Enhance Patient Care Research Articles

Predicting the Early Detection of Breast Cancer Using Hybrid Machine Learning Systems and Thermographic Imaging

ABSTRACTBreast cancer is a leading cause of mortality among women, emphasizing the critical need for precise early detection and prognosis. However, conventional methods often struggle to differentiate precancerous lesions or tailor treatments effectively. Thermal imaging, capturing subtle temperature variations, presents a promising avenue for non‐invasive cancer detection. While some studies explore thermography for breast cancer detection, integrating it with advanced machine learning for early diagnosis and personalized prediction remains relatively unexplored. This study proposes a novel hybrid machine learning system (HMLS) incorporating deep autoencoder techniques for automated early detection and prognostic stratification of breast cancer patients. By exploiting the temporal dynamics of thermographic data, this approach offers a more comprehensive analysis than static single‐frame approaches. Data processing involves splitting the dataset for training and testing. A predominant infrared image was selected, and matrix factorization was applied to capture temperature changes over time. Integration of convex factor analysis and bell‐curve membership function embedding for dimensionality reduction and feature extraction. The autoencoder deep neural network further reduces dimensionality. HMLS model development included feature selection and optimization of survival prediction algorithms through cross‐validation. Model performance was assessed using accuracy and F‐measure metrics. HMLS, integrating clinical data, achieved 81.6% accuracy, surpassing 77.6% using only convex‐NMF. The best classifier attained 83.2% accuracy on test data. This study demonstrates the effectiveness of thermographic imaging and HMLS for accurate early detection and personalized prediction of breast cancer. The proposed framework holds promise for enhancing patient care and potentially reducing mortality rates.

Integrating pharmacy practice into precision emergency medicine: A pathway to enhanced patient care

Integrating pharmacy practice into precision emergency medicine: A pathway to enhanced patient care

Understanding the role of machine learning in predicting progression of osteoarthritis.

Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures. Out of 1,160 studies initially identified, 39 were included. Most studies (85%) were published between 2020 and 2024, with 82% using publicly available datasets, primarily the Osteoarthritis Initiative. ML methods were predominantly supervised, with significant variability in the definitions of OA progression: most studies focused on structural changes (59%), while fewer addressed pain progression or both. Deep learning was used in 44% of studies, while automated ML was used in 5%. There was a lack of standardization in evaluation metrics and limited external validation. Interpretability was explored in 54% of studies, primarily using SHapley Additive exPlanations. Our systematic review demonstrates the feasibility of ML models in predicting OA progression, but also uncovers critical limitations that currently restrict their clinical applicability. Future priorities should include diversifying data sources, standardizing outcome measures, enforcing rigorous validation, and integrating more sophisticated algorithms. This paradigm shift from predictive modelling to actionable clinical tools has the potential to transform patient care and disease management in orthopaedic practice.

Systematic Literature Review: Health Technology in Nursing

Digital health technologies such as telehealth, electronic health records (EHR), and artificial intelligence (AI) have transformed nursing by enhancing patient care, improving workflow efficiency, and expanding healthcare accessibility. This systematic literature review examines the current role of these technologies in nursing practice, focusing on their impact on patient outcomes, workflow optimization, and accessibility of healthcare services. Findings indicate that telehealth increases healthcare access for underserved populations, EHRs improve care coordination and documentation accuracy, and AI supports predictive care and clinical decision-making. However, challenges such as data security, usability, and ethical considerations remain. Addressing these issues through training, system improvements, and ethical guidelines is essential for optimizing the benefits of digital health technologies in nursing. This review provides insights for healthcare providers and policymakers, emphasizing the importance of technology integration and support in advancing nursing practices.

CMOS Pulse Oximeter

Abstract: This paper presents the design and implementation of a non-invasive pulse oximeter based on Complementary MetalOxide-Semiconductor (CMOS) technology, aimed at enhancing the accuracy and accessibility of blood oxygen saturation measurements. Pulse oximetry is a critical tool in clinical and personal health monitoring, providing vital information about respiratory function. The integration of CMOS technology allows for miniaturization and energy efficiency, facilitating the development of compact, wearable devices capable of continuous monitoring. The proposed system utilizes advanced signal processing techniques, including lock-in detection and dual-wavelength illumination, to improve the reliability of photoplethysmographic (PPG) signal acquisition under various conditions. Experimental validation demonstrates the device's capability to deliver accurate SpO2 readings even in the presence of motion artifacts and ambient light interference. Furthermore, the calibration methods employed ensure that measurements are consistent and precise across different users. By leveraging the advantages of CMOS technology, this study not only highlights the potential for improved patient comfort and compliance but also opens new avenues for the application of pulse oximetry in diverse environments. The findings underscore the effectiveness of CMOS-based pulse oximeters in advancing non-invasive health monitoring solutions, ultimately contributing to enhanced patient care and outcomes.

Integrating FinOps in healthcare for optimized financial efficiency and enhanced care

The integration of Financial Operations (FinOps) in healthcare is increasingly recognized as a critical strategy for optimizing financial efficiency and enhancing patient care. This paper explores the principles and practices of FinOps, emphasizing its role in improving cost management, fostering financial transparency, and ensuring better resource allocation in healthcare organizations. By addressing traditional inefficiencies in healthcare financial management, FinOps promotes collaboration between finance and operations, enabling real-time financial decision-making that aligns with both operational goals and patient outcomes. Additionally, the paper outlines the challenges healthcare organizations face in managing rising costs and complex billing systems, and provides practical strategies for the successful implementation of FinOps, including the use of advanced technologies such as cloud-based platforms and data analytics tools. Ultimately, the adoption of FinOps not only enhances short-term financial efficiency but also contributes to the long-term sustainability and scalability of healthcare services.

Leveraging Artificial Intelligence and Data Science for Integration of Social Determinants of Health in Emergency Medicine: Scoping Review.

Social determinants of health (SDOH) are critical drivers of health disparities and patient outcomes. However, accessing and collecting patient-level SDOH data can be operationally challenging in the emergency department (ED) clinical setting, requiring innovative approaches. This scoping review examines the potential of AI and data science for modeling, extraction, and incorporation of SDOH data specifically within EDs, further identifying areas for advancement and investigation. We conducted a standardized search for studies published between 2015 and 2022, across Medline (Ovid), Embase (Ovid), CINAHL, Web of Science, and ERIC databases. We focused on identifying studies using AI or data science related to SDOH within emergency care contexts or conditions. Two specialized reviewers in emergency medicine (EM) and clinical informatics independently assessed each article, resolving discrepancies through iterative reviews and discussion. We then extracted data covering study details, methodologies, patient demographics, care settings, and principal outcomes. Of the 1047 studies screened, 26 met the inclusion criteria. Notably, 9 out of 26 (35%) studies were solely concentrated on ED patients. Conditions studied spanned broad EM complaints and included sepsis, acute myocardial infarction, and asthma. The majority of studies (n=16) explored multiple SDOH domains, with homelessness/housing insecurity and neighborhood/built environment predominating. Machine learning (ML) techniques were used in 23 of 26 studies, with natural language processing (NLP) being the most commonly used approach (n=11). Rule-based NLP (n=5), deep learning (n=2), and pattern matching (n=4) were the most commonly used NLP techniques. NLP models in the reviewed studies displayed significant predictive performance with outcomes, with F1-scores ranging between 0.40 and 0.75 and specificities nearing 95.9%. Although in its infancy, the convergence of AI and data science techniques, especially ML and NLP, with SDOH in EM offers transformative possibilities for better usage and integration of social data into clinical care and research. With a significant focus on the ED and notable NLP model performance, there is an imperative to standardize SDOH data collection, refine algorithms for diverse patient groups, and champion interdisciplinary synergies. These efforts aim to harness SDOH data optimally, enhancing patient care and mitigating health disparities. Our research underscores the vital need for continued investigation in this domain.

Role of Hypoperfusion Intensity Ratio in Vessel Occlusions: A Review on Safety and Clinical Outcomes.

The hypoperfusion intensity ratio (HIR) is a quantitative metric used in vascular occlusion imaging to evaluate the extent of brain tissue at risk due to hypoperfusion. Defined as the ratio of tissue volume with a time-to-maximum (Tmax) of >10 seconds to that of >6 seconds, HIR assists in differentiating between the salvageable penumbra and the irreversibly injured core infarct. This review explores the role of HIR in assessing clinical outcomes and guiding treatment strategies, including mechanical thrombectomy and thrombolytic therapy, for patients with large vessel occlusions (LVOs). Evidence suggests that higher HIR values are associated with worse clinical outcomes, indicating more severe tissue damage and reduced potential for salvage through reperfusion. Additionally, HIR demonstrates predictive accuracy regarding infarct growth, collateral flow, and the risk of reperfusion hemorrhage. It has shown superiority over traditional metrics, such as core infarct volume, in predicting functional outcomes. HIR offers valuable insights for risk stratification and treatment planning in patients with LVOs and distal medium vessel occlusions (DMVOs). Incorporating HIR into clinical practice enhances patient care by improving decision-making processes, promoting timely interventions, and optimizing post-intervention management to minimize complications and improve recovery outcomes.

The Integration of Robotics in Proactive Healthcare: Embracing Proactive Medicine for Enhanced Patient Care and Ethical Considerations

The Integration of Robotics in Proactive Healthcare: Embracing Proactive Medicine for Enhanced Patient Care and Ethical Considerations

Fusing convolutional learning and attention-based Bi-LSTM networks for early Alzheimer’s diagnosis from EEG signals towards IoMT

The Internet of Medical Things (IoMT) is poised to play a pivotal role in future medical support systems, enabling pervasive health monitoring in smart cities. Alzheimer’s disease (AD) afflicts millions globally, and this paper explores the potential of electroencephalogram (EEG) data in addressing this challenge. We propose the Convolutional Learning Attention-Bidirectional Time-Aware Long-Short-Term Memory (CL-ATBiLSTM) model, a deep learning approach designed to classify different AD phases through EEG data analysis. The model utilizes Discrete Wavelet Transform (DWT) to decompose EEG data into distinct frequency bands, allowing for targeted analysis of AD-related brain activity patterns. Additionally, the data is segmented into smaller windows to handle the dynamic nature of EEG signals, and these segments are transformed into spectrogram images, visually depicting brain activity distribution over time and frequency. The CL-ATBiLSTM model incorporates convolutional layers to capture spatial features, attention mechanisms to emphasize crucial data, and BiLSTM networks to explore temporal relationships within the sequences. To optimize the model’s performance, Bayesian optimization is employed to fine-tune the hyperparameters of the ATBiLSTM network, enhancing its ability to generalize and accurately classify AD stages. Incorporating Bayesian learning ensures the most effective model configuration, improving sensitivity and specificity for identifying AD-related patterns. Our model extracts discriminative features from EEG data to differentiate between AD, Mild Cognitive Impairment (MCI), and healthy controls (CO), offering a more comprehensive approach than existing two-class detection algorithms. By including the MCI category, our method facilitates earlier identification and potentially more impactful therapy interventions. Achieving a 96.52% accuracy on Figshare datasets containing AD, MCI, and CO groups, our approach demonstrates strong potential for practical use, accelerating AD identification, enhancing patient care, and contributing to the development of targeted treatments for this debilitating condition.

Superior performance of a center-point AI model over VFLDNet in automated cobb angle estimation for scoliosis assessment.

Aims to establish the superiority of our proposed model over the state-of-the-art vertebra-focused landmark detection network (VFLDNet) in automating Cobb angle estimation from spinal radiographs. Utilizing a private dataset for external validation, we compared the performance of our center-point detection-based vertebra localization and tilt estimation network (VLTENet) with the key-point detection-based VFLDNet. Both models' Cobb angle predictions were rigorously evaluated against manual consensus score using metrics such as mean absolute error (MAE), correlation coefficient, intraclass correlation coefficient (ICC), Fleiss' kappa, Bland-Altman analysis, and classification metrics [sensitivity (SN), specificity, accuracy] focusing on major curve estimation and scoliosis severity classification. A retrospective analysis of 118 cases with 342 Cobb angle measurements revealed that our model achieved a MAE of 2.15° for total Cobb angles and 1.89° for the major curve, significantly outperforming VFLDNet's MAE of 2.80°and 2.57°, respectively. Both models demonstrated robust correlation and ICC, but our model excelled in classification consistency, particularly in predicting major curve magnitude (ours: kappa = 0.83; VFLDNet: kappa = 0.67). In subgroup analyses by scoliosis severity, our model consistently surpassed VFLDNet, displaying superior mean (SD) differences, narrower limits of agreement, and higher SN, specificity, and accuracy, most notably in moderate (ours: SN = 86.84%; VFLDNet: SN = 83.16%) to severe (ours: SN = 92.86%; VFLDNet: SN = 85.71%) scoliosis. Our model emerges as the superior choice for automated Cobb angle estimation, particularly in assessing major curve and moderate to severe scoliosis, underscoring its potential to revolutionize clinical workflows and enhance patient care.

Acceptance of Digital Discharge Management Interventions Among Patients After Bariatric Surgery: A Cross-Sectional Study

Bariatric surgery is an effective long-term treatment for severe obesity, but relapse rates remain high. Digital interventions can enhance patient care, yet research on the intention to use digital discharge management interventions is lacking. This study aims to assess the behavioral intention to use digital discharge management interventions after bariatric surgery and to identify differences in sociodemographic and medical characteristics, as well as potential key drivers and barriers. A cross-sectional study with N = 514 patients was conducted using the Unified Theory of Acceptance and Use of Technology (UTAUT). Mean scores for behavioral intention and predictors were calculated. Group differences were analyzed with independent t-tests and analyses of variance with post hoc tests. Drivers and barriers were assessed through multiple hierarchical regression analysis. The behavioral intention to use digital discharge management interventions was high. Significant predictors included age (β = −0.17, p < 0.001), eHealth literacy (β = 0.10, p = 0.037), internet anxiety (β = −0.15, p = 0.003), and time since bariatric operation (β = −0.13, p = 0.005). The predictors performance expectancy (β = 0.23, p < 0.001), effort expectancy (β = 0.36, p < 0.001), and social influence (β = 0.26, p < 0.001) were significantly positive key factors. These results confirm the need for implementing digital discharge interventions after bariatric surgery, with various drivers and barriers identified for application usage.

The Critical Role of Perceived Academic Institutional Support among Students in a Divided Society

Abstract Background To promote health equity, nursing schools must foster student diversity and develop future nurses’ abilities to care for patients from different backgrounds. Although academic experiences play an essential role in promoting multiculturalism, in Israel, where the Jewish-Arab divide is deep and central, this could be challenging. This study examined the attitudes of Israeli Jewish and Arab nursing students concerning their interactions across group lines. Methods In December 2022, 362 undergraduate students completed a survey on the association between cultural intelligence (CQ), satisfaction with the college’s support, and intergroup social interaction. Results The findings revealed that (1) Arab students (minority group) demonstrated higher CQ, especially regarding awareness of cultural differences and motivation for intercultural encounters; (2) Arab students perceived the intergroup encounter with Jewish students as closer and warmer than Jewish students; (3) Arab students’ satisfaction with the college’s support was higher than that of Jewish students; (4) Structural Equation Modeling revealed that (χ2/df=1.61, CFI=.970, TLI=.955, NFI=.927, RMSEA=.041) the support provided by the educational academic setting serves as a primary explanatory factor in the association between CQ and social distance. Conclusions Educational institutions should ensure their students feel supported to facilitate closer social interactions. This emphasizes the importance of proactive measures to encourage diversity and cultural competence in nursing education. Such efforts may positively impact the students’ mental health and enhance patient care. Key messages • To foster student social interaction in divided societies, academia must ensure a high sense of support. • Proactive nursing education promoting diversity and cultural competence may benefit student mental health and patient care.

Exploring the Impacts of Artificial Intelligence Interventions on Provider Practice practice

Abstract Background Despite the growing prevalence of AI in healthcare, there has been a lack of recent studies exploring its impact on providers’ practices and patient outcomes. This study investigates healthcare providers’ perceptions of AI interventions’ influence on their practice efficiency and patient outcomes, as well as the correlation between providers’ perceptions of AI and its effects on patient outcomes. Methods A self-administered questionnaire was mailed to 38 healthcare providers at a rural medical center in north Texas, with a 70% percent response rate (n = 27). In addition to descriptive statistics, multivariable logistic regression test was conducted to discern the relationship between their perceptions of AI and its impact on patient outcomes. Results The findings revealed that a majority of providers perceived AI to still be in its early stages, posing challenges for practice and having a limited impact on patient outcomes. Additionally, various factors such as age, gender, a user-centered design approach, AI experience, along with perceptions of workplace support and stress, significantly shape providers’ attitudes toward AI and ultimately affecting patient outcomes. Conclusions Before AI technology can fulfill its promise of transforming healthcare through integration with other technologies, the healthcare sector must address numerous hurdles. It is crucial to approach the development and testing of intricate systems like AI-integrated electronic health record (EHR) systems with caution to ensure their reliability and dependability in clinical decision-making. Additionally, navigating medico-legal responsibilities and pursuing fair distribution of benefits are equally crucial. Key messages • This study offers insights into the extent of AI interventions’ impact on healthcare. • This study specifically focuses on enhancing patient care, reducing costs, and improving the efficiency of the healthcare system.

Progressing towards personalised medicine: the Genomic Data Infrastructure (GDI) project

Abstract Funded by the European Commission under the Digital Europe Programme and through co-funding from participating Member States, the European Genomic Data Infrastructure (GDI) aims to support the 1+Million Genome initiative for establishing an effective data infrastructure for genomic data. GDI has the potential to fill the gap between genomic research and clinical practice, promoting the implementation of data-driven precision medicine in Europe with the harmonisation of genomic and related phenotypic and clinical data across adhering countries. Launched in November 2022, the GDI project is a collaborative effort involving partners from 20 European countries, divided into 8 Work Packages across three Pillars. GDI is structured with the development of a framework (Pillar I Long-term sustainability) and the necessary tools (Pillar II 1+MG infrastructure deployment), with the implementations of use cases (Pillar III Application and innovation solution) to support research and innovation, integrate findings into the clinic and healthcare, and improve public health measures. In the first 2 years, we completed the starter kit which we are currently testing with real data. Over the subsequent years, the GDI project aims to provide a federated and sustainable infrastructure for facilitating research that use genomic data. Through authorized secure access, clinicians and researchers can leverage genomic insights for faster and more precise clinical decision-making, diagnosis, and treatments. The GDI project represents a significant opportunity to improve healthcare outcomes and public health measures by leveraging the potential of genomics. By providing secure access to extensive genomic datasets combined with phenotyped data, the infrastructure sets the foundation for international partnerships in personalized medicine. Ultimately, this initiative holds the promise of advancing medical understanding, enhancing patient care, and fostering economic growth across Europe. Key messages • GDI drives personalised medicine via international collaboration, building a sustainable infrastructure. Harmonizing data accelerates innovation, improving outcomes and fostering growth in Europe. • GDI aims to revolutionize healthcare by collecting and harmonizing genomic and clinical data in Europe. It facilitates faster and more precise decisions and treatments, improving healthcare outcomes.

AI in Prosthodontics: A Narrative Review Bridging Established Knowledge and Innovation Gaps Across Regions and Emerging Frontiers

As the discipline of prosthodontics evolves, it encounters a dynamic landscape characterized by innovation and improvement. This comprehensive analysis underscores future developments and transformative solutions across its various subspecialties: fixed, removable, implant, and maxillofacial prosthodontics. The narrative review examines the latest advancements in prosthetic technology, focusing on several critical areas. The integration of artificial intelligence and machine learning into prosthetic design and fitting processes is revolutionizing the field, serving as a common thread that links these innovative technologies across all subspecialties. This includes advancements in automated diagnostics, predictive analysis, and treatment planning. Furthermore, the review offers a forward-looking perspective on how these innovations are influencing each prosthetic dentistry domain, patient outcomes, and current clinical practices. By thoroughly analyzing contemporary research and emerging technologies, the study illustrates how these advancements represent a growing focal point of interest in developing countries, such as Romania, with the potential to redefine the trajectory of prosthetic rehabilitation and enhance patient care not only within this country but also beyond.

The Molecular Classification of Pheochromocytomas and Paragangliomas: Discovering the Genomic and Immune Landscape of Metastatic Disease.

Pheochromocytomas (PCCs) and paragangliomas (PGLs, together PPGLs) are the most hereditary tumors known. PPGLs were considered benign, but the fourth edition of the World Health Organisation (WHO) classification redefined all PPGLs as malignant neoplasms with variable metastatic potential. The metastatic rate differs based on histopathology, genetic background, size, and location of the tumor. The challenge in predicting metastatic disease lies in the absence of a clear genotype-phenotype correlation among the more than 20 identified genetic driver variants. Recent advances in molecular clustering based on underlying genetic alterations have paved the way for improved cluster-specific personalized treatments. However, despite some clusters demonstrating a higher propensity for metastatic disease, cluster-specific therapies have not yet been widely adopted in clinical practice. Comprehensive genomic profiling and transcriptomic analyses of large PPGL cohorts have identified potential new biomarkers that may influence metastatic potential. It appears that no single biomarker alone can reliably predict metastatic risk; instead, a combination of these biomarkers may be necessary to develop an effective prediction model for metastatic disease. This review evaluates current guidelines and recent genomic and transcriptomic findings, with the aim of accurately identifying novel biomarkers that could contribute to a predictive model for mPPGLs, thereby enhancing patient care and outcomes.

Optimising liver screening in patients with a Fontan circulation: a cost-effective approach to detection of Fontan associated liver disease which enhances patient care

Abstract Introduction Patients with Fontan circulation, require screening for liver cirrhosis and malignancy, to which they are susceptible with time. Surveillance biopsies reveal nearly all adult Fontan patients develop asymptomatic fibrosis, and a majority have abnormal liver function tests (LFTs). This presents significant financial healthcare burden and psychological stress with testing for patients. Objective We aim to present "real-world" liver screening data, from a leading Adult Congenital Heart Disease (ACHD) centre, to propose a cost-reducing testing regimen, which delivers effective screening, while taking account of the challenges of current guidance, patient compliance, and clinical practice. Methods We analysed liver screening data for Fontan patients at a leading ACHD centre, focusing on biochemical tests (bilirubin, ALT, GGT) and imaging. We evaluated the 10-year financial impact of current practices, versus a hypothetical model based on age and risk factors, alongside biannual and annual screening strategies. Tariffs for biochemical test were provided by the local pathology service and we used NHS HRG tariffs for imaging and phlebotomy. Results We included data from 114 patients with a Fontan circulation (median age 32 range 19-56, 42% females). Eighteen patients developed liver cirrhosis diagnosed on imaging (median age 32, range 22-48). None developed hepatocellular carcinoma. A lack of clear and agreed local screening guidelines (over years) resulted in vastly different investigations for FALD, with inconsistent time intervals for testing. Bilirubin was elevated in 59% (55% in age 18-29), ALT was elevated in 41% (31% in age 18-29), GGT was elevated in 83% (80% in age 18-29). We found 3 cases of cirrhosis in the age group 18-29, all with hypoplastic left heart; 4 cases of cirrhosis in age group 30-34 - all had significant comorbidities; 33% had normal bilirubin, LFT and GGT, potentially not requiring further screening until the age of 35. Eleven patients (22%) age 35+ had confirmed cirrhosis (Figure 1). Performing full FALD screening with biochemistry and liver ultrasound every 12-24 months would increase the cost of screening three- to six-fold. We propose rationalising liver screening by age and underlying risk (Figure 2) to minimise the economic burden and equally the psychological impact of frequent testing on patients. Conclusions Our analysis underscores the necessity for an optimised liver screening protocol in Fontan patients, balancing diagnostic accuracy with cost-efficiency. By integrating this data, we have identified a pathway to streamline screening, tailoring it to individual risk profiles and age groups, thereby reducing unnecessary tests, lowering costs and reducing the impact of testing on patients. This approach not only fosters better patient care, but also provides a framework for developing clearer, cost-effective screening guidelines, to monitor liver health, in these complex CHD patients.

Leveraging digital platforms to improve Public Health in hospital smart buildings

Abstract The integration of digital platforms into hospital infrastructure represents a significant leap forward in public health management. This session explores how smart hospital buildings, equipped with advanced digital technologies, can enhance public health outcomes through more effective disease surveillance, improved environmental controls and enhanced patient care management. Smart hospital buildings utilize a range of new technologies such as IoT sensors, AI-driven analytics and big data solutions to monitor and optimize various aspects of hospital operations and care. These technologies enable real-time data collection on environmental conditions, patient health metrics and resource utilization, creating an ecosystem that not only supports the immediate health needs of patients but also contributes to broader public health goals. The session will address four key areas where digital platforms can make an impact: -Disease Surveillance and Management: Digital tools can track health data trends and patterns within hospital settings, enabling early detection of infectious outbreaks and more precise tracking of patient outcomes. -Environmental Monitoring: IoT sensors can continuously assess conditions such as air quality, temperature and humidity, which are critical for preventing hospital-acquired infections and ensuring a safe environment for patients and staff. -Resource Optimization: AI algorithms can analyse data to optimize resource allocation, ensuring that medical supplies and human resources are used efficiently, which is crucial in health emergencies. -Patient-Centred Care: Digital platforms can facilitate personalized patient care through real-time health monitoring and data analysis, ensuring timely medical interventions and better health management. This session will highlight case studies demonstrating successful implementation of digital technologies in smart hospital buildings and discuss the challenges and lessons learned from these initiatives.

Exploring Risk Factors for Post-operative Complications in Laparoscopic Common Bile Duct Exploration: A Literature Review.

Gallstone-related diseases, particularly common bile duct (CBD) stones, pose a significant global health challenge. The emergence of laparoscopic common bile duct exploration (LCBDE) has transformed the management of these conditions by offering a less invasive alternative to traditional open surgery. This literature review aims to analyze published literature to identify and understand the risk factors associated with LCBDE. The study aims to offer valuable insights that could potentially enhance patient care and outcomes in managing CBD stones. A comprehensive search of English-language studies from the past 20 years was conducted using PubMed, focusing on peer-reviewed primary research, systematic reviews, and meta-analyses. From 830 initial articles, 25 were selected based on relevance and availability. The review identified several key risk factors influencing LCBDE outcomes, including patient-related factors such as advanced age, high comorbidity scores, narrow bile ducts, and complex stones, which increase the likelihood of complications. Surgeon experience also plays a crucial role, with lower complication rates observed among surgeons who have performed at least 70 LCBDE procedures over a 10-year period. Additionally, procedure-related factors such as the transcystic (TC) approach, primary suture (PS) closure, and intraoperative imaging were found to reduce complication rates. LCBDE remains a promising approach for managing bile duct stones, particularly when patient, surgeon, and procedural factors are optimized.