Search Results (2,206)

The study screened clinical and laboratory indicators accessible within 48 h of admission for children with Epstein–Barr virus-associated hemophagocytic lymphohistiocytosis (EBV-HLH) and macrophage activation syndrome (MAS) to establish an efficient and convenient differential diagnosis model. The study retrospectively analyzed the clinical data and laboratory results obtained within 48 h of admission from 106 pediatric patients initially hospitalized and diagnosed with EBV-HLH or MAS at the Children’s Hospital of Soochow University from January 2019 to November 2024. Univariate logistic regression and LASSO regression filtered the variables alongside cross-validation and dimensionality reduction. The final regression model constructed a predictive nomogram, calibration curve, ROC curve, and DCA curve to evaluate the discrimination capacity and net clinical benefit of the model. Univariate logistic regression, LASSO regression with five-fold cross-validation, and stepwise multivariate logistic regression identified skin rash, bone marrow hemophagocytosis, fibrinogen, neutrophil percentage, and hepatosplenomegaly as the core predictors for differentiating EBV-HLH from MAS. The constructed nomogram and diagnostic calculator demonstrated favorable discriminative ability, with an area under the receiver operating characteristic curve of 0.938 (95% CI: 0.894–0.974). The calibration curve showed good agreement between predicted probabilities and actual observed outcomes, yielding a Brier score of 0.099. Decision curve analysis indicated that the model provided a significant positive net clinical benefit across a risk threshold range of 5% to 95%. A simple scoring system based on common clinical indicators effectively distinguished EBV-HLH from immune-related HLH in the early stages and guided initial treatment decisions. Full article

Background and Objectives: Cardiology departments generate large volumes of unstructured free-text reports that impose substantial manual review burdens on clinicians; at Hospital de Santa Maria—Portugal’s largest public hospital—manual review of 12,651 reports took approximately seven minutes per report, representing over 1475 h of avoidable administrative work. This study presents Text2FHIRwallet, a health professional digital wallet that automates extraction and structuring of clinical entities from unstructured Portuguese cardiology reports using fine-tuned Named Entity Recognition (NER) models and maps the results to Fast Healthcare Interoperability Resources (FHIR) R4 patient summaries. Materials and Methods: Following the Design Science Research Methodology (DSRM) and CRISP-DM, we fine-tuned four transformer-based models—BERTimbau Base, BERTimbau Large, Albertina PT-PT, and MediAlbertina—on 305 manually annotated cardiology reports (77,309 tokens; κ = 0.85 inter-annotator agreement) covering eight clinical entity types, drawn from a corpus of 12,651 anonymised documents. Entities were mapped to FHIR R4 resources and delivered through a secure, role-based mobile wallet (React Native). Evaluation comprised token-level NER benchmarking with bootstrapped confidence intervals and McNemar’s testing, FHIR mapping accuracy assessment on 100 manually reviewed reports, processing-efficiency measurement, and a usability pilot with 10 cardiologists (SUS, NPS). Results: MediAlbertina achieved the highest NER performance (macro F1 = 0.985, 95% CI: 0.979–0.990), significantly outperforming all baseline models (p < 0.01, McNemar’s test) and comparing favourably with—though not directly comparable to, given differing languages and datasets—published benchmarks such as GPT-4 (F1 = 0.962 in ophthalmology NER) and fine-tuned BERT models for lung cancer NER (F1 ≈ 0.85–0.90). FHIR mapping accuracy was 98% on 100 independently reviewed reports. Report processing time was reduced from approximately seven minutes to 15–30 s (93–96% reduction), with peak batch-inference throughput of up to 1000 reports/h under parallelised GPU load (observed end-to-end throughput in pilot deployment was approximately 250 reports/h). The pilot usability evaluation yielded a SUS score of 87 (excellent) and an NPS of 80. Conclusions: Text2FHIRwallet demonstrates that domain-specific fine-tuning of a Portuguese-language pretrained language model achieves near-ceiling clinical NER accuracy, enabling scalable, interoperable, and privacy-compliant patient summary generation from unstructured cardiology text, offering an end-to-end pathway for integrating AI-driven NLP into clinical workflows and FHIR-based health information ecosystems, with implications for administrative efficiency, care coordination, and clinical research in non-English-language settings. Full article

Durable medical devices (DMD) are essential healthcare assets, yet their management in public hospitals is constrained by fragmentation, limited traceability, reactive maintenance, and weak lifecycle integration. This study proposes a framework for digitally enabled, sustainable, and circular DMD management. A mixed-methods design integrated a literature review, expert consultation using the Best–Worst Method, weighted technology nominations, and case-based process mapping in Brazilian hospitals. Eleven experts assessed the criteria guiding Industry 4.0 technology selection for DMD management and the technologies best responding to these priorities; nine consistent judgments were aggregated. Patient-Centered Care, Operational Efficiency, and Resource Efficiency and Cost Reduction emerged as the leading influences on technology selection. Big Data and Analytics, Artificial Intelligence, the Internet of Things, Cloud Computing, Cyber-Physical Systems, Smart Sensors, and Machine Learning formed the priority portfolio, accounting for 84% of the weighted score. The cases contextualized these priorities by revealing discontinuous information flows, limited asset visibility, corrective maintenance, fragmented governance, and weak end-of-life practices. By connecting decision priorities and technological capabilities with observed gaps, the TO-BE framework organizes sustainable procurement, traceable use, predictive maintenance, redeployment, refurbishment, and responsible disposal through material and information flows, providing a pathway for digital and circular transformation in resource-constrained healthcare systems. Full article

As small and medium enterprise organisations (SMEs) grapple with competing demands, decision-making is affected when faced with cost and regulatory uncertainties in response to sustainability obligations. Organisations adapt differently, focusing their resources to the most efficient use to optimise resources and enhance productivity. Qualitative interviews were carried out with 22 SME leaders spanning multiple sectors, including manufacturing, retail, services, construction, logistics, and hospitality, enabling examination of cross-sector variation while maintaining a shared regional context. The interviews lasted 45–65 min and captured managerial cognition, tacit knowledge, and informal strategic processes. The analysis reveals that SMEs do not respond uniformly to sustainability pressures. Instead, their responses are shaped by the interaction between cost pressures and regulatory uncertainty, producing distinct but overlapping strategic patterns. Three dominant mechanisms emerged across cases: cost filtering, temporal disruption under regulatory uncertainty, and strategic response variation. These mechanisms collectively explain how SMEs balance economic viability with environmental performance under constraint. The paper contributes to debates on sustainability transitions, resilience, and SME sustainability transitioning and growth strategies by identifying pathways that balance economic viability with environmental performance. Full article

Background: This study explores physicians’ perceptions and satisfaction with Electronic Medical Record (EMR) systems in resource-limited healthcare settings, addressing a critical gap in research. It identifies key factors essential for optimizing EMR adoption and enhancing healthcare delivery. Methods: This study employed a facility-based mixed-method cross-sectional study comprising a quantitative survey of 249 physicians at St. Paul’s Hospital Millennium Medical College (SPHMMC), sampled through stratified random sampling techniques, with thematic analysis of semi-structured interviews of eight physicians, selected using criterion and stratified purposive sampling techniques. Quantitative data was analyzed using both binary and multivariable logistic regression, and qualitative data from semi-structured interviews was analyzed using thematic analysis. Results: The study reveals that 91% of physicians experience workflow disruptions due to EMR system use. Despite this, physicians were satisfied with efficiency gains associated with EMR systems, with 65.8% noticing improvements in their efficiency and 50.6% reporting reduced workloads. 78.7% of physicians expressed satisfaction with workflow integration. EMR communication tools were rated positively, with 92.0% satisfied with integration. Overall, 79.5% of St. Paul’s Hospital Millennium Medical College (SPHMMC) physicians are satisfied with the implemented EMR system, with factors such as ease of use, integration, confidence in navigation, sufficient technical support, and perceived effectiveness in facilitating patient care across departments affecting satisfaction. The qualitative study also highlighted the following themes: a disconnect between system functionality and resource availability, usability and functionality gaps affecting workflow, inadequate training and support, data security and privacy concerns, and mixed perceptions of the EMR system’s impact on patient care benefits and challenges. Conclusions: The study shows that while the EMR system at SPHMMC has a mixed perception among physicians, it has generally satisfied them, and this supports a multifaceted approach to enhance physician satisfaction and optimize EMR utilization at SPHMMC. This includes enhancing system usability, providing comprehensive training, ensuring readily available technical support, improving workflow integration, and strengthening data security measures. Full article

Overview: SeptiCyte RAPID is an FDA-cleared gene expression test that quantifies host immune response to aid in the diagnosis of sepsis. The test yields a score (the SeptiScore) ranging from 0–15, distributed across four bands (1–4) based on increased likelihood of sepsis. Each band can be characterized by average positive and negative likelihood ratios (LR+ and LR−, respectively) for the discrimination of sepsis versus the non-infectious systemic inflammatory response syndrome (SIRS). Methods: A retrospective analysis of prospectively collected data from a combined cohort of critically ill patients suspected of sepsis (n = 889), recruited across 19 hospitals in the USA and Europe. The analysis quantified the LR+ and LR− parameters as a function of SeptiScore, for discrimination of sepsis vs. SIRS in patients admitted to ICU. Hypotheses: (1) The likelihood ratio (LR) framework provides a clinically useful interpretive approach that complements the previously used SeptiScore banding scheme; (2) Low Band 1 SeptiScores are associated with sufficiently small LR− to support the use of SeptiCyte RAPID as a rule-out test for sepsis; (3) High Band 4 SeptiScores are associated with sufficiently large LR+ to support the use of SeptiCyte RAPID as a rule-in test for sepsis; and (4) SeptiScore-derived LR+ and LR− values can be combined with estimates of pre-test probability (derived from patient characteristics and/or other diagnostic tests) to generate individualized, patient-specific post-test probabilities of sepsis. Results: The SeptiCyte RAPID test demonstrates strong diagnostic performance in distinguishing sepsis from SIRS. The likelihood ratios across different score bands provide clear clinical utility: the median LR+ was 3.26 (range 2.57–4.24) for Band 3, and 6.97 (range 4.35–15.57) for Band 4, providing evidence toward ruling in sepsis at high SeptiScores. Conversely, the median LR− was 0.16 (range 0.14–0.20) for Band 2 and 0.085 (range 0.014–0.16) for Band 1, providing evidence toward ruling out sepsis at low SeptiScores. A higher-resolution analysis of SeptiCyte RAPID performance confirmed these trends by evaluating LR+ and LR− at specific values within each band. The sepsis group was further stratified according to whether patients were classified as blood culture positive (BC+) or blood culture negative (BC−), and the detailed LR+ and LR− analyses were repeated. A monotonic increase in likelihood ratio with increasing SeptiScore was consistently observed, independent of whether sepsis patients were culture-positive, culture-negative, or unstratified with respect to blood culture status. Conclusions: High SeptiScores have correspondingly high LR+ values, and low SeptiScores have correspondingly low LR− values, both of which may have clinical utility. High likelihood ratios for Band 4 SeptiScores, which precede traditional microbiology results, may provide clinicians with early confidence of a sepsis diagnosis and microbiology diagnostic stewardship. Low likelihood ratios for Band 1 SeptiScores may prompt clinicians to consider an alternate diagnosis to sepsis. These are diagnostic-performance-based observations; whether they translate into fewer missed diagnoses or more efficient use of hospital resources has not been directly assessed in this study and will require dedicated clinical outcome studies. Full article

Background: The QIAstat-Dx Gastrointestinal Panel (QGP), a rapid multiplex polymerase chain reaction (PCR) test, provides high diagnostic accuracy for ruling in common gastrointestinal pathogens. Its short turnaround time supports earlier therapeutic decisions and antibiotic stewardship, but its high cost highlights the need for more selective testing. This study aimed to develop a clinical score to identify paediatric patients most likely to have a positive bacterial QGP result. To our knowledge, no such score exists. Methods: We conducted a retrospective study including 70 paediatric patients (median age 1.08 years) who underwent QGP testing between April and October 2025 in a tertiary hospital in Bucharest, Romania. Clinical and biological variables, along with sonographic indicators of bowel inflammation, were evaluated for their association with positive bacterial QGP results. Results: No pathogens were detected in 40% of cases, a single pathogen was detected in 32.9% of samples, two pathogens in 21.4%, and three or more in 5.7%. Bacterial organisms were identified in 45.7% of tests, with Salmonella being most common (17.6% of all tests; 28.6% of positive tests). Of all evaluated variables, fever, bloody stools, C-reactive protein (CRP) > 2 mg/dL, sonographic evidence of bowel inflammation, and exclusion of other infectious foci were included in our prediction score, each assigned one point (with a maximum of 5 points). Other laboratory markers showed no significant association with a positive bacterial QGP result. Receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) of 0.758 (95% CI: 0.596–0.920, p = 0.005). The optimal cut-off, determined using Youden’s index, was 2.5, with 80% sensitivity and 67.7% specificity. A score ≥ 3 indicated a markedly increased likelihood of a positive bacterial QGP result. Conclusions: The proposed QGP clinical score may support selective use of QGP testing, improving diagnostic efficiency and reducing unnecessary costs. Prospective multicentre validation is warranted. Full article

Smart cities (SCs) use the Internet of Things (IoT) to collect and process data to communicate with their infrastructure and assets in real time. A great deal of techniques, such as encryption protocols, Random Forest-based AI-driven threat detection, and blockchain architectures, have been developed to address cybersecurity challenges in smart cities (SCs). These techniques, however, have limitations such as their scalability, high computational expenses, and energy inefficiency. Therefore, in this study, to overcome these challenges, we propose a blockchain-based infrastructure called BlockSafeNet. This uses artificial intelligence, big data, and blockchain to enhance cybersecurity in SCs. The effectiveness of the proposed BlockSafeNet framework was evaluated using responsiveness, computational time, encryption quality score, detection rate, false positive rate, latency, throughput, and energy consumption as the primary cybersecurity performance metrics. These metrics were selected to assess communication efficiency, threat detection capability, privacy preservation, scalability, and overall security performance within smart-city IoT environments. To ensure secure data transactions, robust threat detection, and efficient communication. The system’s high calculation speed and detection rate show potential for managing sensitive maternal health data collected by IoT devices. The platform also shows how IoT may be used by healthcare services to monitor public health in real time, allowing hospitals, emergency services, and public health agencies to securely share data. This aids in resource optimization, improving service delivery, and preserving data privacy and trust in SCs. Data was obtained from the UCI Machine Learning Repository on Kaggle to validate the developed framework. By evaluating the effectiveness of BlockSafeNet in tackling cybersecurity challenges, we establish its practical relevance and usability in SCs. The proposed BlockSafeNet framework achieved a responsiveness of 24 s, an encryption quality score of 0.89, computational time of 85 s, and a detection rate of 91%, demonstrating significant improvements in secure IoT communication, privacy preservation, and AI-driven cyber threat detection within smart city infrastructures. shows that SC IoT security has significantly improved through the adoption of new data protection methods and better measures of security, providing a positive impact on the SC ecosystem. Full article

Cardiac contractility modulation (CCM) is an implantable device-based therapy that delivers biphasic, non-excitatory electrical signals to the ventricular myocardium during the absolute refractory period. By enhancing contractile performance without inducing depolarization or altering ventricular activation, CCM acts as bioelectronic myocardial conditioning. Current evidence supports its use in selected patients with symptomatic heart failure, reduced or mildly reduced left ventricular ejection fraction, narrow QRS duration, persistent symptoms despite guideline-directed medical therapy, and no indication for cardiac resynchronization therapy. In this population, CCM improves functional status and quality of life, whereas evidence for reductions in mortality or recurrent heart failure hospitalization remains less definitive. Whether CCM also reduces arrhythmic burden remains uncertain. Candidates for CCM frequently exhibit atrial and ventricular remodeling, neurohormonal activation, implantable cardioverter-defibrillators, and vulnerability to atrial fibrillation, ventricular arrhythmias, and device therapies. Mechanistically, CCM may render the failing myocardium less arrhythmogenic through coordinated effects on calcium handling, electromechanical remodeling, fibrosis-related substrate, contractile efficiency, and heart-failure stability. However, pivotal trials were not designed to assess arrhythmic endpoints, leaving the relationship between CCM and arrhythmic burden insufficiently characterized. This review summarizes CCM evidence, mechanistic rationale, available arrhythmic signals, device-related considerations, and future research priorities for prospective studies in this evolving field. Full article

The design of ultra-low-temperature refrigeration systems has gained increasing importance in recent years, driven by the growing demand for very-low-temperature storage cabinets for many applications, such as the preservation of vaccines and biological materials. In the first part of this study, the thermal design of both two-stage and three-stage cascade refrigeration systems was developed using the Engineering Equation Solver (EES). The systems were evaluated under evaporator temperatures of −85 °C, −80 °C, and −75 °C and ambient temperatures ranging from −5 °C to 40 °C. For the two-stage configuration, the R170/R161 refrigerant pair was assessed as an alternative to the conventional R170/R290 combination. In the three-stage configuration, the performance of the R1150/R170/R290 and R1150/R170/R161 refrigerant combinations was analyzed. The results indicate that under identical operating conditions, the three-stage system demonstrated lower compressor power consumption and reduced exergy destruction compared to the two-stage configuration, while achieving higher coefficients of performance (COPs) and exergy efficiency. In the second part of the study, long-term dynamic simulations of energy consumption for both two-stage and three-stage systems were carried out using TRNSYS 18 under varying ambient temperature conditions. The simulations were performed for hospital installation rooms located in three different cities: Muğla (Turkey), Milan (Italy), and Warsaw (Poland). The results of the dynamic simulations indicate that the use of R161 leads to significant energy savings compared to R290. Specifically, when comparing the three-stage cascade system using R1150/R170/R161 with the conventional two-stage R170/R290 system, energy consumption reductions of 20.5% in Warsaw, 23.6% in Milan, and 26.6% in Muğla were achieved. Full article

The main objective of the current study is to assess the techno-economic feasibility, climate change adaptability, and sustainability of photovoltaic energy systems in six large hospitals in Turkey (Adana, Başakşehir, Bursa, Elazig, Gaziantep, and Yozgat) to achieve United Nations recommendations as Sustainable Development Goal 7 (affordable and clean energy) and Sustainable Development Goal 13 (climate action). This study aims to determine the impact of long-term climate change on the availability of photovoltaic (PV) energy resources. To achieve this goal, this research was conducted through a multi-step approach combining (1) the detection of long-term climate trends using linear regression on the TerraClimate database, (2) the spatial analysis of photovoltaic solar energy potential using high-resolution satellite imagery (Google Maps) for roof suitability and parking areas, (3) the estimation of photovoltaic electricity generation and the calculation of the capacity factor, (4) the application of the Response Surface Methodology (RSM) based on NASA Giovanni data to model the nonlinear reciprocal relationships between precipitation (R), aerosol optical thickness (AOT), photovoltaic solar energy production, and (5) the techno-economic analysis using the Levelized energy cost (LCOE), payback period, and CO₂ emission reductions. The results show statistically consistent warming trends across all sites with trends for Tmax ranging from +0.0205 to +0.0268 °C/year and for Tmin from +0.0208 to +0.0300 °C/year. The temperature of PV cells increases at a rate of +0.0197 °C/year and the wind speed decreases by −0.0031 to −0.0149 m/s/year, which indicates a reduction in convective cooling. Solar radiation, on the other hand, is relatively constant with small trends ranging from +0.0002 to +0.0566 W/m²/year, and confirms the consistent solar resource availability. Seasonal PV resource potential varies from ~70–95 W/m² in winter to 290–310 W/m² in summer. Furthermore, the installed PV capacities are between 6 MW (Yozgat) and 47 MW (Başakşehir) with capacity factors of 17.0–19.7% and payback periods of 4.31–4.88 years. RSM models have high explanatory power (R² = 0.57–0.74) with AOT as the most important negative driver of PV performance. Consequently, the results show that while the solar resource of Türkiye is stable and highly exploitable, PV efficiency is increasingly determined by climate-induced thermal stress and reduced wind cooling. The study highlights the economic viability, environmental advantages, and strategic relevance of PV systems at hospitals for resilient, low-carbon healthcare infrastructure in future climate scenarios. Full article

Accurate classification of cells from body fluid specimens can support cytological analysis, but microscopic images often present challenges such as low contrast, unclear boundaries, staining variation, class imbalance, and overlapping morphology. This study evaluated the impact of image enhancement techniques on deep learning-based multi-class classification of body fluid cells. The dataset comprised 7071 microscopic images from Srinagarind Hospital, Thailand, annotated by expert technologists. After preprocessing and cell extraction, 22,062 single-cell images across 13 cell types were obtained. A 70:30 train–test split was used, with augmentation and random undersampling applied only to the training set. Nine individual enhancement filters and five combined settings were tested using MobileNetV3, DenseNet121, ResNet50, and EfficientNetB3. Performance was measured using weighted accuracy, precision, recall, and F1-score. DenseNet121 with Edge Enhance achieved the best individual-filter performance (accuracy 83.36%, F1-score 83.08%). For combined settings, DenseNet121 with CLAHE followed by Detail performed best (accuracy 83.01%, F1-score 82.54%), though it did not surpass the top individual filter. Multi-step enhancement provided limited additional benefit. Class-wise results showed strong performance for distinct cell types, while macrophages, monocytes, and lymphocytes remained challenging. Grad-CAM visualization further indicated that model attention was generally concentrated on relevant cellular regions, including nuclei, cytoplasm, and cell boundaries. Overall, selected enhancement techniques offer modest improvements, but effectiveness depends on model architecture and data characteristics. As this study used a single-institution dataset without external validation, the approach should be considered an assistive framework rather than a clinically validated diagnostic system. Full article

This paper proposes a digital twin (DT)-enabled edge computing framework for healthcare task offloading in smart medical environments. The DT maintains a continuously synchronized virtual representation of the physical edge system, capturing queue states, resource utilization, and energy dynamics. Based on this virtual state, a deterministic optimization model is formulated to support real-time offloading and scheduling decisions under latency, energy, and fairness constraints. Unlike prediction-only approaches, the proposed DT operates in a closed-loop manner, where state estimation and synchronization directly influence scheduling feasibility and system performance. The offloading problem is formulated as a mixed-integer linear programming (MILP) model to jointly optimize task allocation, delay minimization, and energy efficiency. The simulated workload includes 10,000 heterogeneous healthcare applications. At each time slot, one to five tasks are generated and uniformly selected from ECG, video-processing, or medical-imaging workloads, with average task sizes of 90 KB, 280 KB, and 150 KB, respectively. This setup aims to emulate diverse real-world healthcare edge workloads with varying communication and computation demands. The proposed approach significantly reduces average latency by up to 57%, eliminates task drops in all evaluated scenarios, and improves load balancing compared with hospital-only and round-robin baselines. Although total energy consumption increases moderately, energy efficiency per completed task improves due to more effective scheduling by stability, reducing deadline violations and enhancing resource utilization. We further analyze the impact of DT freshness and updated frequency and show that outdated or misaligned DT updates can degrade performance by increasing delay and leading to suboptimal decisions, while overly frequent updates introduce additional coordination overhead. These results highlight the importance of jointly designing DT synchronization mechanisms and optimization-based scheduling strategies for reliable and cost-efficient healthcare edge systems. Full article

Objectives: Hospital efficiency provides a systems-level perspective for identifying unrealized capacity to improve care delivery, quality, and patient safety. This study evaluated relative technical efficiency among U.S. short-term acute care hospitals (2018–2024) and evaluated how structural IT operating investments are converted into clinical service volume and quality-related performance. Methods: A longitudinal panel of 8589 hospital-year observations was utilized to estimate technical efficiency with an output-oriented Data Envelopment Analysis (DEA) under variable returns to scale. A secondary Simar–Wilson double-bootstrapped truncated regression (n = 2147 complete casesexamined associations with bias-corrected efficiency, clinical quality, case mix, and operational scale. Results: Mean DEA efficiency was 0.567, with 7.38% (n = 634) operating on the annual efficiency frontier and a mean output expansion potential of 102.49%. Efficient hospitals maintained higher IT operating spending. Stage 2 regression showed that higher Hospital Value-Based Purchasing quality performance was significantly associated with greater inefficiency. Conversely, higher case mix complexity and larger operational scale were associated with higher efficiency. Conclusions: Most hospitals operated below the best-practice frontier, indicating gaps in converting resources into service volume and quality. Because core operational drivers were included in the primary DEA model, observed associations show descriptive structural patterns rather than direct cause-and-effect relationships. Full article

Background/Objectives: Breast pumps are essential tools when breastfeeding dyads are separated or there are breastfeeding challenges that prevent or reduce effective milk removal. Hospital-grade and personal-use pumps offer different features and performance characteristics. Direct comparative studies evaluating pump efficacy and maternal satisfaction are limited in this area, hampering healthcare professionals in their support of lactating women. We compared the efficacy and maternal perceptions of a hospital-grade breast pump (Symphony; Medela AG) and an electric personal-use pump (Pump in Style Pro; Medela AG). Methods: In total, 28 lactating mothers with healthy-term infants participated in an unblinded randomised crossover trial. Participants attended two pumping sessions using either the hospital-grade pump or personal-use pump. Effectiveness of milk removal and maternal perception were measured. Participants completed a 24 h milk profile to enable calculation of the percentage of available milk removed from the breast. Results: There were no significant differences for milk removal efficacy parameters after accounting for pre-expression degree of fullness of the breast between pumps. Similarly, there was no difference in comfort ratings. However, mothers perceived the hospital-grade breast pump vacuum as smoother than the personal-use pump (p = 0.003) and rated the hospital-grade breast pump as having a more pleasant sound (p < 0.001). Both findings remained significant after adjustment for pre-expression degree of fullness (p < 0.001 and p = 0.030, respectively). Conclusions: The hospital-grade pump and the personal-use pump did not differ with respect to milk removal efficacy and comfort. However, participants did perceive differences in vacuum application. Full article