Delayed discharge represents a persistent challenge in healthcare systems, contributing to inefficiencies in hospital bed utilization, increased costs, and reduced patient flow. In small and centralized healthcare systems, these effects may be further amplified due to limited post-acute care capacity and restricted patient redistribution. This study quantified the prevalence of inappropriate hospital days as a proxy for delayed discharge and examined their relationship with patient demographics, medical specialty, and associated costs in an acute general hospital. A quantitative analysis of 220 medical records was conducted using a modified Appropriateness Evaluation Protocol (AEP). Descriptive statistics and non-parametric tests were applied to identify significant associations between inappropriate hospital days and selected variables. According to the findings, within this intentionally enriched sample of patients with a length of stay exceeding seven days in the selected wards, nearly half of all accumulated inpatient bed days failed to meet acute necessity criteria, with the vast majority of these service backlogs stemming from placement delays in downstream rehabilitation facilities and long-term care institutions. Within this high-risk cohort, inappropriate hospital days, used here as a proxy for delayed discharge, were more common among older patients and among medical specialties than in other specialties, while no consistent relationship was observed with gender. Cost analysis (carried out over a three-month period) indicated an order-of-magnitude estimate of the acute bed day resources associated with inappropriate hospital days in the units studied. These findings are consistent with the interpretation that inappropriate hospital days, used here as a proxy indicator of delayed discharge, may be influenced by a combination of external capacity constraints and internal operational inefficiencies, although causality cannot be established from this observational design. While the findings cannot be generalised to the entire inpatient population or health system, within this intentionally enriched cohort of long stay inpatients the study highlights the need to strengthen post-acute care provision, improve discharge coordination processes, and enhance system integration to optimise hospital efficiency and patient flow in similar operational contexts.
Patient-matched implants (PMIs) enable precise anatomical reconstruction but often introduce unforeseen intraoperative challenges that can provoke stress, reduce frustration tolerance, and influence surgical decision-making. Despite the growing clinical use of PMIs, the behavioural and psychological dimensions underpinning these challenging surgeries remain underexplored. This study examined the relationship between surgeon temperament, specifically frustration tolerance threshold, patience, and adherence to planned surgical workflows during PMI procedures. A qualitative thematic study was conducted over 22 months across two academic centres and 86 private surgical practices in South Africa. Data were collected through semi-structured interviews with consultant surgeons, assistant surgeons, surgical technologists, and biomedical engineers, supplemented by direct observation and detailed field notes. Inductive content analysis, thematic coding, and descriptive quantitative trends derived from Likert-style questionnaires were used to identify behavioural patterns associated with intraoperative stress and workflow deviation. Participant reports indicated that low frustration tolerance, often expressed as impatience, was perceived to be linked to increased deviations from surgical plans, including implant modification (reported in 4.6% of the 86 practices), even when design and fit were optimal. In 2.3% of the 86 practices surveyed, surgical team members reported incidents where impatience was perceived to have compromised patient safety. Stress inoculation theory and emotional intelligence frameworks offered explanatory models for the observed behaviours. Within the limits of this exploratory qualitative study, surgeon temperament—particularly mental preparedness and frustration tolerance—emerged as a recurring theme associated with intraoperative PMI workflow adherence. Whether these factors are determinants of workflow adherence whilst using high-fidelity PMIs, or merely correlated with other unmeasured variables, remains to be tested in future quantitative research.
To identify the determinants, prevalence, and proposed solutions for potentially avoidable hospital transfers (PAHT) of residential aged care facility (RACF) residents in Australia, a scoping review was conducted using PubMed, CINAHL, Cochrane Library and Google Scholar, covering the period from 2015 to 2025. The database search identified 1350 articles, of which 43 studies met inclusion criteria. Prevalence of PAHT ranged from of 8.5% to 95.2% when defined as specific conditions manageable by an outreach service; from 2.2% to 55.6% when defined as potentially manageable with primary care; and from 11.6% to 53% when defined as an Emergency Department (ED) presentation not requiring a hospital admission. The most frequently reported determinant of PAHT pertained to the overarching theme of RACF clinical decision-making and systemic RACF practice-based factors. Establishment of, and access to, outreach services was the main proposed solution, alongside onsite General Practitioners (GP), Advance Care Planning (ACP), and capacity building for RACF nurses. Despite Australia’s diversity, our findings demonstrate patterns and similarities in prevalence, determinants and solutions. Consensus or standardisation of denominators would enhance comparability of outcomes across studies. These findings offer opportunities to reconsider solutions in response to the chronic challenge of avoidable nursing home patient transfers.
Environmental disturbances from hospital demolition and construction can aerosolise pathogenic fungal spores, particularly those of Aspergillus species, posing a serious threat to immunocompromised patients. This paper presents a structured narrative review of representative case studies to evaluate the relationship between demolition activities and airborne Aspergillus exposure, with a focus on clinical risk and environmental monitoring. Three exemplar studies were selected to illustrate high-intensity short-duration demolition, prolonged mechanical demolition, and meteorologically integrated risk assessment. By examining these cases, this review identifies gaps in current knowledge, methodological limitations, and challenges in causal attribution. The analysis supports the development of a novel conceptual framework for assessing and managing Aspergillus-related risks during hospital redevelopment, offering a structured approach to future infection prevention and control strategies. This framework is intended as a conceptual tool to support evidence-informed decision-making while acknowledging the limitations inherent in a targeted narrative review rather than a systematic synthesis.
Rural hospitals are essential access points for healthcare delivery in the United States, yet they continue to experience disproportionate rates of closure and service disruption that threaten community health, economic stability, and equity. This rapid systematic review synthesizes recent peer-reviewed evidence examining rural hospital closures and service disruptions, with emphasis on financial, policy, workforce, and performance-related factors and their downstream impacts. Guided by PRISMA methodology, four databases were searched for U.S.-based studies published between January 2024 and June 2025. Following screening and consensus-based review, 59 articles met inclusion criteria. Across studies, financial vulnerability, characterized by revenue instability, low patient volumes, unfavorable payer mix, and reliance on non-operating revenue, emerged as a dominant precursor to closure and service reductions. Policy context, particularly Medicaid expansion status, telehealth and broadband infrastructure, and reimbursement adequacy, strongly shaped hospital sustainability. Closures and service disruptions were consistently associated with increased travel distances, reduced access to maternal, surgical, mental health, and chronic care services, higher prices at surviving hospitals, and increased strain on remaining providers. Workforce shortages further compounded these challenges. Collectively, findings demonstrate that rural hospital closures reflect interconnected structural weaknesses rather than isolated organizational failure. Coordinated policy action, targeted financial stabilization, workforce development, and technology-enabled care models are necessary to mitigate continued erosion of rural healthcare access.
Hospitals are complex institutions where clinical, social, and cultural practices intersect. In Ghanaian hospitals, interactions are shaped by multilingual communication, professional hierarchies, cultural norms, and community expectations. Drawing on five months of ethnographic fieldwork, this study examines coordination of care in a Ghanaian hospital to understand how boundaries (i.e., professional hierarchies, communication barriers, community accountabilities), ruling relations, and power dynamics influence care. Institutional ethnography (IE) was implemented. Participants were purposively sampled, and data were collected through participant observation, documentary materials, and interviews with nurses (n = 11), patients (n = 21), and caregivers (n = 11). Thematic and IE analyses trace how boundaries and professional hierarchies, informal economies, and multilayered surveillance (“medical,” “social,” and “community” gazes) organize care work through institutional texts and how nurses, patients, and caregivers access or deliver care. The findings showed that boundaries at the Yendi Hospital were simultaneously rigid and negotiable, with informal economic activities compensating for institutional resource constraints. Communication work was central to navigating linguistic and cultural diversity, while surveillance from within and beyond the hospital-shaped behaviour and accountability. These everyday practices revealed how institutional texts and societal forces co-produce the conditions of care, with implications for teamwork, patient–provider relationships, and hospital governance.
Background: Artificial intelligence (AI) is increasingly entering hospital practice through diagnostic, predictive, workflow, operational, and generative applications. Hospitals often govern these systems as procurement or information-technology projects rather than as clinical–organizational interventions requiring indication, evaluation, monitoring, accountability, and withdrawal. Objective: To refine the concept of an “AI Hospital Formulary” as an operational, proportional, and accountable framework for the safe, equitable, and sustainable adoption of hospital AI. Design and Methods: This is a perspective article using a structured, non-systematic narrative synthesis and conceptual framework development. Targeted literature and policy sources were identified through purposive searches and citation chaining through 20 July 2026. The synthesis compares the formulary with existing oversight approaches, maps its lifecycle gates to regulatory and risk management duties, and applies the framework to a worked example based on published evaluations of the Epic Sepsis Model. The EQUATOR reporting-guideline selection tool was consulted, and SANRA was used to strengthen the narrative synthesis component. Framework: The revised framework combines a hospital-wide AI register, a standardized formulary monograph, six lifecycle gates, proportional review pathways, governance-of-governance safeguards, cloud and data-sovereignty controls, continuous monitoring of technical and behavioral feedback loops, and explicit renewal or deprescribing criteria. The worked example shows how version-specific evidence can lead to local validation, controlled implementation, restriction, suspension, or renewal rather than automatic adoption. Conclusions: Hospitals should not merely purchase, install, and update AI systems. They should prescribe, monitor, audit, renew, restrict, and, when necessary, deprescribe them. The AI Hospital Formulary is proposed as a complementary institutional layer that converts external standards and existing governance approaches into documented portfolio decisions at the hospital level.
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.
Preventing inpatient falls remains challenging for healthcare institutions globally, including in Singapore. Integrating technological innovations into fall prevention measures may optimize inpatient care and improve health outcomes. A multiphase study was conducted from 2019 to 2022, employing a human-centred design (HCD) approach to develop a technology-based inpatient fall prevention system (IFPS). The four phases include (1) pre-design observations and focus groups, (2) feature prioritization and wireframe development, (3) prototype testing and safety assessments, and (4) post-design staff training and feedback collection. The developed IFPS integrated artificial intelligence (AI) video analytics for bed-exit prediction with communication devices and autonomous commode delivery to facilitate ward communication and reduce staff workload. This paper describes the development process and user evaluation of the IFPS to assess its operational usability and safety. Potential users of the IFPS, such as ward nurses and patients, suggested features for the IFPS during the pre-design phase and thereafter evaluated the system through focus group discussions and/or feedback surveys. Pre-design focus group participants (n = 24) emphasized durability and user-friendliness requirements, informing system design. When evaluating the system, nurse users (n = 39) perceived the IFPS as effective in reducing falls (65%), enabling them to perform other duties (85%), and allowing them to remain with patients without searching for a commode (64%). Patient users (n = 21) found pre-recorded messages effective (91%), though communication clarity varied. Engaging healthcare workers in IFPS development offered valuable context-based insights, highlighting the importance of addressing technology acceptance factors early to promote adoption of fall prevention technologies in acute care settings.
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.
This study explores how hospital location (rural/non-rural) may moderate the nurse staffing ratio’s impact on three hospital-acquired infections. This study used data from 2022 to 2024 on nurse staffing and hospital characteristics from the American Hospital Association Annual Survey and data on hospital-acquired infection rates from the Medicare Care Compare dataset provided by the Centers for Medicare and Medicaid Services. After removing missing values, the final dataset included 7997 hospital-year observations across the US. Independent variables include rural hospital designation, nursing hours per patient day, and RN FTE per adjusted day. The dependent variables included infection rates of Central Line-Associated Bloodstream Infection, Catheter-Associated Urinary Tract Infection, and Methicillin-Resistant Staphylococcus aureus. Multiple regression was performed in Stata 18. Our research found that across all three infection types, an increase in nursing hours per patient day is significantly associated with a decrease in the infection rate, and that impact was not moderated by hospital rurality. Extra time spent with patients in either a rural or non-rural hospital decreased hospital-acquired infection rates. While RN FTEs were included in the model, total nursing hours per patient day emerged as the more consistent predictor of lower hospital-acquired infection rates.
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.
This study analyzed environmental noise levels in neonatal hospital units, including both low- and high-risk nurseries, as well as neonatal intensive care units (NICUs). Continuous 24 h measurements over ten days revealed that average sound levels significantly exceeded international recommendations. Hourly LAeq values frequently reached or surpassed 65 dB, with over 20% of daily recordings exceeding this limit, and in some instances, more than 50%. Heatmaps indicated consistent noise patterns: high-risk nurseries experienced peaks during late morning and afternoon, low-risk nurseries at night, while NICU maintained elevated levels throughout the day. The main sources of noise included alarms, medical equipment, and activity from staff or visitors. This highlights the need for hospital policies aimed at protecting the neurosensory health of neonates. These findings provide evidence-based recommendations for creating quieter environments in neonatal care.
Delirium is a common and serious condition among neurological patients, and the overlap between delirium symptoms and neurological disorders complicates both diagnosis and management. Despite its clinical impact, guidance for delirium management in neurological settings remains limited. This qualitative study aimed to investigate healthcare professionals’ perceptions of delirium management in a Danish neurological hospital setting. Focus group interviews were conducted with five multidisciplinary healthcare professional groups. Maximum variation sampling was used to capture diverse perspectives, and 24 healthcare professionals from the same neurological department participated. Data were analyzed using reflexive thematic analysis. Three themes were identified: (1) delirium care practices in an acute neurological setting; (2) multidisciplinary collaboration in delirium care; and (3) responsibility for delirium care. The findings highlight challenges related to prioritization, mono-professional practices, and organizational structures that shape how responsibility for delirium management is understood and enacted. Overall, the study illustrates the complexity of delirium management within multidisciplinary neurological teams and suggests the need for context-sensitive approaches that support collaboration and clarify responsibilities in clinical practice.
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.
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.
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.
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.
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.
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.
