The Functional Resonance Analysis Method as a health care research methodology: a scoping review.

OBJECTIVE: The objective of this review was to examine and map the literature on the use of the Functional Resonance Analysis Method (FRAM) in health care research. INTRODUCTION: The FRAM is a resilient health care tool tat offers an approach to deconstruct complex systems by mapping health care processes to identify essential activities, how they are interrelated, and the variability that emerges, which can strengthen or compromise outcomes. Insight into how the FRAM has been operationalized in health care can help researchers and policy-makers understand how this method can be used to strengthen health care systems. INCLUSION CRITERIA: This scoping review included research and narrative reports on the application of the FRAM in any health care setting. The focus was to identify the key concepts and definitions used to describe the FRAM; the research questions, aims, and objectives used to study the FRAM; the methods used to operationalize the FRAM; the health care processes examined; and the key findings. METHODS: A three-step search strategy was used to find published and unpublished research and narrative reports conducted in any country. Only papers published in English were considered. No limits were placed on the year of publication. CINAHL, MEDLINE, Embase, PsycINFO, Inspec Engineering Village, ProQuest Nursing & Allied Health were searched originally in June 2020 and again in March 2021. A search of the gray literature was also completed in March 2021. Data were extracted from papers by two independent reviewers using a data extraction tool developed by the reviewers. Search results are summarized in a flow diagram, and the extracted data are presented in tabular format. RESULTS: Thirty-one papers were included in the final review, and most (n = 25; 80.6%) provided a description or definition of the FRAM. Only two (n = 2; 6.5%) identified a specific research question. The remaining papers each identified an overall aim or objective in applying the FRAM, the most common being to understand a health care process (n = 20; 64.5%). Eleven different methods of data collection were identified, with interviews being the most common (n = 21; 67.7%). Ten different health care processes were explored, with safety and risk identification (n = 8; 25.8%) being the most examined process. Key findings identified the FRAM as a mapping tool that can identify essential activities or functions of a process (n = 20; 64.5%), how functions are interdependent or coupled (n = 18; 58.1%), the variability that can emerge within a process (n = 20; 64.5%), discrepancies between work as done and work as imagined (n = 20; 64.5%), the resiliency that exists within a process (n = 12; 38.7%), and the points of risk within a process (n = 10, 32.3%). Most papers (n = 27; 87.1%) developed models representing the complexity of a process. CONCLUSIONS: The FRAM aims to use a systems approach to examine complex processes and, as evidenced by this review, is suited for use within the health care domain. Interest in the FRAM is growing, with most of the included literature being published since 2017 (n = 24; 77.4%). The FRAM has the potential to provide comprehensive insight into how health care work is done and how that work can become more efficient, safer, and better supported.

A knowledge elicitation study to inform the development of a consequence model for Arctic ship evacuations: Qualitative and quantitative data.

Expert knowledge was elicited to develop a life-safety consequence severity model for Arctic ship evacuations (Browne et al., 2021). This paper presents the associated experimental design and data. Through semi-structured interviews, participants identified factors that influence consequence severity. Through a survey, participants evaluated consequence severity of different ship evacuation scenarios. The methodology represents a two-phased mixed methods design. Life-safety consequence severity is measured as the expected number of fatalities resulting from an evacuation. Participants of the study were experts in various fields of the Arctic maritime industry. Sixteen experts participated in the interviews and the survey (sample size: n = 16). Sample size for the interviews was based on thematic data saturation. Predominantly the same group of experts participated in the survey. Interviews were analysed using thematic analysis. Interview data informed the development of evacuation scenarios defined in the survey. The interview guide and survey questions are presented. Data tables present the codes that emerged through thematic analysis, including code reference counts and code intersection counts. Data tables present the raw data of participant responses to the survey. This data can support further investigation of factors that influence consequence severity, definition of a broader range of evacuation scenarios, and establishment of associated consequence severities. This data has value to Arctic maritime policy-makers, researchers, and other stakeholders engaged in maritime operational risk management.

A dynamic version of the FRAM for capturing variability in complex operations.

Functional Resonance Analysis Method (FRAM) is a function-based approach to model complex socio-technical systems and to manage variability. The current FRAM related tools are unable to capture qualitative and quantitative characteristics of variability as well as temporal variations. This study presents in detail a dynamic FRAM-based tool, which is called DynaFRAM. It is introduced to address the variability-related deficiencies of the FRAM related tools. It aims to capture variability in complex operations. It is a dynamic tool developed to capture time related variations in complex operations. This increases the attractiveness of the DynaFRAM for complex operations where specialists and practitioners make decisions in complicated situations. The ability of the DynaFRAM is demonstrated by examining a healthcare related case study. Although the ability of the DynaFRAM is assessed through capturing variations in healthcare operations, it can be applied to other domains in a similar manner.•The DynaFRAM is a dynamic FRAM-based tool.•It is able to captures different characteristics of variability.•It facilitates understanding and analysis of variability in complex operations.

Modeling and analyzing hospital to home transition processes of frail older adults using the functional resonance analysis method (FRAM).

The main purpose of this study was to model and analyze hospital to home transition processes of frail older adults in order to identify the challenges within this process. A multi-phase, multi-sited and mixed methods design was utilized, in which, Phase 1 included collecting semi-structured interviews and focus group data, and Phase 2 consisted of six patient/caregiver dyad prospective case studies. This study was conducted in three hospitals in three cities in a single province in Canada. The Functional Resonance Analysis Method (FRAM) was employed to model daily operations of the transition process. The perspectives of both healthcare providers and patients/caregivers were used to build the FRAM model. The transition model was then tested using a customized version of the FRAM. The six patient/caregiver cases were used in the process of testing the FRAM model. The results of building the FRAM model showed that five categories of functions contributed to the transition model, including admission, assessment, synthesis, decision-making, and readmission. The outcomes of using the customized version of the FRAM revealed challenges affecting the transition process including waitlists for geriatric units, team-based care, lack of a discharge planner, financial concerns, and follow-up plans. The findings of this study could assist managers and other decision makers to improve the transition processes of frail older adults by addressing these challenges. The FRAM method employed in this study can be applied widely to identify work practices that are more or less successful, so that procedures and practices can be adapted to nudge healthcare processes towards paths that will yield better outcomes.

A food chain-based ecological risk assessment model for oil spills in the Arctic environment.

This paper investigates the linkage between the acute impacts on apex marine mammals with polar cod responses to an oil spill. It proposes a Bayesian network-based model to link these direct and indirect effects on the apex marine mammals. The model predicts a recruitment collapse (for the scenarios considered), causing a higher risk of mortality of polar bears, beluga whales, and Narwhals in the Arctic region. Whales (adult and calves) were predicted to be at higher risk when the spill was under thick ice, while adult polar bears were at higher risk when the spill occurred on thin ice. A spill over the thick ice caused the least risk to whale and adult polar bears. The spill's timing and location have a significant impact on the animals in the Arctic region due to its unique sea ice dynamics, simple food web, and short periods of food abundance.

The functional resonance analysis method as a health care research methodology: a scoping review protocol.

OBJECTIVE: The objective of this review is to examine and map the literature on the use of the functional resonance analysis method in health care research. INTRODUCTION: Health care systems are highly complex and involve interrelated functions, organizations, individuals, and technologies. Understanding how these elements interact and impact health care processes is difficult because of inherent contextual and human variables. The functional resonance analysis method offers an approach to deconstruct complex systems and examine relationships between individual processes and elements. By using the functional resonance analysis method, researchers can map health care processes and uncover performance variables that can emerge and strengthen, or compromise, intended outcomes. Insight into how the functional resonance analysis method has been operationalized in health care research will help researchers and policy makers understand how the method can be used to strengthen health care systems. INCLUSION CRITERIA: The scoping review will consider research and narrative reports on the application of the functional resonance analysis method in health care research. The concepts of interest are the research questions/aims/objectives, methods used to operationalize the functional resonance analysis method, key concepts and definitions of the functional resonance analysis method, and key findings. Studies that used the functional resonance analysis method in any health care setting will be considered. METHODS: The scoping review will aim to locate published and unpublished literature by employing a three-step search strategy. Only papers published in English will be considered and no limits will be placed on the year of publication. Data extracted will include key concepts and definitions of the functional resonance analysis method, research questions/aims/objectives, methods used to operationalize the functional resonance analysis method, and key findings. Extracted data will be reported in tabular form and presented narratively to express the review question.

Risk assessment of Arctic aquatic species using ecotoxicological biomarkers and Bayesian network.

The risk to Arctic aquatic species due to accidental oil spills is not well studied. One of the key reasons for this limitation is the lack of understanding of the dose-response relations for the species in the Arctic region. The present study addresses this knowledge gap. It proposes a new approach to develop dose-response curves for Arctic aquatic species. The application of the approach is demonstrated using the estimation of mortality risk in Boreogadus saida (polar cod) due to exposure from polycyclic aromatic hydrocarbons (PAH). The proposed approach considers the toxicity mechanism in Arctic species (i.e. polar cod) and regional environmental factors, and models these as a belief-based Bayesian Network (BN). The BN model integrates diverse ecotoxicology biomarker data types and predicts the cell death probability due to exposure to a toxicant (PAH in crude oil). The input data and results from the model were verified using data available in the literature. Seasonal sea ice played a major role in containing PAH exposure and subsequent risk to polar cod. However, the physiological factors, such as presence of higher Phase II activity, and higher oxyradical scavenging ability, had greater impact on PAH risk mitigation.

Dynamic ecological risk modelling of hydrocarbon release scenarios in Arctic waters.

The Arctic is an ecologically diverse area that is increasingly vulnerable to damages from oil spills associated with commercial vessels traversing newly open shipping lanes. The significance of such accidents on Arctic marine habitats and the potential for recovery can be examined using ecological risk assessment (ERA) coupled with a dynamic object-oriented Bayesian network (DOOBN). A DOOBN approach is useful to represent the probabilistic relationships inherent in the interactions between key events associated with an oil spill, including oil dispersion from the source, ice-oil slick interactions, seawater-oil slick formation, sedimentation, and exposures to different aquatic life. From such analysis, a probabilistic cost analysis can be performed to examine the theoretical cost of habitat services lost and restored. The application of an ERA-DOOBN model to assess oil spills in the Arctic is demonstrated using a case study. The utility of the model output for determining habitat restoration costs and developing policy guidelines for ecological response measures in the Arctic is also discussed.

Arctic marine fish 'biotransformation toxicity' model for ecological risk assessment.

Oil and gas exploration and marine transport in the Arctic region have put the focus on the ecological risk of the possibly exposed organisms. In the present study, the impacts of sea ice, extreme light regime, various polar region-specific physiological characteristics in polar cod (Boreogadus saida) and their effects on xenobiotic distribution and metabolism are studied. A Bayesian belief network is used to model individual fish toxicity. The enzyme activity in the fish liver and other pertinent organs is used as a proxy for cellular damage and repair and is subsequently linked to toxicity in polar cod. Seasonal baseline variation in enzyme production is also taken into consideration. The model estimates the probability of exposure concentration to cause cytotoxicity and circumvents the need to use the traditionally obtained No Observed Effect Concentration (NOEC). Instead, it uses biotransformation enzyme activity as a basis to estimate the probability of individual cell damages.

Assessment of changes in neural activity during acquisition of spatial knowledge using EEG signal classification.

OBJECTIVE: This study explored the classification of electroencephalography (EEG) signals to assess changes in neural activity as individuals performed a training task in a virtual environment simulator. Commonly, task behavior and perception are used to assess a trainee's ability to perform a task, however, changes in cognition are not usually measured and could be important to provide a true indication of an individual's level of knowledge or skill. APPROACH: In this study, 15 participants acquired spatial knowledge via 60 navigation trials (divided into ten blocks) in a novel virtual environment. Time performance, perceived certainty, and EEG signal data were collected. MAIN RESULTS: A significant increase in alpha power and classification accuracy of EEG data from block 1 against blocks 2-10 was observed and stabilized after block 7, while time performance and perceived certainty measures improved and stabilized after block 5 and 6, respectively. SIGNIFICANCE: Results suggest that changes in neural activity, which may reflect an increase in cognitive efficiency, could provide additional insight beyond time performance and perceived certainty.

Human performance data collected in a virtual environment.

This data article describes the experimental data used in the research article "Incorporating individual differences in human reliability analysis: an extension to the virtual experimental technique" (Musharraf et al., 2017) [1]. The article provides human performance data for 36 individuals collected using a virtual environment. Each participant was assigned to one of two groups for training: 1) G1: high level training and 2) G2: low level training. Participants' performance was tested in 4 different virtual scenarios with different levels of visibility and complexity. Several performance metrics of the participants were recorded during each scenario. The metrics include: time to muster, time spent running, interaction with fire doors and watertight doors, interaction with hazards, and reporting at different muster locations.

Aquatic ecotoxicological models and their applicability in Arctic regions.

Dose-response modeling is one of the most important steps of ecological risk assessment. It requires concentration-effects relationships for the species under consideration. There are very limited studies and experimental data available for the Arctic aquatic species. Lack of toxicity data hinders obtaining dose-response relationships for lethal (LC50 values), sub-lethal and carcinogenic effects. Gaps in toxicity data could be filled using a variety of in-silico ecotoxicological methods. This paper reviews the suitability of such methods for the Arctic scenario. Mechanistic approaches like toxicokinetic and toxicodynamic analysis are found to be better suited for interspecies extrapolation than statistical methods, such as Quantitative Structure-Activity Relationships/Quantitative Structure Activity-Activity Relationship, ICE, and other empirical models, such as Haber's law and Ostwald's equation. A novel approach is proposed where the effects of the toxicant exposure are quantified based on the probability of cellular damage and metabolites interactions. This approach recommends modeling cellular damage using a toxicodynamic model and physiology or metabolites interactions using a toxicokinetic model. Together, these models provide more reliable estimates of toxicity in the Arctic aquatic species, which will assist in conducting ecological risk assessment of Arctic environment.

A Flexible Hierarchical Bayesian Modeling Technique for Risk Analysis of Major Accidents.

Safety analysis of rare events with potentially catastrophic consequences is challenged by data scarcity and uncertainty. Traditional causation-based approaches, such as fault tree and event tree (used to model rare event), suffer from a number of weaknesses. These include the static structure of the event causation, lack of event occurrence data, and need for reliable prior information. In this study, a new hierarchical Bayesian modeling based technique is proposed to overcome these drawbacks. The proposed technique can be used as a flexible technique for risk analysis of major accidents. It enables both forward and backward analysis in quantitative reasoning and the treatment of interdependence among the model parameters. Source-to-source variability in data sources is also taken into account through a robust probabilistic safety analysis. The applicability of the proposed technique has been demonstrated through a case study in marine and offshore industry.

Dynamic fugacity model for accidental oil release during Arctic shipping.

Improved understanding of ecological risk associated with Arctic shipping would help advance effective oil spill prevention, control, and mitigation strategies. Ecological risk assessment involves analysis of a release (oil), its fate, and dispersion, and the exposure and intake of the contaminant to different receptors. Exposure analysis is a key step of the detailed ecological risk assessment, which involves the evaluation of the concentration and persistence of released pollutants in the media of contact. In the present study, a multimedia fate and transport model is presented, which is developed using a fugacity-based approach. This model considers four media: air, water, sediment, and ice. The output of the model is the concentration of oil (surrogate hydrocarbons-naphthalene) in these four media, which constitutes the potential exposure to receptors. The concentration profiles can subsequently be used to estimate ecological risk thereby providing guidance to policies for Arctic shipping operations, ship design, and ecological response measures.

Modeling oil weathering and transport in sea ice.

This paper presents a model of oil weathering and transport in sea ice. It contains a model formulation and scenario simulation to test the proposed model. The model formulation is based on state-of-the-art models for individual weathering and transport processes. The approach incorporates the dependency of weathering and transport processes on each other, as well as their simultaneous occurrence after an oil spill in sea ice. The model is calibrated with available experimental data. The experimental data and model prediction show close agreement. A sensitivity analysis is conducted to determine the most sensitive parameters in the model. The model is useful for contingency planning of a potential oil spill in sea ice. It is suitable for coupling with a level IV fugacity model, to estimate the concentration and persistence of hydrocarbons in air, ice, water and sediments for risk assessment purposes.

Fault and event tree analyses for process systems risk analysis: uncertainty handling formulations.

Quantitative risk analysis (QRA) is a systematic approach for evaluating likelihood, consequences, and risk of adverse events. QRA based on event (ETA) and fault tree analyses (FTA) employs two basic assumptions. The first assumption is related to likelihood values of input events, and the second assumption is regarding interdependence among the events (for ETA) or basic events (for FTA). Traditionally, FTA and ETA both use crisp probabilities; however, to deal with uncertainties, the probability distributions of input event likelihoods are assumed. These probability distributions are often hard to come by and even if available, they are subject to incompleteness (partial ignorance) and imprecision. Furthermore, both FTA and ETA assume that events (or basic events) are independent. In practice, these two assumptions are often unrealistic. This article focuses on handling uncertainty in a QRA framework of a process system. Fuzzy set theory and evidence theory are used to describe the uncertainties in the input event likelihoods. A method based on a dependency coefficient is used to express interdependencies of events (or basic events) in ETA and FTA. To demonstrate the approach, two case studies are discussed.

Distribution of arsenic and copper in sediment pore water: an ecological risk assessment case study for offshore drilling waste discharges.

Due to the hydrophobic nature of synthetic based fluids (SBFs), drilling cuttings are not very dispersive in the water column and settle down close to the disposal site. Arsenic and copper are two important toxic heavy metals, among others, found in the drilling waste. In this article, the concentrations of heavy metals are determined using a steady state "aquivalence-based" fate model in a probabilistic mode. Monte Carlo simulations are employed to determine pore water concentrations. A hypothetical case study is used to determine the water quality impacts for two discharge options: 4% and 10% attached SBFs, which correspond to the best available technology option and the current discharge practice in the U.S. offshore. The exposure concentration (CE) is a predicted environmental concentration, which is adjusted for exposure probability and bioavailable fraction of heavy metals. The response of the ecosystem (RE) is defined by developing an empirical distribution function of predicted no-effect concentration. The pollutants' pore water concentrations within the radius of 750 m are estimated and cumulative distributions of risk quotient (RQ=CE/RE) are developed to determine the probability of RQ greater than 1.

Evaluation of generic types of drilling fluid using a risk-based analytic hierarchy process.

The composition of drilling muds is based on a mixture of clays and additives in a base fluid. There are three generic categories of base fluid--water, oil, and synthetic. Water-based fluids (WBFs) are relatively environmentally benign, but drilling performance is better with oil-based fluids (OBFs). The oil and gas industry developed synthetic-based fluids (SBFs), such as vegetable esters, olefins, ethers, and others, which provide drilling performance comparable to OBFs, but with lower environmental and occupational health effects. The primary objective of this paper is to present a methodology to guide decision-making in the selection and evaluation of three generic types of drilling fluids using a risk-based analytic hierarchy process (AHP). In this paper a comparison of drilling fluids is made considering various activities involved in the life cycle of drilling fluids. This paper evaluates OBFs, WBFs, and SBFs based on four major impacts--operations, resources, economics, and liabilities. Four major activities--drilling, discharging offshore, loading and transporting, and disposing onshore--cause the operational impacts. Each activity involves risks related to occupational injuries (safety), general public health, environmental impact, and energy use. A multicriteria analysis strategy was used for the selection and evaluation of drilling fluids using a risk-based AHP. A four-level hierarchical structure is developed to determine the final relative scores, and the SBFs are found to be the best option.