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Hearing voices can be a debilitating and traumatic experience, and psychiatric hospitals can feel unsafe and overstimulating to voice hearers. Research suggests this may prolong a service user’s admission time and lead to an unhelpful experience. Therefore, a hearing voices group (HVG) was developed to create a safe space where voice hearers could share their story with others with lived experience and access support.

The group was developed by the first author under the supervision of the inpatient psychology lead. Both a practice-based and expert by experience-based approach were considered during the development of the group structure. The group also aligned with the Hearing Voices Network and the Trust’s values. A questionnaire was developed to evaluate the effectiveness of the group using six five-point Likert scale questions and three open questions to collect the data, which was then analysed.

The themes from the qualitative data showed that staff and ward-based promotion of the group were paramount to ensuring patient engagement. The results also showed that voice hearers found the group therapeutic, and some found the coping skills shared to be beneficial and effective.

A large percentage of women (76%) reported that they had attended a HVG before. This was not the case for service users from the male unit and psychiatric intensive care unit. This result is considered to reflect the fact that women in that unit had the opportunity to attend more than once during their admission, rather than that they had accessed these groups in other settings or in the community. Therefore, in the future, it would be useful to change this question to “have you attended any other HVGs in the past, prior to this admission?”, which might be more appropriate for data collection.

To the best of the authors’ knowledge, this is the original work of the first author, who is an expert by experience and an assistant psychologist. The results suggest that HVGs can be beneficial for patients in acute and intensive mental health care and highlight some necessary adaptations and the importance of adopting an MDT approach in promoting therapeutic groups.

This study aims to analyze a conspicuous corpus of literature related to the field of technology-based intensive care research and to develop an architecture model of the future smart intensive care unit (ICU).

Papers related to the topics of electronic health record (EHR), big data, data flow and clinical decision support in ICUs were investigated. These concepts have been analyzed in combination with secondary use of data, prediction models, data standardization and interoperability challenges. Based on the findings, an architecture model evaluated using MIMIC III is proposed.

Research identified issues regarding implementation of systems, data sources, interoperability, management of big data and free text produced in ICUs and lack of accuracy of prediction models. ICU should be treated as part of a greater system, able to intercommunicate with other entities.

The research examines the current needs of ICUs in interoperability and data management. As environment changes dynamically, continuous assessment and evaluation of the model with other ICU databases is required.

The proposed model improves ICUs interoperability in national health system, ICU staff intercommunication, remote access and decision support. Its modular approach ensures that ICUs can have their own particularities and specialisms while ICU functions provide ongoing expertise and training to upgrade its staff.

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.

The purpose of this paper is to demonstrate the close relationship between the disciplines of law and health-care studies. This interrelation has become particularly evident during the spread of the COVID-19 pandemic, when restrictive human rights provisions have been initiated by many states for the sake of public health. Research focuses on the notional proximity of the principle of proportionality and its health-care correlative: effectiveness. It also goes through the influence of acceptance rates for the application of restrictive measures.

Research focuses on interdisciplinary literature review, taking into consideration judicial decisions and data on acceptance rates of restrictive human rights measures in particular. Analysis goes in depth when two categories of restrictive human rights measures against the spread of the pandemic are examined in depth: restrictive measures to achieve social distancing and mandatory vaccination of professional groups.

Restrictive human rights measures for reasons of public health are strongly affected by the need for effective health-care systems. This argument is verified by judicial decision-making which relies to the necessity of health-care effectiveness to a great extent. The COVID-19 pandemic offers a laminate example of the two disciplines’ interrelation and how they infiltrate each other.

Further implications for research point at the need to institutionalize a cooperative scheme between legal and health-care decision-making, given that this interrelation is strong.

The originality of this paper lies on the interdisciplinary approach between law and health-care studies. It explains how state policies during the pandemic were shaped based on the concepts of effectiveness and proportionality.

This paper aims to explore the antecedents and consequences of service chain flexibility (SCF) in healthcare service delivery.

A structural model was developed based on a literature review. A 29-indicator questionnaire was circulated among service providers in the healthcare system across India, and 253 valid responses were received, corresponding to a response rate of 46%. The research model was assessed using a cross-sectional research design, and the data were analyzed by structural equation modeling using analysis of moment structures (AMOS) software.

Service orientation (SO), technology integration (TI), knowledge sharing (KS) and supply chain integration (SCI) were identified as antecedents of SCF, the consequence of which is responsiveness in service delivery (RSD). Furthermore, patient-centered care moderates the relationship between SCF and RSD.

This paper highlights the impact of SCF on RSD in healthcare organizations. Consideration of the four constructs of SO, TI, KS and SCI as antecedents of SCF and, in turn, RSD may be one of the limitations. Future work may identify other theoretical constructs with potential impacts on SCF and RSD. Furthermore, eight months for data collection could have resulted in early-late response bias. This study was operationalized in India and may reflect political, economic, social, technological, environmental and legal factors unique to India.

The study provides suggestions to practitioners for building RSD by leveraging SO, TI, KS and SCI in flexibility-driven service chain processes. Recognizing the relationships among these constructs can aid in the timely formulation of corrective actions and patient-centric policies.

This paper highlights how focusing on a SCF can promote RSD. This understanding may aid the design of processes that develop patient-centricity and deliver health as a social good in an effective manner.

The empirical evidence from this study can help hospitals integrate and build flexibility in their functions, thus enabling them to deliver responsiveness in care.

This study aims to explore the management learning during a long-term crisis like a pandemic. The paper addresses both what health-care managers have learnt during the COVID-19 pandemic and how the management learning is characterized.

The paper is based on a qualitative case study carried out during the COVID-19 pandemic at two different public hospitals in Sweden. The study, conducted with semi-structured interviews, applies a combination of within-case analysis and cross-case comparison. The data were analyzed using thematic deductive analysis with the themes, i.e. sensemaking, decision-making and meaning-making.

The COVID-19 pandemic was characterized by uncertainty and a need for continuous learning among the managers at the case hospitals. The learning process that arose was circular in nature, wherein trust played a crucial role in facilitating the flow of information and enabling the managers to get a good sense of the situation. This, in turn, allowed the managers to make decisions meaningful for the organization, which improved the trust for the managers. This circular process was iterated with higher frequency than usual and was a prerequisite for the managers’ learning. The practical implications are that a combined management with hierarchical and distributed management that uses the normal decision routes seems to be the most successful management method in a prolonged crisis as a pandemic.

The gained knowledge can benefit hospital organizations, be used in crisis education and to develop regional contingency plans for pandemics.

This study has explored learning during the COVID-19 pandemic and found a circular process, “the management learning wheel,” which supports management learning in prolonged crises.

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.

A new triage method, MBCE (Medical Bio Social Ethics), is presented with social justice, bio, and medical ethics for critical resource distribution during a pandemic. Ethical triage is a complex and challenging process that requires careful consideration of medical, social, cultural, and ethical factors to guide the decision-making process and ensure fair and transparent allocation of resources. When assigning priorities to patients, a clinician would evaluate each patient’s medical condition, age, comorbidities, and prognosis, as well as their cultural and social background and ethical factors.

A statistical analysis shows no interactions among the ethical triage factors. It implies the ethical components have no moderation effect; hence, each is independent. The result also points out that medical and bioethics may have an affinity for interactions. In such cases, there seem to be some ethical factors related to bio and medical ethics that are correlated. Therefore, the triage team should be careful in evaluating patient cases. The algorithm is explained with case histories of the selected patient. A group of triage nurses and general medical practitioners assists with the triage.

The MBCE triage algorithm aims to allocate scarce resources fairly and equitably. Another ethical principle in this triage algorithm is the principle of utility. In a pandemic, the principle of utility may require prioritizing patients with a higher likelihood of survival or requiring less medical care. The research presents a sensitivity analysis of a patient’s triage score to show the algorithm’s robustness. A weighted score of ethical factors combined with an assessment of triage factors combines multiple objectives to assign a fair triage score. These distinctive features of the algorithm are reasonably easy to implement and a new direction for the unbiased triage principle.

The idea is to make decisions about distributing and using scarce medical resources. Triage algorithms raise ethical issues, such as discrimination and justice, guiding medical ethics in treating patients with terminal diseases or comorbidity. One of the main ethical principles in triage algorithms is the principle of distributive justice.

A region’s transforming care partnership identified that autistic adults without an intellectual disability (ID) may be falling through gaps in services when presenting with a significant emotional and/or behavioural need in the absence of a mental health diagnosis. The region’s intensive support teams (ISTs) for adults with ID therefore piloted a short-term “behavioural support service” for this population. The purpose of this paper is to evaluate this pilot.

This study represents a mixed-methods service evaluation over a four year pilot period. The quantitative component examined referral rates and demographic data of accepted and declined referrals; and length of referral episodes and Health of The Nation Outcomes Scores (HoNOS) for accepted referrals. The qualitative component used thematic analysis to identify key themes relating to reasons for referral, clinical/therapeutic needs, and the models of support that most informed assessments and interventions at individual and systems levels.

The ISTs accepted 30 referrals and declined 53. Most accepted referrals were male (83%), and under 24 years old (57%). Average HoNOS scores were above the thresholds generally associated with hospital admission. Key qualitative themes were: transitional support; sexual risks/vulnerabilities; physical aggression; domestic violence; and attachment, trauma and personality difficulties. Support mostly followed psychotherapeutic modalities couched in trauma, attachment and second- and third-wave cognitive behavioural therapies. Positive Behaviour Support (PBS) did not emerge as a model of preference for service users or professionals.

This project represents one of the first of this type for autistic adults without an ID in the UK. It provides recommendations for future service development and research, with implications for Transforming Care policy and guidance.

The objective of this study was to delineate and compare enteral nutrition (EN) practices among neonatal units across the Arabian Gulf countries.

A cross-sectional study was conducted by recruiting 255 clinicians working in neonatal units in the Arabian Gulf countries.

Out of 255 invited clinicians, 73 (29%) participated in the survey. Neonatal units used varied EN strategies, where feeding practices exhibited variability. The majority (74%) of units had a local standard feeding protocol, while 18% followed international protocols, and 8% did not adhere to a specific protocol. When maternal milk was not used, the main alternatives were preterm formula (67%) and predigested formula (14%). The age at which the first EN was commenced and the reported advancement rate showed significant variations among different units (p < 0.001). The initiation of fortification was primarily driven by reaching a specific enteral volume (commonly reported as 100 mL/kg/day) and addressing poor postnatal growth. Fortification practices did not differ significantly among professions, except for the initial fortification strength, where none of the dietitians and only 8.3% of neonatologists preferred full strength, compared to 28.6% and 21.4% of medical residents and nurses, respectively (p = 0.033).

This study marks the first exploration of EN practices in neonatal units, examining their local and cross-country variations. It provides valuable insights to guide local trials and foster global collaboration among neonatal units to establish a unified knowledge base, standardized practices and promote research and innovation, ultimately contributing to optimal feeding practices for very preterm infants.