Hospitals

Healthcare systems operate in safety-critical environments where rapid adaptation and sustained functioning must occur simultaneously, yet existing safety frameworks tend to conceptualise agility and resilience as separate, sequential, or retrospective capabilities. This conceptual separation limits understanding of how safety is enacted during disruption, when healthcare workers and organisations must respond in real time without temporal or structural buffers. This paper introduces agilience as an emerging conceptual construct that captures the concurrent enactment of agility (rapid adaptation) and resilience (sustained functioning, recovery, and learning) under conditions of uncertainty. Drawing on safety science, resilience engineering, organisational theory, and comparative industry literature, this conceptual commentary clarifies how agilience extends existing Safety-I and Safety-II paradigms by addressing the temporal gap between prevention-focused and learning-focused approaches. Agilience is positioned as both an explanatory lens and an aspirational organisational state, highlighting the alignment required between individual adaptive capability and organisational structures to support safe, sustainable care delivery. The paper outlines the defining features, boundaries, and system conditions under which agilience becomes visible, and illustrates its relevance through healthcare examples. By articulating agilience as a distinct conceptual contribution, this work provides a foundation for future empirical investigation, measurement development, and application in healthcare safety management. Full article

Health systems face pressure to strengthen resilience against supply chain disruptions while maintaining cost-effective service delivery. This mixed-methods study describes a pilot project that integrated 3D printing services into a Canadian provincial health authority. Quantitative data were derived from internal clinical engineering work orders, where a scenario-based economic analysis compared original equipment manufacturer (OEM) procurement with modelled 3D-printed parts. Using conservative assumptions, selected non-electronic structural parts were assigned a fixed unit cost. Qualitative data were collected from two focus groups with clinical engineers and other end-users. Results from an exploratory scenario-based economic analysis suggest that substituting selected structurally simple clinical engineering parts with 3D-printed alternatives would be associated with modelled cost impacts ranging from a 67.4% net increase (OEM prices halved and 3D-printing costs doubled) to a 69.6% cost reduction (OEM prices increased by 10% and 3D-printing costs decreased by 20%). Demand changes affected absolute savings but not the percent difference (58.1% under ±50% quantity changes), and a pessimistic procurement scenario (OEM prices decreased by 30% and 3D-printing costs increased by 50%) reduced savings to 10.3%. Focus groups highlighted perceived benefits and implementation challenges associated with integrating additive manufacturing. Implementation was facilitated through an outsourcing model, which was perceived to shift certain responsibilities and risk-management functions to the vendor. Long-term adoption will require clearer communication and targeted education. This pilot study suggests that, under constrained regulatory scope and scenario-based assumptions, additive manufacturing may contribute to supply chain resilience and may be associated with modelled cost advantages for selected low-risk components. Full article

Effective communication in critical care units, such as the Cardiovascular Intensive Care Unit (CVICU), is vital for patient safety; however, clinical notes from multiple professionals are often lengthy and complex. This study evaluated the Mistral large language model for summarizing Cardiovascular Intensive Care Unit progress notes using the Illness severity, Patient summary, Action list, Situation awareness and contingency planning, and Synthesis by receiver (I-PASS) framework, a standardized mnemonic for patient handoffs in healthcare. A total of 385 patients were included in the cohort, and all the progress notes associated with each patient were combined into a single document and summarized by the model. The readability was assessed using multiple metrics, including Flesch Reading Ease, Flesch-Kincaid Grade Level, Gunning-Fog Index, Simple Measure of Gobbledygook Index (SMOG), Automated Readability Index, and Dale-Chall Score. The readability metrics showed that the summaries generated with the Mistral Large Language Model (LLM) were much more difficult to read than the original notes, requiring a higher reading level. In a small clinician review, junior residents rated the summaries overall more favorably than senior residents, who often identified missing clinical details. Although Mistral condensed the documentation, this reduced readability and some loss of context may limit its usefulness for clinical handoffs. As a preliminary study with a small clinician-reviewed sample, these findings are descriptive and will require validation in larger clinical settings. Full article

The increasing availability of free-text components in electronic medical records (EMRs) offers unprecedented opportunities for machine learning research, enabling improved disease phenotyping, risk prediction, and patient stratification. However, the use of narrative clinical data raises distinct ethical challenges that are not fully addressed by conventional frameworks for structured data. We conducted a narrative review synthesizing conceptual and empirical literature on ethical issues in free-text EMR research, focusing on privacy, fairness, autonomy, interpretability, and governance. We examined technical methods, including de-identification, differential privacy, bias mitigation, and explainable AI, alongside normative approaches, such as participatory design, dynamic consent models, and multi-stakeholder governance. Our analysis highlights persistent risks, including re-identification, algorithmic bias, and inequitable access, as well as limitations in current regulatory guidance across jurisdictions. We propose ethics-by-design principles that integrate ethical reflection into all stages of machine learning research, emphasize relational accountability to patients and stakeholders, and support global harmonization in governance and stewardship. Implementing these principles can enhance transparency, trust, and social value while maintaining scientific rigor. Ethical integration is therefore not optional but essential to ensure that machine learning research using free-text EMRs aligns with both clinical relevance and societal expectations. Full article

This Perspective presents a framework for US hospitals treating foreign patients to reconceptualize international healthcare trade by leveraging all four modes of trade in health services under the General Agreement on Trade in Services (GATS), which include information exchange (Mode 1), patient travel/medical tourism (Mode 2), commercial presence (Mode 3), and temporary movement of healthcare personnel (Mode 4). This framework illustrates how hospitals could adopt multi-modal approaches and describes the strategic implications for hospitals and their international patient programs. Historically, US hospitals have focused primarily on international patient travel (Mode 2), but this narrow approach creates vulnerability to disruption. Mode 2 exports by US hospitals have not recovered to pre-pandemic levels, making expansion into other modes essential for maintaining competitive advantages while mitigating systemic risks. Diversification into other modes, such as digital health and telemedicine (Mode 1), co-branding and managing facilities (Mode 3) and visiting professorships (Mode 4) are single-mode approaches for diversification. Multi-country clinical trials are an example of cross-border trade that addresses all four modes of GATS. Overall, this perspective provides a new framework for US providers engaged in or considering entry into international markets that does not solely rely on Mode 2 medical tourism but instead adopts a multi-modal, cross-border health service paradigm. Full article

Background: Artificial Intelligence (AI) holds significant potential to enhance operational efficiency and quality in healthcare. However, despite substantial investment, its widespread, sustained implementation is limited, necessitating a thorough risk assessment to overcome current adoption barriers. Methods: This scoping review, guided by the Arksey and Malley framework, systematically mapped 13 articles published between 2019 and 2024, sourced from five major databases (including CINAHL, Medline, and PubMed). A rigorous, systematic process involving independent data charting and critical appraisal, using the Critical Appraisal Skills Programme (CASP) tool, was implemented, followed by thematic synthesis to address the research questions. Results: AI demonstrates a significant positive impact on both operational efficiency (e.g., optimised resource allocation, reduced waiting times) and patient outcomes (e.g., improved patient-centred, proactive care, and identification of readmission risks). Major implementation hurdles identified include high costs, critical data security and privacy concerns, the risk of algorithmic bias, and significant staff resistance stemming from limited understanding. Conclusions: Healthcare managers must address key challenges related to cost, bias, and staff acceptance to leverage the potential of AI fully. Strategic investments, the implementation of robust data governance frameworks, and comprehensive staff training are crucial steps for mitigating risks and creating a more efficient, patient-centred, and effective healthcare system. Full article

Technological advancements driving smart healthcare transformation need new models and solutions for emerging technology challenges. The objective of this review paper is to introduce the concept of smart healthcare, identify its main characteristics, highlight the key drivers of its adoption (“Technological Advancements, Digital Citizen Societies, Shifting Models of Patient Care, Healthcare Workforce Shortages, Rising Costs of Healthcare Delivery, and Impacts of COVID-19”), and present the primary challenges associated with its implementation (“Reduced Human Interaction and Patient Monitoring, Data Accuracy and Reliability, Data Security and Privacy, Interoperability and System Performance, Ethical Concerns and Trust in AI, High Financial Costs”). The paper is written in simplified language to enable a wide range of healthcare stakeholders—particularly healthcare professionals with limited technical backgrounds—to develop a foundational understanding of smart healthcare. This knowledge can foster greater engagement in efforts to transform healthcare systems into smarter, more efficient models. Furthermore, the findings of this review may support future research efforts, especially those aimed at developing models or frameworks that facilitate the practical integration of smart healthcare beyond theoretical concepts, by offering a synthesized framework for SHC. Full article

Hospitals are complex systems that function most effectively when operations are coordinated and supported by real-time information and feedback loops. Sustained growth, quality improvement, and financial viability increasingly rely on data-based management (DBM), yet adoption and use vary widely across healthcare institutions. This study examined the enabling and hindering factors influencing DBM, with the aim of generating insights to strengthen data use and improve management of eye hospitals. A qualitative multiple case study design was employed in six purposefully selected eye hospitals in India, varying in size and baseline capacity for DBM. At each site, five to six key personnel were interviewed. Data collection involved audio-recorded interviews, transcripts, and field notes, and analysis followed a grounded theory approach using open and axial coding to identify themes, relationships, and develop a conceptual framework. Findings reaffirmed the core enablers—leadership commitment, data availability, and technology adoption. Additional drivers included operational adaptability, regulatory demands, systematic improvement practices, daily reporting, information policies, and the use of communication platforms such as WhatsApp. Key barriers were incomplete data entry, software limitations, inadequate analytical reporting, and inconsistent adherence to processes. Overall, effective DBM requires both foundational enablers and contextual drivers, while addressing barriers to institutionalizing data use and improving outcomes. Full article

Health systems function optimally when accountability principles, legal frameworks, and governance processes are clearly defined, understood, and implemented. Together these elements set norms, standards, and systems for the practice of health professionals, facilities, products, and the provision of quality and safe care. Ghana, like other countries, has these elements in place but could be more effective. When the system fails, the consequences of a lack of accountability are widespread, significant, and impact the poor and vulnerable the hardest. Achieving accountability for the legal and social expectation of high-quality, safe healthcare is an ongoing challenge, for every country, not just Ghana. Hence, a key dual question arises: within Ghana, how can health system accountability be enhanced through examining legal frameworks and their implementation? The following six key elements are identified to promote accountability in health systems: establish and implement effective healthcare governance arrangements; capacity development, understanding, and delineation of stakeholder roles and responsibilities; appropriate financing and resourcing; establishing and maintaining effective management of required infrastructure; undertaking measurement for accountability; and focusing on people-centered care. A clear focus on these six elements enables the delivery of equitable, high-quality, safe care for the population and a better future for all. Full article

Person-centred cultures are increasingly recognised as essential to the delivery of compassionate, safe, and effective healthcare. While Lean Six Sigma (LSS) is widely adopted as a process improvement methodology, its application is often critiqued for lacking alignment with relational or values-based care. This study aimed to test the transferability of three previously developed Programme Theories (PTs), generated through realist inquiry in a public hospital setting, within a large private acute hospital. Realist-informed adjudication workshops were conducted with interdisciplinary staff who had completed university-accredited training in LSS. Structured workbooks, visual artefacts, and thematic synthesis were used to identify how context–mechanism–outcome configurations (CMOCs) held, shifted, or evolved in the new setting. All three PTs were confirmed, with six CMOCs refined, and eight new configurations generated. Key refinements included the role of strategic intent, informal improvement communities, and intrinsic motivation. These findings suggest that values-based mechanisms underpinning person-centred LSS are not confined to public systems and may be equally active in private settings. The study confirms the explanatory strength of the original PTs while contributing new insights into their adaptability. It offers practical guidance for healthcare leaders seeking to embed person-centred improvement approaches across diverse systems, regardless of sectoral funding or governance structures. Full article

The challenge for healthcare policy makers, managers and practitioners is finding ways to effectively collaborate with patients and community to plan, deliver and evaluate services. The study examined how managers engage the community with the strategic redesign of health services. The study focused on four large scale redevelopment projects, valued at A$2.8B, occurring within a health district in New South Wales, Australia. The study employed a multiple qualitative methods design comprising semi-structured interviews and focus groups. Participants were professionals (n = 24) involved in the strategic planning of health facility redevelopment. Thematic analysis was used to identify, analyse and report findings. Three issues emerged as significant factors influencing engagement, including the following: establishing a new mindset to service planning and delivery; future proofing service delivery; and management of stakeholder expectations. The unique contribution of the research is the identification of three interwoven strategies with 30 actions proposed to assess, understand and respond to external factors: 1. Foster an environment that allows for flexible and adaptable thinking and discussion; 2. Develop systems, structures and processes that facilitate engagement; 3. Encourage systems thinking for effective continuous service provision and redevelopment. Large scale redevelopment projects provide a platform for the strategic redesign of health services. When doing so, engaging the community with strategic planning, implementation and evaluation of healthcare services can lead to improved care outcomes. Full article

Understanding the utilization patterns of nuclear medicine (NM) services is essential for optimizing resource allocation and service provision. This study aimed to address the regional evidence gap by reporting the demand for NM services by sex and age at a public hospital in Jamaica. This was a non-experimental, retrospective study of NM scans that were completed at the University Hospital of the West Indies from 1 June 2022 to 31 May 2024. While all scans were reported in the descriptive totals, for patients with multiple scans during the study period, only the data from the first visit was used in the inferential statistical analysis. This was performed with the IBM SPSS (version 29.0) software and involved the use of chi-square goodness of fit and multinomial logistic regression. A total of 1135 NM scans for 1098 patients were completed (37 patients had more than one scan); 596 (54.3%) were female and 502 (45.7%) were male, with the ages ranging from 3 days to 94 years old. Among the female patients, there was a greater demand in the ≥60 years age group for cardiac amyloid scans (χ² = 6.40, p < 0.05), while females 18–59 years had a greater demand for thyroid scans (χ² = 7.714, p < 0.05) and bone scans (χ² = 3.904, p < 0.05). On the other hand, significantly more males in the ≥60 age group presented for cardiac amyloid (χ² = 4.167; p < 0.05) and bone scans (χ² = 145.79, p < 0.01). Males were significantly less likely to undergo a thyroid scan than females (p < 0.01, OR = 0.072, 95% CI: 0.021, 0.243) while individuals aged 18–59 years were more likely to undergo this scan than patients aged 60 or older (p = 0.02, OR = 3.565, 95% CI: 1.258, 10.104). Males were more likely to do a cardiac amyloid scan (p < 0.05, OR = 2.237, 95% CI: 1.023, 4.891) but less likely to undergo a cardiac rest/stress test than females (p = 0.02, OR = 0.307, 95% CI: 0.114, 0.828). Prolonged life expectancy and an aging population have the potential to impact NM utilization, thus requiring planning for infrastructure, equipment, work force, and supplies. Cancer-related and cardiovascular indications are a top priority at this facility; hence, age- and sex-specific analysis are useful in establishing models for policy makers with regard to the allocation of economic and human resources for the sustainability of this specialized service. Full article

Objective: This study aimed to develop a performance evaluation index system for a district-level public hospital in Chongqing, China, based on Diagnosis-Related Groups (DRGs), to provide a benchmark for performance assessment in similar hospitals. The system was constructed using a literature analysis, the Delphi method, and the Analytic Hierarchy Process (AHP) to identify and weight relevant indicators. Results: The evaluation system consists of three primary indicators and eighteen secondary indicators. Key secondary indicators include the Case Mix Index (CMI), cost consumption index, low-risk group mortality rate, the proportion of patients with three- or four-level surgeries at discharge, and the proportion of medical service revenue to medical income. In 2020, significant improvements were observed in several indicators, such as a decrease in the low-risk group mortality rate to 0% and increases in the proportion of patients with three- or four-level surgeries and CMI by nearly 10% and 13%, respectively. Conclusions: This study successfully developed a comprehensive and scientifically sound performance evaluation index system for a district-level public hospital in Chongqing. The system has proven effective in objectively assessing inpatient medical care performance and providing valuable guidance for improving healthcare services in similar settings. Full article

Hospital layouts play a critical role in supporting efficient care processes, which are continually evolving. While care processes adapt over time, the spatial needs of care professionals are expected to remain relatively stable. This study proposes an evaluation framework combining cognitive mapping and space syntax analysis (SSA) to assess how hospital layouts align with these spatial needs. The framework is applied to a real-world emergency department (ED) with two distinct layout configurations. Cognitive mapping captures spatial needs from the perspective of care professionals, while SSA evaluates how the layout supports or constrains these needs. Drawing on the open building approach, we interpret layout adaptability through a layered system of primary (rigid), secondary (adaptable), and tertiary (care process) levels. Our results show that the choices in primary and secondary system designs can limit the functionality of the tertiary system. This approach supports informed decision-making by addressing multiple spatial needs simultaneously, offering insights into the coherence between spatial configuration and care delivery, and enabling quantitative comparison across different layout designs. Full article

Background: The emergency department (ED) often represents the entry point to care for patients that require urgent medical attention or have no alternative for medical treatment. This has implications on scope of practice and how quality of care is measured. A diverse array of methodologies has been developed to evaluate the quality of clinical care and broadly includes quality improvement (QI), quality assurance (QA), observational research (OR) and clinical quality registries (CQRs). Considering the overlap between QI, QA, OR and CQRs, we conceptualized a modular framework for TECOR to effectively and efficiently streamline clinical quality evaluations. Streamlining is both appropriate and justified as it reduces redundancy, enhances clarity and optimizes resource utilization, thereby allowing clinicians to focus on delivering high-quality patient care without being overwhelmed by excessive data and procedural complexities. The objective of this study is to describe the process for designing a modular framework for ED CQRs using TECOR as a case study. Methods: We performed a scoping audit of all quality projects performed in our ED over a 1-year period (1 January 2021 to 31 December 2021) as well as data mapping and categorical formulation of key themes from the TECOR dataset with clinical data sources. Both these processes then informed the design of TECOR. Results: For the audit of quality projects, we identified 29 projects. The quality evaluation methodologies for these projects included 12 QI projects, 5 CQRs and 12 OR projects. Data mapping identified that clinical information was fragmented across 11 distinct data sources. Through thematic analysis during data mapping, we identified three extraction techniques: self-extractable, manual entry and on request. Conclusions: The modular framework for TECOR aims to enable an efficient streamlined approach that caters to all aspects of clinical quality evaluation to enable higher throughput of clinician-led quality evaluations and improvements. TECOR is also an essential component in the development of a learning health system to drive evidence-based practice and the subject of future research. Full article

This study applies stochastic discrete event modeling to demonstrate that reducing wait times for specialized outpatient clinics in the Costa Rican public healthcare system is possible. The classification process identified four medical specialties with the longest wait times. It includes the creation of a separate queuing theory model for each specialty. The birth and death model allowed for estimating the number of arrivals and consultations in the simulation. Validation was performed by comparing the model’s input and output data with real-world statistical reports. An analysis of medical specialists revealed that approximately 22% of patients referred to secondary care did not require specialized medical consultation. Through simulation and the use of stochastic input data, patient waiting times decreased. In an optimistic scenario, waiting times decreased steadily across all specialties over 24 months. Ophthalmology and orthopedics reduced their waiting times to less than 300 days. Otorhinolaryngology decreased from 370 to 250 days, and urology showed the most significant improvement, decreasing from 350 to 100 days in the first year and remaining stable. This evidence transforms the traditional paradigm of increasing capacity as the only solution to the waiting list problem and positions improving the referral process as an alternative. To achieve these results, the study highlights the importance of implementing improved triage protocols in primary care, integrating decision-support tools for general practitioners using machine learning, for example, to reduce unnecessary referrals. Training programs and feedback mechanisms could also align referral practices with specialty criteria. While these strategies were not implemented in this study, the simulation results provide a solid basis for their design and future evaluation. Full article

This study analyzes the comparative performance of ten hospitals in the Kadutu Health Zone in the Democratic Republic of Congo using the EGIPSS model. This study was carried out at the height of the COVID-19 pandemic in August and September 2021, in a changing global context where health systems were called upon to improve their resilience capacity while maintaining high levels of performance. This is a descriptive observational study using documentary review, interviews with 85 key informants, and participatory observation at ten hospitals selected based on several criteria, including the organization of a complete complementary package of activities assigned to a hospital in the DR Congo. This study mainly reveals three facts, namely that (i) university hospitals show the best performance, (ii) adaptive capacity considerably influences the other dimensions of the EGIPSS model and the overall performance of the hospital, and (iii) to adapt, hospitals need resources and good management and governance. Adapting hospitals in the Kadutu Health Zone to the changing context requires a holistic approach that combines clinical work with research, investments in infrastructure (often dilapidated and not modern), training, technology, and governance. It also involves learning from practices implemented in more efficient hospitals. Full article

The majority of Australians who die each year do so in an acute hospital setting and are cared for during the end of their life by a multidisciplinary team comprising nurses, medical staff, and allied health staff. Despite the range of professional disciplines that services this patient group, the experiences of the staff providing this end-of-life care is not well understood. This study sought to explore the experiences of multidisciplinary staff providing this care at an acute hospital in Sydney, Australia and to identify the barriers that affect the end-of-life care provided. Data were collected through an online survey from a multidisciplinary sample group. A combination of statistical analysis and thematic analysis was used to analyse the data with four key themes emerging. These themes included the implications for staff working in end-of-life care, communication gaps in the acute hospital setting, recognition of the dying process, and improvement of end-of-life care through further education. This study highlighted the challenges experienced by healthcare staff in the end-of-life context, with recommendations provided for increased education and training. The need for staff to receive training with a focus on end-of-life skill development, professional confidence, and preparedness for end-of-life conversations was highlighted. Full article

Healthcare is a dynamic and ever-changing phenomenon and is subject to multiple challenges, particularly concerning sustainability and cost issues. The literature identifies bed space and problems related to the lack of hospital beds as being directly or indirectly related to both admission and discharge processes, with delays in in-patient discharges being identified as a variable of significance when it comes to a health system’s overall performance. In this respect, the aim of this research was to explore factors related to delayed discharges in an acute hospital setting in Malta, a small European member state, through the perspectives of health professionals. This study followed a qualitative approach. Semi-structured interviews (n = 8) and focus groups (n = 2) were conducted with a diverse group of experienced health professionals. Informed consent was obtained from all participants, and all data were treated with strict confidentiality throughout the study. The sample was limited to professionals working in adult, non-specialized healthcare settings. Manual thematic analysis was carried out. Codes were grouped to derive seven main themes, which were identified after carrying out the thematic analysis process on the transcripts of the interviews/focus groups. The derived themes are the following: (a) a faulty system, which is open to abuse and inefficiency, (b) procedural delays directly impacting delayed discharges, (c) long-term care/social cases as a major cause of delayed discharges, (d) the impact of external factors on delayed discharges, (e) stakeholder suggestions to management to counteract delayed discharges, (f) the impact of COVID-19 on delayed discharges, and (g) inter-professional relationships. Factors related to delayed discharges and the effects of delayed discharges on the hospital emerged from the main findings, together with specific potential interventions to minimise delays in discharge. Health professional interactions and the effects of inter-professional relationship setbacks on delayed discharges were explored, and the impact of the COVID-19 pandemic on hospital dynamics and additional delays were also addressed. This information is intended to provide hospital administrators with data-driven internal organisational evidence to guide them through changes and to inform future decisions regarding hospital performance and efficiency from a discharge delay perspective. Full article