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Most UK ambulance services undertake remote assessments of 999 calls with nurses and paramedics to manage demand and reduce inappropriate hospital admissions. However, little is known about the differences in the types of cases managed by the two professions comparatively, their clinical outcomes, and the quality and safety they offer.

The retrospective descriptive study analysed data collected at Welsh Ambulance Services University NHS Trust (WAST) from prioritisation, triage, and audit tools between the 17th May 2022 to 8th November 2022. A total of 21,076 cases and 728 audits were included for review.

There was little difference in the type and frequency of the presenting complaints assessed, and clinical outcomes reached in percentage terms. Whilst paramedics had more highly compliant call audits and fewer non-compliant call audits, there was, again, little difference in percentage terms between the two, indicating positive levels of safety across the two professional groups.

There continues to be a substantial difference between UK paramedics to those in the Middle East, the United States, and some African nations, which may limit the applicability of findings. This study also looked at a six-month window from only one UK service using one type of prioritisation and triage tool. Future research could explore longer periods from multiple services using various tools. It is important to note that this study did not link outcome data with primary, secondary or tertiary care settings. Thus, it is impossible to determine if the level of care aligned closely with the final diagnosis.

The practical implications of this work include better workforce planning for agencies who have perhaps only employed one type of clinician or a reaffirmation to those who have employed both. The authors suggest that the training and education of both sets of clinicians could remain general in nature, with no overt requirement for specificity based on professional registration alone. Commissioners and stakeholders in the wider health economy should consider ensuring equitable access to alternative pathways for patients assessed by both nurses and paramedics.

It has been posited that UK nurses and paramedics are, by virtue of their consistency in education, skill set, licensure, and general experience, both able to achieve safe and effective remote outcomes in 999 settings. This study provides evidence to support that hypothesis. These results say more about the two professions' ability to work together rather than just the professions themselves. The multidisciplinary team approach is well-established in acute care settings, and is broadly considered to improve communication, coordination decision making, adherence to up-to-date treatment recommendations, and be positive for shared learning and development for younger colleagues.

Most UK services use a mix of nurses and paramedics; however, little is known about the differences in the types of cases managed by the two professions comparatively, their clinical outcomes, and the quality and safety they each offer. The most recent studies of this nature were published in 2003 and 2004 and looked only at low-acuity 999 calls when remote assessment was not even an established role for UK paramedics. This study updates the literature, identifies areas for future research, and applies to the international setting for the most part.

Interventions that improve timely access to outpatient health services are essential in managing demand. This process evaluation aimed to describe the implementation, mechanism of impact and context of an intervention to reduce waiting for first appointments in an outpatient epilepsy clinic.

The UK Medical Research Council framework was used as the theoretical basis for a process evaluation alongside an intervention trial. The intervention, Specific Timely Appointments for Triage (STAT), is a data-driven approach that combines a one-off backlog reduction strategy with methods to balance supply and demand. A mixed methods process evaluation synthesised routinely collected quantitative and qualitative data, which were mapped to the domains of implementation, mechanisms of impact and contextual elements.

The principles of the STAT model were implemented as intended without adaptation. The STAT model reached all patients referred, including long waiters and was likely generalisable to other medical outpatient clinics. Mechanisms of impact were increased clinic capacity and elimination of unwanted variation. Contextual elements included the complexity of healthcare systems and the two-tier triage practice that contributes to prolonged waiting for patients classified as non-urgent.

This process evaluation shows how a data-driven strategy was applied in a medical outpatient setting to manage demand. Improving patient flow by reducing waiting in non-urgent, outpatient care is a complex problem. Understanding how and why interventions work is important for improved timeliness of care, and sustainability of public health services.

Overcrowding continues to afflict emergency departments (EDs), and its attendant consequences are becoming increasingly severe. The burden of the COVID-19 pandemic is further escalating the situation worldwide. One of the most critical questions is how to adequately quantify what constitutes overcrowding and determine implications for operations management in improving service efficiency. This paper aims to discuss the aforementioned.

The authors propose the time and class complexity measures for ED service systems, taking into account important patient-level and system characteristics. Using an extensive data set from a Canadian ED, the authors investigate the performance of complexity-based measures in predicting service delays.

The authors find that the complexity measure is potentially more important than some well-known crowding metrics. In particular, EDs can improve service efficiency by managing the level of complexity within a desirable interval. Furthermore, complexity exposes how the interplay between demand-side behavioral changes and supply-side responses affects operational performance. Moreover, the results suggest that arrival patterns—the number of patients of each class arriving per time and times between events (arrivals and service completions)—increase the risk of service delays more than the demand volume.

This paper is the first to provide an extensive investigation into the application of the complexity-based measure for ED crowding. The study demonstrates potential values to be gained in ED service systems if complexity measure is incorporated into their operations management decisions.

This work aims to integrate the concepts generated by a systematic literature review on patient flows in emergency departments (ED) to serve as a basis for developing a generic process model for ED.

A systematic literature review was conducted using PRISMA guidelines, considering Lean Healthcare interventions describing ED patients’ flows. The initial search found 141 articles and 18 were included in the systematic analysis. The literature analysis served as the basis for developing a generic process model for ED.

ED processes have been represented using different notations, such as value stream mapping and workflows. The main alternatives for starting events are arrival by ambulance or walk-in. The Manchester Triage Scale (MTS) was the most common protocol referred to in the literature. The most common end events are admission to a hospital, transfer to other facilities or admission to an ambulatory care system. The literature analysis allowed the development of a generic process model for emergency departments. Nevertheless, considering that several factors influence the process of an emergency department, such as pathologies, infrastructure, available teams and local regulations, modelling alternatives and challenges in each step of the process should be analysed according to the local context.

A generic business process model was developed using BPMN that can be used by practitioners and researchers to reduce the effort in the initial stages of design or improvement projects. Moreover, it’s a first step toward the development of generalizable and replicable solutions for emergency departments.

Artificial intelligence (AI) applications are increasingly used for day-to-day operations in healthcare. Each has a relatively limited scope or task, and several find application in managerial and organizational processes. More and more, AI and machine learning (ML) devices have received US FDA approval in the last decade. This chapter covers the main AI applications in healthcare, with a focus on organizational AI solutions (administrative AI), the main AI developers, their investment and real-world data and case studies in healthcare and other sectors. AI can be applied in resource management and procurement, resource allocation, clinical case management, staff work shift scheduling and handling of emergencies. AI applications are becoming ubiquitous in hospital (e.g. emergency room and operating theatre) and outpatient settings (e.g. ambulatory care and dentistry clinics). Their implementation is expected to bring direct benefits for patient care and satisfaction. This chapter gives a broad definition of AI in healthcare settings, with a focus on administrative applications and their use in case study data.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

This study aims to facilitate access to vascular disease screening for low-income individuals living in remote and conflict areas based on the results of a pilot trial in Colombia. Also, to increase the amount of diagnosis training of vascular surgery (VS) in civilians.

The operation method includes five stages: strategy development and adjustment; translation of the strategy into a real-world setting; operation logistics planning; strategy analysis and adoption. The operation plan worked efficiently in this study’s sample. It demonstrated high sensibility, efficiency and safety in a real-world setting.

The authors developed and implemented a flow model operating plan for screening vascular pathologies in low-income patients pro bono without proper access to vascular health care. A total of 140 patients from rural areas in Colombia were recruited to a controlled screening session where they underwent serial noninvasive ultrasound assessments conducted by health professionals of different training stages in VS.

The plan was designed to be implemented in remote, conflict areas with limited access to VS care. Vascular injuries are critically important and common among civilians and military forces in regions with active armed conflicts. As this strategy can be modified and adapted to different medical specialties and geographic areas, the authors recommend checking the related legislation and legal aspects of the intended areas where we will implement this tool.

Different sub-specialties can implement the described method to be translated into significant areas of medicine, as the authors can adjust the deployment and execution for the assessment in peripheral areas, conflict zones and other public health crises that require a faster response. This is necessary, as the amount of training to which VS trainees are exposed is low. A simulated exercise offers a novel opportunity to enhance their current diagnostic skills using ultrasound in a controlled environment.

Evaluating and assessing patients with limited access to vascular medicine and other specialties can decrease the burden of vascular disease and related complications and increase the number of treatments available for remote communities.

It is essential to assess the most significant number of patients and treat them according to their triage designation. This management is similar to assessment in remote areas without access to a proper VS consult. The authors were able to determine, classify and redirect to therapeutic interventions the patients with positive findings in remote areas with a fast deployment methodology in VS.

Access to health care is limited due to multiple barriers and the assessment and response, especially in peripheral areas that require a highly skilled team of medical professionals and related equipment. The authors tested a novel mobile assessment tool for remote and conflict areas in a rural zone of Colombia.

The study aims to explore the utilisation of Clinical Decision Support (CDS) tools in Tanzanian healthcare facilities by identifying the tools used, the challenges encountered and the adaptive strategies employed by healthcare practitioners. It utilises an Activity Theory (AT) approach to understand the dynamic interactions between healthcare providers, CDS tools and the broader healthcare system.

The research adopts a qualitative approach in two prominent regions of Tanzania, Dar es Salaam and Dodoma. It involves semi-structured interviews with 26 healthcare professionals and key stakeholders across ten healthcare facilities, supplemented by document reviews. The study employs AT to analyse the interactions between healthcare professionals, CDS tools and the broader healthcare system, identifying best practices and providing recommendations for optimising the use of CDS tools.

The study reveals that Tanzanian healthcare practitioners predominantly rely on non-computerised CDS tools, such as clinical guidelines prepared by the Ministry of Health. Despite the availability of Health Information Systems (HIS), these systems often lack comprehensive decision-support functionalities, leading practitioners to depend on traditional methods and their professional judgement. Significant challenges include limited accessibility to updated clinical guidelines, unreliable infrastructure and inadequate training. Adaptive strategies identified include using non-standardised tools like Medscape, professional judgement and reliance on past experiences and colleagues’ opinions.

The investigation was constrained by access limitations because it was challenging to get some respondents to share information. However, a sufficient number of individuals participated in the interviews, and their knowledge was very beneficial in understanding the procedures and tools for clinical decision support.

This study contributes to AT by extending its application to a low-resource healthcare setting, uncovering new dimensions of the theory related to socio-cultural and technological constraints in healthcare facilities in Tanzania. It provides valuable insights into the practical barriers and facilitators of HIS and CDS tool implementation in developing countries, emphasising the need for context-specific adaptations, robust training programs and user-centred designs. The findings highlight the resilience and imagination of healthcare practitioners in adapting to systemic limitations, offering recommendations to enhance clinical decision-making and improve patient care outcomes in Tanzania.

Emergency medicine can save lives and in 2018 the World Health Assembly passed resolution 72.16 ensuring the role of emergency care in all health systems. With a continued global shortage of emergency physicians, with many low-medium-income countries (LMIC) still to develop emergency medicine as a speciality, the role of emergency nurses is critical to deliver the WHO Emergency Care System Framework (WHO, 2018). Emergency medicine doctors play a critical role in collaborating with nurses, in emergency medicine where nurses are often the first clinicians are often the first clinicians to interact with patients in emergency care settings, making up the majority of health-care professionals in LMIC (Mamalelala, 2024). Yet emergency nursing has yet to become established in Nepal, where nurses are often recruited to emergency departments, without having received any training in emergency or critical care treatment and management. The purpose of this paper is to outline a collaborative leadership approach to co-design an airway, breathing, circulation, disability, exposure (ABCDE) structured approach to an emergency nursing training module designed for nurses to feel empowered in the emergency department and to report on its findings.

This study draws upon mixed methodology research, enrolling 30 nurses (n = 30) from an emergency department in a tertiary hospital in Nepal through three stages of the project: Stage 1: training module co-design, collaborative leadership exploring the rationale for a training module and core features of design based on the ABCDE of emergency medicine; Stage 2: quantitative data were collected to assess baseline pre- and post-intervention knowledge and follow-up knowledge assessment at 30 and 45 days; Stage 3: qualitative data were collected with 24/30 (80%) nurses to evaluate the impact and application of the nurses ABCDE learning 7 months post-training. The qualitative survey was undertaken using online Google Forms.

Nurses were fully engaged in the co-design and collaboration of the development of an ABCDE training module which was delivered over 3 h. Full engagement was secured from all nurses in the department, and there were statistically significant advances in ABCDE emergency knowledge from the baseline, however, this knowledge began to decrease at 30 and 45 days. A follow-up qualitative survey was distributed to nurses seven months after training with an 80% return rate, which reported a range of examples of how nurses were continuing to apply their learning in practice.

This training module for emergency nurses was designed collaboratively from the “bottom up” in a tertiary hospital in Nepal, recognising the need to develop emergency nursing in the emergency department. The data revealed promising findings, while knowledge decreased from the post-training questionnaire, qualitative evidence revealed significant changes in practice, with the greatest reported change in the management of the airway. While this training module has made a difference in the quality of care provided, there is a need for a country-wide strategy in this area otherwise it is likely that such an initiative will only be developed by hospitals at a local level (Lecky, 2014). Education and training initiatives for nurses that focus on an evidence-based approach to clinical practice can bridge the workforce gap in the short term, however, the Government of Nepal must decide on establishing a recognised post-graduate sub-specialty in emergency nursing, the duration of training, who should be trained and what curriculum should be followed (Lecky, 2014).

Workflow optimisation is crucial for establishing a viable acute stroke (AS) intervention programme in a large tertiary care centre. This study aims to utilise Lean Six Sigma (LSS) principles to enhance the hospital's workflow.

The Action Research methodology was used to implement the project and develop the case study. The study took place in a large tertiary care academic hospital in India. The Define-Measure-Analyse-Improve-Control approach optimised the workflow within 6 months. Lean tools such as value stream mapping (VSM), waste audits and Gemba were utilised to identify issues involving various stakeholders in the workflow. Sigma-level calculations were used to compare baseline, improvement and sustainment status. Additionally, statistical techniques were effectively employed to draw meaningful inferences.

LSS tools and techniques can be effectively utilised in large tertiary care hospitals to optimise workflow through a structured approach. Sigma ratings of the processes showed substantial improvement, resulting in a five-fold increase in clinical outcomes. Specifically, there was a 43% improvement in outcome for patients who underwent acute stroke revascularisation. However, certain sigma ratings deteriorated during the control and extended control (sustainment) phases. This indicates that ensuring the sustainability of quality control interventions in healthcare is challenging and requires continuous auditing.

The article presents a single case study deployed in a hospital in India. Thus, the generalisation of outcomes has a significant limitation. Also, the study encounters the challenge of not having a parallel control group, which is a common limitation in quality improvement studies in healthcare. Many studies in healthcare quality improvement, including this one, are limited by minimal data on long-term follow-up and the sustainability of achieved results.

This study pioneers the integration of LSS methodologies in a large Indian tertiary care hospital, specifically targeting AS intervention. It represents the first LSS case study applied in the stroke department of any hospital in India. Whilst most case studies discuss only the positive aspects, this article fills a critical gap by unearthing the challenges of applying LSS in a complex healthcare setting, offering insights into sustainable quality improvement and operational efficiency. This case study contributes to the theoretical understanding of LSS in healthcare. It showcases its real-world impact on patient outcomes and process optimisation.