model Risk

This article develops a methodology for quantifying model risk in quantile risk estimates. The application of quantile estimates to risk assessment has become common practice in many disciplines, including hydrology, climate change, statistical process control, insurance and actuarial science, and the uncertainty surrounding these estimates has long been recognized. Our work is particularly important in finance, where quantile estimates (called Value‐at‐Risk) have been the cornerstone of banking risk management since the mid 1980s. A recent amendment to the Basel II Accord recommends additional market risk capital to cover all sources of “model risk” in the estimation of these quantiles. We provide a novel and elegant framework whereby quantile estimates are adjusted for model risk, relative to a benchmark which represents the state of knowledge of the authority that is responsible for model risk. A simulation experiment in which the degree of model risk is controlled illustrates how to quantify Value‐at‐Risk model risk and compute the required regulatory capital add‐on for banks. An empirical example based on real data shows how the methodology can be put into practice, using only two time series (daily Value‐at‐Risk and daily profit and loss) from a large bank. We conclude with a discussion of potential applications to nonfinancial risks.     

Tutorial: Parallel Computing of Simulation Models for Risk Analysis

Simulation models are widely used in risk analysis to study the effects of uncertainties on outcomes of interest in complex problems. Often, these models are computationally complex and time consuming to run. This latter point may be at odds with time‐sensitive evaluations or may limit the number of parameters that are considered. In this article, we give an introductory tutorial focused on parallelizing simulation code to better leverage modern computing hardware, enabling risk analysts to better utilize simulation‐based methods for quantifying uncertainty in practice. This article is aimed primarily at risk analysts who use simulation methods but do not yet utilize parallelization to decrease the computational burden of these models. The discussion is focused on conceptual aspects of embarrassingly parallel computer code and software considerations. Two complementary examples are shown using the languages MATLAB and R. A brief discussion of hardware considerations is located in the Appendix.     

The Essential Elements of a Risk Governance Framework for Current and Future Nanotechnologies

Societies worldwide are investing considerable resources into the safe development and use of nanomaterials. Although each of these protective efforts is crucial for governing the risks of nanomaterials, they are insufficient in isolation. What is missing is a more integrative governance approach that goes beyond legislation. Development of this approach must be evidence based and involve key stakeholders to ensure acceptance by end users. The challenge is to develop a framework that coordinates the variety of actors involved in nanotechnology and civil society to facilitate consideration of the complex issues that occur in this rapidly evolving research and development area. Here, we propose three sets of essential elements required to generate an effective risk governance framework for nanomaterials. (1) Advanced tools to facilitate risk‐based decision making, including an assessment of the needs of users regarding risk assessment, mitigation, and transfer. (2) An integrated model of predicted human behavior and decision making concerning nanomaterial risks. (3) Legal and other (nano‐specific and general) regulatory requirements to ensure compliance and to stimulate proactive approaches to safety. The implementation of such an approach should facilitate and motivate good practice for the various stakeholders to allow the safe and sustainable future development of nanotechnology.     

Building a Human Health Risk Assessment Ontology (RsO): A Proposed Framework

Over the last decade the health and environmental research communities have made significant progress in collecting and improving access to genomic, toxicology, exposure, health, and disease data useful to health risk assessment. One of the barriers to applying these growing volumes of information in fields such as risk assessment is the lack of informatics tools to organize, curate, and evaluate thousands of journal publications and hundreds of databases to provide new insights on relationships among exposure, hazard, and disease burden. Many fields are developing ontologies as a way of organizing and analyzing large amounts of complex information from multiple scientific disciplines. Ontologies include a vocabulary of terms and concepts with defined logical relationships to each other. Building from the recently published exposure ontology and other relevant health and environmental ontologies, this article proposes an ontology for health risk assessment (RsO) that provides a structural framework for organizing risk assessment information and methods. The RsO is anchored by eight major concepts that were either identified by exploratory curations of the risk literature or the exposure‐ontology working group as key for describing the risk assessment domain. These concepts are: (1) stressor, (2) receptor, (3) outcome, (4) exposure event, (5) dose‐response approach, (6) dose‐response metric, (7) uncertainty, and (8) measure of risk. We illustrate the utility of these concepts for the RsO with example curations of published risk assessments for ionizing radiation, arsenic in drinking water, and persistent pollutants in salmon.     

Beyond Value‐at‐Risk: GlueVaR Distortion Risk Measures

We propose a new family of risk measures, called GlueVaR, within the class of distortion risk measures. Analytical closed‐form expressions are shown for the most frequently used distribution functions in financial and insurance applications. The relationship between GlueVaR, value‐at‐risk, and tail value‐at‐risk is explained. Tail subadditivity is investigated and it is shown that some GlueVaR risk measures satisfy this property. An interpretation in terms of risk attitudes is provided and a discussion is given on the applicability in nonfinancial problems such as health, safety, environmental, or catastrophic risk management.     

Risk Analysis for Environmental Health Triage

The Homeland Security Act mandates the development of a national, risk-based system to support planning for, response to, and recovery from emergency situations involving large-scale toxic exposures. To prepare for and manage consequences effectively, planners and responders need not only to identify zones of potentially elevated individual risk but also to predict expected casualties. Emergency response support systems now define "consequences" by mapping areas in which toxic chemical concentrations do or may exceed Acute Exposure Guideline Levels (AEGLs) or similar guidelines. However, because AEGLs do not estimate expected risks, current unqualified claims that such maps support consequence management are misleading. Intentionally protective, AEGLs incorporate various safety/uncertainty factors depending on the scope and quality of chemical-specific toxicity data. Some of these factors are irrelevant, and others need to be modified, whenever resource constraints or exposure-scenario complexities require responders to make critical trade-off (triage) decisions in order to minimize expected casualties. AEGL-exceedance zones cannot consistently be aggregated, compared, or used to calculate expected casualties and so may seriously misguide emergency response triage decisions. Methods and tools well established and readily available to support environmental health protection are not yet developed for chemically-related environmental health triage. Effective triage decisions involving chemical risks require a new assessment approach that focuses on best estimates of likely casualties, rather than on upper plausible bounds of individual risk. If risk-based consequence management is to become a reality, federal agencies tasked with supporting emergency response must actively coordinate to foster new methods that can support effective environmental health triage.     

Using Prior Risk‐Related Knowledge to Support Risk Management Decisions: Lessons Learnt from a Tunneling Project

The authors of this article have developed six probabilistic causal models for critical risks in tunnel works. The details of the models' development and evaluation were reported in two earlier publications of this journal. Accordingly, as a remaining step, this article is focused on the investigation into the use of these models in a real case study project. The use of the models is challenging given the need to provide information on risks that usually are both project and context dependent. The latter is of particular concern in underground construction projects. Tunnel risks are the consequences of interactions between site‐ and project‐ specific factors. Large variations and uncertainties in ground conditions as well as project singularities give rise to particular risk factors with very specific impacts. These circumstances mean that existing risk information, gathered from previous projects, is extremely difficult to use in other projects. This article considers these issues and addresses the extent to which prior risk‐related knowledge, in the form of causal models, as the models developed for the investigation, can be used to provide useful risk information for the case study project. The identification and characterization of the causes and conditions that lead to failures and their interactions as well as their associated probabilistic information is assumed to be risk‐related knowledge in this article. It is shown that, irrespective of existing constraints on using information and knowledge from past experiences, construction risk‐related knowledge can be transferred and used from project to project in the form of comprehensive models based on probabilistic‐causal relationships. The article also shows that the developed models provide guidance as to the use of specific remedial measures by means of the identification of critical risk factors, and therefore they support risk management decisions. Similarly, a number of limitations of the models are discussed.     

Construction Safety Risk Modeling and Simulation

By building on a genetic‐inspired attribute‐based conceptual framework for safety risk analysis, we propose a novel approach to define, model, and simulate univariate and bivariate construction safety risk at the situational level. Our fully data‐driven techniques provide construction practitioners and academicians with an easy and automated way of getting valuable empirical insights from attribute‐based data extracted from unstructured textual injury reports. By applying our methodology on a data set of 814 injury reports, we first show the frequency‐magnitude distribution of construction safety risk to be very similar to that of many natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state‐of‐the‐art techniques in hydroclimatology and insurance, we then introduce univariate and bivariate nonparametric stochastic safety risk generators based on kernel density estimators and copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithful to the original data, allowing safety‐related decision making under uncertainty to be grounded on extensive empirical evidence. One of the implications of our study is that like natural phenomena, construction safety may benefit from being studied quantitatively by leveraging empirical data rather than strictly being approached through a managerial perspective using subjective data, which is the current industry standard. Finally, a side but interesting finding is that in our data set, attributes related to high energy levels (e.g., machinery, hazardous substance) and to human error (e.g., improper security of tools) emerge as strong risk shapers.     

Developing a Broadly Applicable Measure of Risk Perception

Decades of research identify risk perception as a largely intuitive and affective construct, in contrast to the more deliberative assessments of probability and consequences that form the foundation of risk assessment. However, a review of the literature reveals that many of the risk perception measures employed in survey research with human subjects are either generic in nature, not capturing any particular affective, probabilistic, or consequential dimension of risk; or focused solely on judgments of probability. The goal of this research was to assess a multidimensional measure of risk perception across multiple hazards to identify a measure that will be broadly useful for assessing perceived risk moving forward. Our results support the idea of risk perception being multidimensional, but largely a function of individual affective reactions to the hazard. We also find that our measure of risk perception holds across multiple types of hazards, ranging from those that are behavioral in nature (e.g., health and safety behaviors), to those that are technological (e.g., pollution), or natural (e.g., extreme weather). We suggest that a general, unidimensional measure of risk may accurately capture one's perception of the severity of the consequences, and the discrete emotions that are felt in response to those potential consequences. However, such a measure is not likely to capture the perceived probability of experiencing the outcomes, nor will it be as useful at understanding one's motivation to take mitigation action.