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Clinical specialist physiotherapy (CSP)-led musculoskeletal triage clinics have been established in the UK as a means of managing patients referred for outpatient orthopaedic consultation. The purpose of this paper is to determine if a CSP could allocate patients into distinct diagnostic triage categories in line with national and international guidelines. A second aim was to describe the CSPs clinical decision making.

A review of 100 spinal patients, consecutively referred between February and April 2013 to a CSP spinal triage clinic was undertaken. Patient demographics (age, gender, employment status), clinical diagnosis and diagnostic triage categories selected for each patient were recorded. Subsequently, if clinical triage led to further investigation or a consultant opinion then the onward management pathway was followed to calculate conversion to consultant surgical intervention.

In total, 69 per cent of patients were independently managed by the CSP and discharged back to the General Practitioner or on to therapy. In total, 30 per cent of patients (n=30) were referred for consultant opinion, of these 12 underwent intervention. This represents a conversion rate to consultant intervention of 40 per cent.

Findings suggest that a significant number of spinal patients referred for an orthopaedic consultation may be managed independently by a CSP. Referral for consultant review was deemed appropriate in terms of conversion to intervention, advice on further imaging, referral to other medical disciplines and patient counselling. This study suggests that CSPs can manage a significant number of patients with spinal conditions whilst providing high-quality referrals to surgical colleagues. This audit provides a novel insight into practitioner behaviour that supports the development of advanced practice for non-medical clinicians.

The purpose of this paper is to show how elements from queueing theory can be used to obtain objective measures of effective capacity in the triage function at Skaraborg Hospital in Sweden without direct observation of the function itself.

Approximately 30,000 patients arrived to the emergency department at Skaraborg Hospital in Sweden during 2011. The exact time of arrival and the exact time of triage were recorded for each patient on an individual level. Basic queueing theory uses arrival rates and system capacity measures to derive average queueing times. The authors use the theoretical relation between these three measures to derive system capacity measures based on observed arrival rates and observed average queueing times.

The effective capacity in the triage process is not a linear function of the number of nurses. However, the management of capacity seems well adapted to the actual demand, even though service levels vary substantially during the day and night.

This paper uses elements from queueing theory in an innovative way to measure the effective capacity of a service process without direct observation, thereby also avoiding the potential risk of the Hawthorne effect.

The purpose of this paper is to investigate the influence of organizational factors on individual decision-making under conditions of uncertainty and time pressure. A method to assess the impact of individual and organizational factors on individual decisions is proposed and experimented in the context of triage decision-making process.

The adopted methodology is based on the bias-variance decomposition formula. The method, usually applied to assess the predictive accuracy of heuristics, has been adjusted to discriminate between the impact of organizational and individual factors affecting heuristic processes. To test the methodology, 25 clinical scenarios have been designed and submitted, through simulations, to the triage nurses of two Spanish hospitals.

Nurses’ decisions are affected by organizational factors in certain task conditions, such as situations characterized by complete and coherent information. When relevant information is lacking and available information is not coherent, decision-makers base their assessments on their personal experience and gut feeling.

Discriminating between the influence of organizational factors and individual ones is the starting point for a more in-depth understanding of how organization can guide the decision process. Using simulations of clinical scenarios in field research does not allow for capturing the influence of some contextual factors, such as the nurses’ stress levels, on individual decisions. This issue will be addressed in further research.

Bias and variance are useful measurements for detecting process improvement actions. A bias prevalence requires a re-design of organizational settings, whereas training would be preferred when variance prevails.

The main contribution of this work concerns the novel interpretation of bias and variance concepts to assess organizational factors’ influence on heuristic decision-making processes, taking into account the level of complexity of decision-related tasks.

Ethics is the foundation on which societies and cultures are based and are fundamental to political, social and economic decision making. Ethical dilemmas have created controversy and heated debate over the years. Disasters have been defined in public health terms as destructive events that result in the need for a wide range of emergency resources to assist and ensure the survival of the stricken population. Lack of medical resources, in conjunction with a mass casualty situation, can present specific ethical challenges. The purpose of this paper is to explore the ethics of disaster management.

In and after a disaster, ethical questions arise regarding appropriate and fair allocation of relief funds to help with recovery. Research in disaster settings poses unique ethical dilemmas. The researcher must determine how to balance the critical need for research with the ethical obligation of respect for, and protection of, the interests of research participants. Ethics as part of an educational program made available to health care providers may assist disaster responders to make the difficult ethical decisions involved in disasters. This literature review discusses these issues in conjunction with disaster response and recovery.

The cardinal virtues of disaster response are prudence, courage, justice, stewardship, vigilance, resilience, self‐effacing charity and communication. These eight virtues are not considered all inclusive, no more than Aristotle considered that his morals or virtues were all inclusive. Ongoing work in disaster management will help to ensure that such situations are managed in an ethical manner that respects the rights and privileges of all those involved.

The literature reviewed for this paper was based on peer reviewed scholarly writings. Concepts of ethics and justice are important issues in disaster situations. This paper offers ideas to prompt further discussion among disaster managers and students of disaster studies.

Social changes are reliant on an understanding of ethics and how it affects society. This paper puts forward ethical concepts to prompt discussion by disaster responders and managers with the hope of improving disaster management.

The paper is an original document that may be useful to students of disaster management and those who teach disaster management

Early identification and separation of suspected Covid-19 patients at triage is vital to prevent disease transmission in healthcare settings. Triaging is a complex and context-specific process to implement especially where resources are scarce and health systems are fragile. The need to allocate these resources in a consistent, transparent, and equitable manner during the covid-19 pandemic is underpinned by ethical principles among which are utilitarianism and egalitarianism. Considerations of social identities such as age, gender, social class, and medical criteria such as comorbidities and frailty may lead to explicit and implicit bias and attendant discrimination. Theoretical constructs such as narrow social utility and reciprocity may be invoked to justify the prioritisation of healthcare workers (HCWs) infected with Covid-19 despite the pitfalls in the underlying assumptions. As no single framework exists to comprehensively guide the Covid-19 triage process, the establishment of institutional recommendations and policies within which are embedded safety nets for managing the physical, mental, and emotional fallouts on HCWs is critical.

The purpose of this paper is to provide an overarching framework to guide the understanding of the allocation and deployment of strategic human capital assets within an organization. Using the concept of medical triage with business units analogous to “patients” and their performance to “symptoms or injuries,” the framework suggests a “steal from the poor” perspective that is counter to conventional organizational decline literature.

This is a conceptual paper proposing that there are five different categories of business unit need for human capital assets: expectant, deceased, immediate, delayed or minimal; all based on the type of environment and holistic performance of the business unit. Based on a business unit’s specific situation, the authors suggest a process model guiding how to conduct a triage analysis to optimize the allocation of strategic human capital assets within an organization.

The authors argue that current trends in assessing strategic human capital assets which make comparisons across organizations are necessary but insufficient (e.g. comparing a store to other stores in its district or region). Each business unit has its own unique internal capabilities and external constraints that also must be accurately assessed to make an informed organizational-level decision about where and how to deploy strategic human capital assets.

Borrowing from medical science, this paper demonstrates a new conceptual framework with propositions for researchers and guidance for practitioners.

The purpose of this paper is to propose a novel approach that automates the visualisation of both quantitative data (the network) and qualitative data (the content) within emails to aid the triage of evidence during a forensics investigation. Email remains a key source of evidence during a digital investigation, and a forensics examiner may be required to triage and analyse large email data sets for evidence. Current practice utilises tools and techniques that require a manual trawl through such data, which is a time-consuming process.

This paper applies the methodology to the Enron email corpus, and in particular one key suspect, to demonstrate the applicability of the approach. Resulting visualisations of network narratives are discussed to show how network narratives may be used to triage large evidence data sets.

Using the network narrative approach enables a forensics examiner to quickly identify relevant evidence within large email data sets. Within the case study presented in this paper, the results identify key witnesses, other actors of interest to the investigation and potential sources of further evidence.

The implications are for digital forensics examiners or for security investigations that involve email data. The approach posited in this paper demonstrates the triage and visualisation of email network narratives to aid an investigation and identify potential sources of electronic evidence.

There are a number of network visualisation applications in use. However, none of these enable the combined visualisation of quantitative and qualitative data to provide a view of what the actors are discussing and how this shapes the network in email data sets.

Presents the full text of the Presidential Address by Stafford Beer to the Triennial Congress of the World Organization of Systems and Cybernetics, New Delhi, India, January 1993. Introduces the components of contemporary change and discusses the diagnostic approach of management cybernetics. Outlines a summary theory of autonomy and considers autonomy at the global level. Offers an account of the cybernetics of chronic societary triage, developing an analysis of triage through category A, B and C partition. Produces a summary theory of team syntegrity and discusses the power and the use of the team syntegrity model. Finally, outlines an action plan for World Syntegration.

The purpose of this paper is to statistically assess the effects of the design factors including usage of data-driven decision support tool (DST), classification of patients (triage), prioritization based on vital scores of patients, number of ambulances and hospital selection rules, on the casualty transportation system’s performance in large-scale disasters. Besides, a data-driven DST for casualty transportation is proposed to enhance the casualty survival and ambulance transportation times during the disaster response stage.

In this study, the authors applied simulation and statistical analysis to evaluate the effects of usage of data-driven DST, classification of patients (triage), prioritization of the patients based on vital scores, number of ambulances and hospital selection rules, on the patient survival and transportation time of the casualty transportation system. An experimental design was made, and 16 scenarios were formulated. Simulation models were developed for all scenarios. The number of unrecoverable casualties and time-spent by the casualties until arriving at the hospital was observed. Then, a statistical analysis was applied to the simulation results, and significant factors were determined.

Utilization of the proposed DST was found to improve the casualty transportation and coordination performance. All main effects of the design factors were found statistically significant for the number of unrecoverable casualties. Besides, for the Time spent Until Arrival of T1-Type Casualty at the Hospital, all of the main factors are significant except the number of ambulances. Respiratory rate, pulse rate, motor response score priority and hospital selection rule based on available hospital capacities must be considered to reduce the number of unrecoverable casualties and time spent until arrival of the casualties at the hospitals.

In this study, the factors that significantly affect the performance of the casualty transportation system were revealed, by simulation and statistical analysis, based on an expected earthquake case, in a metropolitan city. Besides, it was shown that using a data-driven DST that tracks victims and intends to support disaster coordination centers and medical staff performing casualty transportation significantly improves survival rate of the victims and time to deliver the casualties. This research considers the whole systems’ components, contributes to developing the response stage operations by filling gaps between using the data-driven DST and casualty transportation processes.

Online production and transmission of child abuse material (CAM) is a complex and growing global problem. The exponential increase in the volume of CyberTips of CAM offending is placing information processing and decision-making strains on law enforcement. This paper presents the outcomes of a project that reviewed an existing risk assessment tool and then developed a new tool for CAM triaging and investigative prioritisation.

Using a mixed method approach, the authors first explored the capacity of an existing risk assessment tool for predicting a police action. The authors then used these findings to design and implement a replacement CAM decision support tool. Using a random sample of CyberTip alert cases from 2018, the authors then tested the efficiency of the new tool.

The existing risk assessment tool was not fit for CAM triaging purposes. Just six questions from the old tool were found to be statistically and significantly associated with law enforcement agents achieving a police action. The authors found that an immediate threat of abuse/endangering a child, potential case solvability, CAM image assessment, chat assessment, criticality and some weighting for professional judgement were significant in being associated with a police action. The new decision support tool is more efficient to complete and achieved a 93.6% convergence of risk ratings with the old tool using 2018 case data.

This research is unique in its development of an evidence-based decision support tool that enhances the ability of law enforcement agents to objectively and efficiently triage and prioritise increasing numbers of CyberTip alerts.