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The primary objective of this study is to investigate the cognitive aspects of spatial experiences of paediatric inpatients who receive long-term treatment in a healthcare setting in relation to the syntactic parameters of healthcare environment. It is aimed to investigate how the change in the child’s cognition caused by the environmental stress experienced by the child during his/her stay in the hospital is related to the physical parameters of the treatment space.

The methodology of the study is based on a correlational analysis to identify the cognitive and syntactic factors of the healthcare environment that contribute to changes in the perceptual processes of a sample group of thirty children. The study examined the relationships between the graph and isovist variables, and the cognitive parameters of paediatric inpatients. The two datasets were subjected to regression analyses in order to identify any significant findings, which allowed for a discussion of how the patients’ changing perceptual processes are influenced by the syntactic measures of the healthcare setting.

The study showed that a syntactically intelligible floor plan contributes significantly to reducing environmental stress among paediatric inpatients. The presence of shared spaces within the healthcare environment, where social interaction with peers is possible, emerges as a crucial factor influencing children’s spatial perception. Additionally, the visibility characteristics of shared spaces may also play a key role in enhancing children’s perceptions of safety.

The limitations of the study include the fact that the study was conducted in an oncology and haematology inpatient unit with challenging conditions in terms of the mobility potentials of the children, which might have affected their perceptual processes. A further limitation is that the sample size comprised only 30 children, and the spatial configuration of the healthcare environment was linear and not particularly complex.

By identifying the impact of spatial design on children’s well-being, the study informs the creation and improvement of healthcare environments. Enhanced understanding of factors like intelligible floor plans, shared spaces and isovist values can lead to more child-friendly facilities, potentially alleviating stress for young patients. Consequently, this research may contribute to improved healthcare outcomes, increased comfort for paediatric inpatients, and a more supportive environment for their families, fostering a holistic approach to paediatric care and positively influencing the overall quality of life for children undergoing long-term treatment.

This study contributes to the theoretical discourse on how the constrained physical conditions of a paediatric healthcare environment may influence the perceptual processes of paediatric inpatients. The results of this evidence-based study have the potential to inform the evaluation of design guidelines for healthcare settings, with the ultimate aim of enhancing therapeutic environments.

The emergence of new generations of artificial intelligence (AI), such as ChatGPT or Copilot has brought about a wave of innovation in the service workplace. These robotic agents can serve as companions, helping employees cope with work-related stress. This research introduces the concept of “artificial companionship,” which explains how robotic agents can function as partners in assisting service employees to fulfill their job responsibilities and maintain their mental well-being.

This research uses a mixed methods approach grounded in social support theory from psychology and management to develop a conceptual framework for the stress-alleviating implications of artificial companionship. A qualitative employee survey is conducted to justify the relevance of the propositions.

This research delineates the concept of artificial companionship. It highlights four distinct roles that AI can play in companionship – instrumental, informative, caring, and intimate. Building on this foundation, the research presents a series of propositions that elucidate the potential of artificial companionship in mitigating stress among employees.

Firms should consider aligning the types of artificial companionship with the demands inherent in employees’ job responsibilities to better reinforce their resilience and sustainment in overcoming work-related challenges.

This research introduces a new perspective on artificial companionship through the lens of social support theory. It extends the current understanding of human-robot collaboration in service workspaces and derives a set of propositions to guide future investigations.

Healthcare organizations have been facing challenges due to high costs and low efficiency in health services. The growth of costs and losses caused by avoidable mistakes lead to the search for solutions, and Health Lean Management appears as a potential solution to help in solving service quality problems, as well as reducing risks. This study aims to analyse the state of the art in the literature centred on the Lean approach in the context of risk management in healthcare organizations, and to identify new research opportunities, highlighting possible lines of future research.

Following a systematic literature review approach, 51 papers were considered relevant for this research, and reviewed to explore the development of literature in this area.

Based on the results, five main research streams were identified: (1) risk management oriented towards patient safety; (2) risk management oriented towards employee safety; (3) importance of attitudes and behaviours in risk reduction projects; (4) Lean tools used in healthcare risk management and (5) Integration of approaches. Moreover, several shortcomings were identified in literature.

Identified shortcomings represent significant opportunities for further research development.

The purpose of this paper is to meet Dr Joyce Shaffer, PhD, ABPP, Clinical Associate Professor at the University of Washington.

This case study is presented in two sections: a positive autoethnography written by Joyce Shaffer, followed by her answers to ten questions.

In this positive autoethnography, Shaffer shares her life story and reveals numerous mental health and positive aging recommendations and insights for us to reflect on.

This is a personal narrative, albeit from someone who has been a clinical psychologist and active in the field of aging for many decades.

A pragmatic approach to aging is recommended. According to Shaffer, “those of us who can recognize the beat of the historical drummer can harvest the best of it and learn from the rest of it.”

Positive aging has strong social implications. Shaffer considers that it is not only about maximizing our own physical, mental, emotional and social health but also about maximizing that of others, to make our world a better place for everyone.

Positive aging can be experienced despite adversity. As Shaffer says, “Adversity used for growth and healed by love is the answer.”

A novel method has been proposed to reduce the false alarm rate of arrhythmia patients regarding life-threatening conditions in the intensive care unit. In this purpose, the atrial blood pressure, photoplethysmogram (PLETH), electrocardiogram (ECG) and respiratory (RESP) signals are considered as input signals.

Three machine learning approaches feed-forward artificial neural network (ANN), ensemble learning method and k-nearest neighbors searching methods are used to detect the false alarm. The proposed method has been implemented using Arduino and MATLAB/SIMULINK for real-time ICU-arrhythmia patients' monitoring data.

The proposed method detects the false alarm with an accuracy of 99.4 per cent during asystole, 100 per cent during ventricular flutter, 98.5 per cent during ventricular tachycardia, 99.6 per cent during bradycardia and 100 per cent during tachycardia. The proposed framework is adaptive in many scenarios, easy to implement, computationally friendly and highly accurate and robust with overfitting issue.

As ECG signals consisting with PQRST wave, any deviation from the normal pattern may signify some alarming conditions. These deviations can be utilized as input to classifiers for the detection of false alarms; hence, there is no need for other feature extraction techniques. Feed-forward ANN with the Lavenberg–Marquardt algorithm has shown higher rate of convergence than other neural network algorithms which helps provide better accuracy with no overfitting.

Nurse turnover in critical care units (CCU) significantly affects patient outcomes and health systems worldwide. To safeguard patient care quality, hospitals must address the underlying reasons for turnover and strategize to retain their skilled nursing workforce. The study proposes a prescriptive framework to reduce nurse turnover in CCUs.

In this study, the integrated methodology of Delphi-AHP-Entropy was used for the comparative prioritization of factors and subfactors that influence nursing staff turnover in CCUs.

Study findings reveal that “Organizational factors” and “Individual factors” dictate critical care nurse attrition rate. At the subfactor level, staffing policy, chronic fatigue, and perceived career are the leading concerns for the decision of nurses whether to work or leave.

This study is valuable for both researchers and healthcare professionals. It examines whether actions related to nurse retention align with existing theory and identifies areas requiring further theoretical or applied studies to enhance understanding in this area. This insight can bolster the field’s knowledge base and integrate theoretical and applied knowledge effectively. Additionally, for healthcare professionals, the study provides an overview of key factors conducive to retaining nursing staff in the CCU, offering valuable guidance for implementing effective strategies.

This study uniquely positions itself by presenting a comprehensive and prescriptive framework for critical care nurse retention in the UAE.

Indoor environmental quality (IEQ) in buildings has an impact on people’s health, productivity and comfort. Maintaining the highest possible IEQ level in complex buildings, such as health care, is difficult due to economic and organizational constraints. This study aims to categorize the vicinities in a typical health-care facility in terms of importance and criticality in relation to the various IEQ factors, as well as to develop an IEQ assessment procedure.

A comprehensive literature review, established standards and structured interviews with industrial hygiene professionals in health-care settings were used in this study. To test the applicability of the developed IEQ assessment procedure, a pilot study was conducted in an existing health-care facility.

This study categorized health-care facilities into various vicinities and discovered three respondents group had varying IEQ perceptions (facility managers, environmental health specialists and nurses). According to the findings, indoor air quality is the most important and dominant factor influencing overall IEQ in health-care facilities. The trial application of the framework shows that much work is needed to improve the level of response and readiness of facility management and occupants to allow for the effective use of the developed procedure.

Previous research did not include a detailed categorization of vicinities in health-care buildings based on IEQ requirements. The findings of this study will help to close this knowledge gap and guide facility managers and operators in recognizing the relative importance of different IEQ factors, maintaining functional requirements and identifying priorities when developing maintenance and operational procedures and allocating resources.

Work engagement denotes to a state of positive and affective motivation of elevated power joined with high educational levels and a great concentration on occupation and it is negatively affected by psychological problems. This study aims to assess the stress and work engagement among Palestinian nurses working in intensive care units.

A cross-sectional design was adopted. A convenience sample consisting of 225 intensive care nurses from governmental hospitals was recruited to participate. Data collection used the Nursing Stress Scale and Utrecht Work Engagement Scale-9, and demographic data were used to gather data throughout the period from February 10 to March 30, 2023.

Findings demonstrated that nurses’ stress levels were high (M = 105.9 ± standard deviation [SD] = 11.91). Concerning work engagement, the nurses endorsed moderate work engagement levels with a mean of 3.7 (SD = 0.4). Regarding work engagement subscales, the mean of vigor was 3.7 (0.6), dedication with a mean of 3.7 (0.6) and absorption with a mean of 3.7 (0.6). Also, the findings showed that stress, gender and monthly income were predictors of work engagement (p < 0.05).

Despite the significant results, this study had the following limitations: the design was cross-sectional, which limits the ability to explore the cause and effect of the study variables, thus longitudinal studies are recommended. In addition, the study adopted a self-reported questionnaire based on participants’ understanding, culture and work context which could influence their responses.

The intensive care nurses had high stress levels and moderate work engagement. Stress, gender and monthly income were predictors of the work engagement. Thus, interventions should be encouraged to minimize stress, which has a significant influence on work engagement.

Health-care organisations face many challenges in delivering safe, high-quality services while experiencing significant pressure to increase productivity and reduce costs. In this context, hospitals have implemented lean six sigma (LSS) programmes to improve their performance. This study aims to explore the application of LSS in three different non-profit Ecuadorian hospitals to comprehend the effectiveness of the methodology under this context.

A multiple-case analysis was performed in four phases: selecting the cases, defining a data collection protocol, performing a within-case analysis of each case and performing a cross-case analysis.

This research found that the LSS application positively impacts hospital performance indicators by reducing service time. The most frequently used tools include the supplier input process output customer diagram, value stream mapping, cause-and-effect diagram, five-why analysis, Gemba walk and paired two-sample test. Lastly, the results show that the most common challenges faced were lack of top management engagement, technical training and data availability.

The study is limited by the constraint of a single Latin American country from which the cases were analysed. Collaboration with external partners, like universities, and government policies promoting training in continuous improvement methodologies are crucial for success. Academic implications stress the importance of integrating soft skills in LSS implementation and engineering education.

This study shows a multiple-case analysis of LSS in a Latin American country highlighting the most commonly used tools, their impact on performance and the challenges of implementing LSS in health-care organisations in non-profit Ecuadorian hospitals.

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.