Hospital preparedness assessment for road traffic accidents with mass casualties: a cross-sectional study in Kurdistan Province, Iran.

BACKGROUND: Road traffic accidents (RTAs) are predicted to become the world's seventh leading cause of death by 2030. Given the significant impact of RTAs on public health, effective hospital preparedness plays a pivotal role in managing and mitigating associated health and life-threatening issues. This study aims to meticulously evaluate the preparedness of selected hospitals in western Iran to handle road traffic accidents with mass casualties (RTAs-MC). METHODS: The study employed a descriptive-analytical approach, utilizing a reliable and valid questionnaire to measure hospitals' preparedness levels. Descriptive statistics (frequency distribution and mean) were utilized to provide an overview of the data, followed by analytical statistics (Spearman correlation test) to examine the relationship between hospital preparedness and its dimensions with the hospital profile. Data analysis, performed using SPSS software, categorized preparedness levels as weak, moderate, or high. RESULTS: The study found that hospitals in Kurdistan province had a favorable preparedness level (70.30) to respond to RTAs-MC. The cooperation and coordination domain had the highest preparedness level (98.75), while the human resource management (59.44) and training and exercise (54.00) domains had the lowest preparedness levels. The analysis revealed a significant relationship between hospital preparedness and hospital profile, including factors such as hospital specialty, number of beds, ambulances, staff, and specialized personnel, such as emergency medicine specialists. CONCLUSION: Enhancing preparedness for RTAs-MC necessitates developing response plans to improve hospital profile, considering the region's geographic and topographic features, utilizing past experiences and lessons learned, implementing of Hospital Incident Command System (HICS), providing medical infrastructure and equipment, establishing communication channels, promoting cooperation and coordination, and creating training and exercise programs.

The impact of ketogenic diet on some metabolic and non-metabolic diseases: Evidence from human and animal model experiments.

The ketogenic diet (KD) is recognized as minimum carbohydrate and maximum fat intakes, which leads to ketosis stimulation, a state that is thought to metabolize fat more than carbohydrates for energy supply. KD has gained more interest in recent years and is for many purposes, including weight loss and managing serious diseases like type 2 diabetes. On the other hand, many believe that KD has safety issues and are uncertain about the health drawbacks. Thus, the outcomes of the effect of KD on metabolic and non-metabolic disease remain disputable. The current narrative review aims to evaluate the effect of KD on several diseases concerning the human health. To our best knowledge, the first report aims to investigate the efficacy of KD on multiple human health issues including type 2 diabetes and weight loss, cardiovascular disease, kidney failure and hypertension, non-alcoholic fatty liver, mental problem, oral health, libido, and osteoporosis. The literature searches were performed in Databases, PubMed, Scopus, and web of Science looking for both animal and human model designs. The results heterogeneity seems to be explained by differences in diet composition and duration. Also, the available findings may show that proper control of carbohydrates, a significant reduction in glycemic control and glycated hemoglobin, and weight loss by KD can be an approach to improve diabetes and obesity, hypertension, non-alcoholic fatty liver, PCOS, libido, oral health, and mental problem if isocaloric is considered. However, for some other diseases like cardiovascular disease and osteoporosis, more robust data are needed. Therefore, there is robust data to support the notion that KD can be effective for some metabolic and non-metabolic diseases but not for all of them. So they have to be followed cautiously and under the supervision of health professionals.

Modelling Planned vs. Actual Start Time to Control the Efficiency of Surgery.

To control the efficiency of surgery, it is ideal to have actual starting times of surgical procedures coincide with their planned start time. This study analysed over 4 years of data from a large metropolitan hospital and identified factors associated with surgery commencing close to the planned starting time via statistical modelling. A web application comprising novel visualisations to complement the statistical analysis was developed to facilitate translational impact by providing theatre administrators and clinical staff with a tool to assist with continuous quality improvement.

An Approach for Generating Realistic Australian Synthetic Healthcare Data.

Healthcare data is a scarce resource and access is often cumbersome. While medical software development would benefit from real datasets, the privacy of the patients is held at a higher priority. Realistic synthetic healthcare data can fill this gap by providing a dataset for quality control while at the same time preserving the patient's anonymity and privacy. Existing methods focus on American or European patient healthcare data but none is exclusively focused on the Australian population. Australia is a highly diverse country that has a unique healthcare system. To overcome this problem, we used a popular publicly available tool, Synthea, to generate disease progressions based on the Australian population. With this approach, we were able to generate 100,000 patients following Queensland (Australia) demographics.

A Step Towards Building Health Digital Twins: Patient Phenotype Representation for Health Outcome Prediction.

Precision medicine aims to provide more effective interventions and preventive options to patients by considering their individual risk factors and by employing available evidence. This proof of concept study presents an approach towards generating holistic virtual representations of patients, a.k.a. health digital twins. The developed virtual representations were applied in two health outcome prediction case studies for readmission and in-hospital mortality predictions. The results demonstrated the effectiveness of the virtual representations to facilitate predictive analysis in practicing precision medicine.

Improving preoperative prediction of surgery duration.

BACKGROUND: Operating rooms (ORs) are one of the costliest units in a hospital, therefore the cumulative consequences of any kind of inefficiency in OR management lead to a significant loss of revenue for the hospital, staff dissatisfaction, and patient care disruption. One of the possible solutions to improving OR efficiency is knowing a reliable estimate of the duration of operations. The literature suggests that the current methods used in hospitals, e.g., a surgeon's estimate for the given surgery or taking the average of only five previous records of the same procedure, have room for improvement. METHODS: We used over 4 years of elective surgery records (n = 52,171) from one of the major metropolitan hospitals in Australia. We developed robust Machine Learning (ML) approaches to provide a more accurate prediction of operation duration, especially in the absence of surgeon's estimation. Individual patient characteristics and historic surgery information attributed to medical records were used to train predictive models. A wide range of algorithms such as Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were tested for predicting operation duration. RESULTS: The results show that the XGBoost model provided statistically significantly less error than other compared ML models. The XGBoost model also reduced the total absolute error by 6854 min (i.e., about 114 h) compared to the current hospital methods. CONCLUSION: The results indicate the potential of using ML methods for reaching a more accurate estimation of operation duration compared to current methods used in the hospital. In addition, using a set of realistic features in the ML models that are available at the point of OR scheduling enabled the potential deployment of the proposed approach.

Sensorial, textural, and rheological analysis of novel pistachio-based chocolate formulations by quantitative descriptive analysis.

Principal component analysis (PCA) was used to investigate the effects of pistachio oil (7.5 and 15%), xanthan gum (0 and 0.3%), distillated monoglyceride (0.5 and 1%), and cocoa butter (7.5 and 15%) on the sensorial descriptors of spread based on pistachio oil. The response variables were the most significant spread texture attributes: hardness, graininess, meltability, adhesiveness to spoon, adhesiveness to mouth, spreadability, fluidity, and oiliness. PCA revealed that the first two principal components explained 90% or more of the variance between the data. The first principal component was dominated by the descriptors' adhesiveness and hardness on the positive side and the descriptors' oiliness and fluidness on the negative side. The descriptor spreadability had a high positive loading on the second principal component. Herschel-Balkley and power law models were fitted to confirm the sensory evaluation results on different formulations. In the current research, the power law model seemed to be more accurate for fitting the samples. In terms of the selected texture attributes determined by the sensory evaluation, using component plot, the optimum combination of variables was found as follows: 15 pistachio oil, 7.5% cocoa butter, 0.3% xanthan gum, and 1% distilled monoglyceride that produced desirable spreads that mimic commercial spread.

The garlic extract-loaded nanoemulsion: Study of physicochemical, rheological, and antimicrobial properties and its application in mayonnaise.

In this research, garlic extract (GE)-loaded water-in-oil nanoemulsion was used as a novel preservative and antioxidant in mayonnaise. GE (5%, 10%, 15%, and 25%) as a dispersed phase, olive oil as a continuous phase, and polyglycerol polyricinoleate (PGPR) as a low HLB surfactant, with a constant surfactant/garlic extract ratio (1:1), were used in the formulations of water-in-oil nanoemulsions. The properties of the active nanoemulsion, including droplet size, free radical scavenging capacity, antimicrobial activity against gram-positive (Staphylococcus aureus [25923 ATCC]), and gram-negative (Escherichia coli H7 O157 [700728 ATCC]) were evaluated. The results showed that the mean droplet size of nanoemulsion increased from 62 to 302 nm and antioxidant capacity was also improved from 95.43% to 98.25% by increasing GE level from 5% to 25%. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) showed that antimicrobial activity against S. aureus could be observed only in high levels of GE (25%) in the formulation of nanoemulsion. The results of the total count analysis showed that the GE-loaded nanoemulsion (NEGE) was effective against the microorganisms, particularly after 4 months of storage. The incorporation of GE and NEGE did not affect significantly the acidity of different mayonnaise samples; however, they affected the concentration of the primary product of lipid oxidation. Adding GE and NGE did not significantly affect the rheological properties of mayonnaise and all samples showed shear-thinning behavior. Sensory evaluation showed that the samples with NEGE had higher scores in texture, spreadability, and mouthfeel, while the control samples had better scores in appearance, color, taste, and total acceptance. In general, the samples containing free GE (not encapsulated) had the lowest scores in all organoleptic characteristics.

Optimization of solvent evaporation method in liposomal nanocarriers loaded-garlic essential oil (Allium sativum): Based on the encapsulation efficiency, antioxidant capacity, and instability.

This study is aimed to optimise the preparation factors, such as sonication time (5-20 min), cholesterol to lecetin ratio (CHLR) (0.2-0.8), and essential oil content (0.1-0.3 g/100 g) in solvent evaporation method for formulation of liposomal nanocarriers containing garlic essential oil (GEO) in order to find the highest encapsulation efficiency and stability with strongest antioxidant and antimicrobial activity. The droplet size, zeta potential, encapsulation efficiency, turbidity, changes in turbidity after storage (as a measure of instability), antioxidant capacity, and antimicrobial activity were measured for all prepared samples of nanoliposome. The sonication time is recognised as the most effective factor on the droplet size, zeta potential, encapsulation efficiency, turbidity, and instability while CHLR was the most effective factor on zeta potential and instability. The content of GEO significantly affected the antioxidant and antimicrobial activity in particular against gram-negative bacteria (Escherichia coli). The results of FTIR based on the identification of functional groups confirmed the presence of GEO in the spectra of the prepared nanoliposome and also it was not observed the interaction between the components of the nanoliposome. The overall optimum conditions were determined by response surface methodology (RSM) as the predicted values of the studied factors (sonication time: 18.99 min, CHLR: 0.59 and content of GEO: 0.3 g/100 g) based on obtaining the highest stability and efficiency as well as strongest antioxidant and antimicrobial activity.

A discrete event simulation for improving operating theatre efficiency.

BACKGROUND: Increasing demand in healthcare services has posed excessive burden on healthcare professionals and hospitals with finite capacity. Operating theatres are critical resources within hospitals that can become bottlenecks in patient flow during high demand conditions. There are substantial costs associated with running operating theatres that include keeping professional staff ready, maintaining operating theatres and equipment, environmental services and cleaning of operating theatres and recovery rooms, and these costs can increase if theatres are not used efficiently. In addition to cost, operating theatre inefficiency can result in surgery cancelations and delays, and eventually, poor patient outcomes, which can be exacerbated under the increase in demand. METHODS: The allocation of surgeries to operating theatres is explored using a simulation model for patients admitted to inpatient beds and sent for surgery. We proposed a discrete event simulation (DES) to model incoming flow to operating theatres of a major metropolitan hospital. We assessed how changing the configuration of surgery at the target hospital affects Key Performance Indicators relating to theatre efficiency. In particular, the model was used to assess impacts of six different scenarios by defining new/hypothetical theatre case-mix, opening and closing times of theatres, turnaround (changeover) time, and repurposing the theatres. Target performance metrics included theatre utilisation, pre-operative length-of-stay, average reclaimable time, the percentage of total theatre time in a year that could be reclaimed, and patient waiting time. A web-based application was developed that allows testing user-defined scenarios and interactive analysis of the results. RESULTS: Extending the opening hours of operating theatres by 1 hour almost halved the number of deferred electives as well as over-run cases but at the expense of reduced theatre utilisation. A one-hour reduction in opening hours resulted in 10 times more deferred elective cases and a negligible increase in theatre utilisation. Reducing turnaround time by 50% had positive effects on theatre management: increased utilisation and less deferred and over-run elective cases. Pooling emergency theatres did not affect theatre utilisation but resulted in a considerable reduction in average wait time and the proportion of the delayed emergency cases. CONCLUSIONS: The developed DES-based simulation model of operating theatres along with the web-based user interface provided a useful interrogation tool for theatre management and hospital executive teams to assess new operational strategies. The next step is to embed simulation as ongoing practices in theatre planning workflow, allowing operational managers to use the model outputs to increase theatre utilisation, and reduce cancellations and schedule changes. This can support hospitals in providing services as efficiently and effectively as possible.

New bed configurations and discharge timing policies: A hospital-wide simulation.

OBJECTIVE: Optimising patient flow is becoming an increasingly critical issue as patient demand fluctuates in healthcare systems with finite capacity. Simulation provides a powerful tool to fine-tune policies and investigate their impact before any costly intervention. METHODS: A hospital-wide discrete event simulation is developed to model incoming flow from ED and elective units in a busy metropolitan hospital. The impacts of two different policies are investigated using this simulation model: (i) varying inpatient bed configurations and a load sharing strategy among a cluster of wards within a medical department and (ii) early discharge strategies on inpatient bed access. Several clinically relevant bed configurations and early discharge scenarios are defined and their impact on key performance metrics are quantified. RESULTS: Sharing beds between wards reduced the average and total ED length of stay (LOS) by 21% compared to having patients queue for individual wards. The current baseline performance level could be maintained by using fewer beds when the load sharing approach was imposed. Earlier discharge of inpatients resulted in reducing average patient ED LOS by approximately 16% and average patient waiting time by 75%. Specific time-based discharge targets led to greater improvements in flow compared to blanket approaches of discharging all patients 1 or 2 hours earlier. CONCLUSIONS: ED access performance for admitted patients can be improved by modifying downstream capacity or inpatient discharge times. The simulation model was able to quantify the potential impacts of such policies on patient flow and to provide insights for future strategic planning.

Formulation of nanoemulsion carriers containing Pennyroyal (Mentha pulegium) and Gijavash (Froriepia subpinnata) essential oils for enriching Doogh (Iranian dairy drink).

Today, the increasing use of chemical preservatives in foods is considered one of the main problems in food industries. This study aimed to produce the pasteurised Doogh (Iranian yogurt drink) containing a nanoemulsion of essential oil (EO) with appropriate quality. A factorial test based on a completely randomised design with two treatments in three levels, including EO type (pennyroyal, Gijavash, and their equal combination) and a control sample was applied to assess the physicochemical and sensory properties of Doogh. The highest negative zeta potential and antioxidant activity percentage were observed in the sample containing the nanoemulsion of pennyroyal and enriched with a combination of two essential oils. The microbial evaluation results indicated that the total microorganism count was minimised in the Doogh containing the nanoemulsion of Gijavash. The nanoemulsions of pennyroyal and Gijavash can be added into Doogh formulation to produce a new product with maximum sensory acceptability.

The physicochemical properties of the spirulina-wheat germ-enriched high-protein functional beverage based on pear-cantaloupe juice.

The formulation of a novel functional juice, enriched with wheat germ powder and spirulina algae and based on cantaloupe and pear juice, was optimized by D-optimal combined design. Firstly, sensory evaluation was performed by hedonic test to evaluate the organoleptic properties, and organoleptically desirable samples were screened for further experiments. Various chemical experiments including PH, acidity, formalin index, total phenol, flavonoids, antioxidant capacity, mineral contents (Fe, Zn, Ca, P, K, Mg, and Cu), and fatty acids profile were evaluated. The steady shear flow rheological test also was performed on the screened samples. The results of sensory evaluation showed that the samples containing 1% spirulina and wheat germ had the highest organoleptic score. The results of physicochemical tests on the selected samples showed that the addition of spirulina and wheat germ powder had little effect on pH, acidity, and formalin index but they affected brix, dry matter, and protein content. Also, the addition of spirulina and wheat germ powder, changed the amounts of antioxidant capacity (from 90 to 98%), total phenol (from 4 to 22 mg GAE/g), and flavonoid content (from 5 to 15 mg/L) in the functional beverages. Furthermore, the results of rheological tests showed that the addition of wheat germ powder in the functional fruit juices increased apparent viscosity however; spirulina did not affect important change in rheological properties. The GC-Mass analysis presented fatty acid profiles of the functional beverages and confirmed the presence of polyunsaturated fatty acids (for example decanoic acid and heptadecanoic acid) in the samples.

Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment.

The Electronic Medical Record (EMR) provides an opportunity to manage patient care efficiently and accurately. This includes clinical decision support tools for the timely identification of adverse events or acute illnesses preceded by deterioration. This paper presents a machine learning-driven tool developed using real-time EMR data for identifying patients at high risk of reaching critical conditions that may demand immediate interventions. This tool provides a pre-emptive solution that can help busy clinicians to prioritize their efforts while evaluating the individual patient risk of deterioration. The tool also provides visualized explanation of the main contributing factors to its decisions, which can guide the choice of intervention. When applied to a test cohort of 18,648 patient records, the tool achieved 100% sensitivity for prediction windows 2-8 h in advance for patients that were identified at 95%, 85% and 70% risk of deterioration.

Daily surgery caseload prediction: towards improving operating theatre efficiency.

BACKGROUND: In many hospitals, operating theatres are not used to their full potential due to the dynamic nature of demand and the complexity of theatre scheduling. Theatre inefficiencies may lead to access block and delays in treating patients requiring critical care. This study aims to employ operating theatre data to provide decision support for improved theatre management. METHOD: Historical observations are used to predict long-term daily surgery caseload in various levels of granularity, from emergency versus elective surgeries to clinical specialty-level demands. A statistical modelling and a machine learning-based approach are developed to estimate daily surgery demand. The statistical model predicts daily demands based on historical observations through weekly rolling windows and calendar variables. The machine learning approach, based on regression algorithms, learns from a combination of temporal and sequential features. A de-identified data extract of elective and emergency surgeries at a major 783-bed metropolitan hospital over four years was used. The first three years of data were used as historical observations for training the models. The models were then evaluated on the final year of data. RESULTS: Daily counts of overall surgery at a hospital-level could be predicted with approximately 90% accuracy, though smaller subgroups of daily demands by medical specialty are less predictable. Predictions were generated on a daily basis a year in advance with consistent predictive performance across the forecast horizon. CONCLUSION: Predicting operating theatre demand is a viable component in theatre management, enabling hospitals to provide services as efficiently and effectively as possible to obtain the best health outcomes. Due to its consistent predictive performance over various forecasting ranges, this approach can inform both short-term staffing choices as well as long-term strategic planning.

Garlic essential oil-based nanoemulsion carrier: Release and stability kinetics of volatile components.

An O/W nanoemulsion of garlic essential oil (GEO) at different oil-to-emulsion (O/E) ratios (5%, 10%, 15%, and 25%) was formulated to protect the volatile components of GEO. The effects of O/E ratios on the encapsulation efficiency (EE%) of volatile compounds and droplet size of nanoemulsions were studied. The results showed that with increasing in E/O ratio, droplet size increased while EE% decreased so that the droplet size was below 100 nm for all samples and the EE% was almost above 80% for most samples. The effects of various factors such as temperature (5°C-45°C), pH values (3-7), ionic strength (0-500 mM), and O/E ratios (5%-25%) on kinetic of nanoemulsions stability were studied. Reducing pH values and raising the temperature, ionic strength, and O/E ratios intensified the instability process and constant rate of instability in all nanoemulsions. The effects of temperature and O/E ratios on the release kinetics of volatile components were evaluated over time, and kinetic parameters such as release rate constant (k), Q10, and activation energy (Ea) were calculated in which results showed a zero-degree model to describe the release kinetic behavior of most nanoemulsions. Both temperature and O/E ratios factors as well as their interaction (which had a synergistic effect) had a significant effect on increasing the release rate of volatiles so that the degree of reaction rate was changed from zero to the first order at simultaneous high levels of both factors. FT-IR spectroscopy was carried out to study interactions among nanoemulsion ingredients. The presence of sulfur-containing functional groups of garlic oil (thiosulphate, diallyl trisulfide, etc.) in nanoemulsions was confirmed by FT-IR.

Matching patients to clinical trials using semantically enriched document representation.

Recruiting eligible patients for clinical trials is crucial for reliably answering specific questions about medical interventions and evaluation. However, clinical trial recruitment is a bottleneck in clinical research and drug development. Our goal is to provide an approach towards automating this manual and time-consuming patient recruitment task using natural language processing and machine learning techniques. Specifically, our approach extracts key information from series of narrative clinical documents in patient's records and collates helpful evidence to make decisions on eligibility of patients according to certain inclusion and exclusion criteria. Challenges in applying narrative clinical documents such as differences in reporting styles and sub-languages are addressed by enriching them with knowledge from domain ontologies in the form of semantic vector representations. We show that a machine learning model based on Multi-Layer Perceptron (MLP) is more effective for the task than five other neural networks and four conventional machine learning models. Our approach achieves overall micro-F1-Score of 84% for 13 different eligibility criteria. Our experiments also indicate that semantically enriched documents are more effective than using original documents for cohort selection. Our system provides an end-to-end machine learning-based solution that achieves comparable results with the state-of-the-art which relies on hand-crafted rules or data-centric engineered features.

Quantifying semantic similarity of clinical evidence in the biomedical literature to facilitate related evidence synthesis.

OBJECTIVE: Published clinical trials and high quality peer reviewed medical publications are considered as the main sources of evidence used for synthesizing systematic reviews or practicing Evidence Based Medicine (EBM). Finding all relevant published evidence for a particular medical case is a time and labour intensive task, given the breadth of the biomedical literature. Automatic quantification of conceptual relationships between key clinical evidence within and across publications, despite variations in the expression of clinically-relevant concepts, can help to facilitate synthesis of evidence. In this study, we aim to provide an approach towards expediting evidence synthesis by quantifying semantic similarity of key evidence as expressed in the form of individual sentences. Such semantic textual similarity can be applied as a key approach for supporting selection of related studies. MATERIAL AND METHODS: We propose a generalisable approach for quantifying semantic similarity of clinical evidence in the biomedical literature, specifically considering the similarity of sentences corresponding to a given type of evidence, such as clinical interventions, population information, clinical findings, etc. We develop three sets of generic, ontology-based, and vector-space models of similarity measures that make use of a variety of lexical, conceptual, and contextual information to quantify the similarity of full sentences containing clinical evidence. To understand the impact of different similarity measures on the overall evidence semantic similarity quantification, we provide a comparative analysis of these measures when used as input to an unsupervised linear interpolation and a supervised regression ensemble. In order to provide a reliable test-bed for this experiment, we generate a dataset of 1000 pairs of sentences from biomedical publications that are annotated by ten human experts. We also extend the experiments on an external dataset for further generalisability testing. RESULTS: The combination of all diverse similarity measures showed stronger correlations with the gold standard similarity scores in the dataset than any individual kind of measure. Our approach reached near 0.80 average Pearson correlation across different clinical evidence types using the devised similarity measures. Although they were more effective when combined together, individual generic and vector-space measures also resulted in strong similarity quantification when used in both unsupervised and supervised models. On the external dataset, our similarity measures were highly competitive with the state-of-the-art approaches developed and trained specifically on that dataset for predicting semantic similarity. CONCLUSION: Experimental results showed that the proposed semantic similarity quantification approach can effectively identify related clinical evidence that is reported in the literature. The comparison with a state-of-the-art method demonstrated the effectiveness of the approach, and experiments with an external dataset support its generalisability.

A Decision Support System for Pathology Test Result Reviews in an Emergency Department to Support Patient Safety and Increase Efficiency.

The review of pathology test results for missed diagnoses in Emergency Departments is time-consuming, laborious, and can be inaccurate. An automated solution, with text mining and clinical terminology semantic capabilities, was developed to provide clinical decision support. The system focused on the review of microbiology test results that contained information on culture strains and their antibiotic sensitivities, both of which can have a significant impact on ongoing patient safety and clinical care. The system was highly effective at identifying abnormal test results, reducing the number of test results for review by 92%. Furthermore, the system reconciled antibiotic sensitivities with documented antibiotic prescriptions in discharge summaries to identify patient follow-ups with a 91% F-measure - allowing for the accurate prioritization of cases for review. The system dramatically increases accuracy, efficiency, and supports patient safety by ensuring important diagnoses are recognized and correct antibiotics are prescribed.