Search results for: health-care logistics
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 1916

Search results for: health-care logistics

896 Generalized Additive Model for Estimating Propensity Score

Authors: Tahmidul Islam

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Propensity Score Matching (PSM) technique has been widely used for estimating causal effect of treatment in observational studies. One major step of implementing PSM is estimating the propensity score (PS). Logistic regression model with additive linear terms of covariates is most used technique in many studies. Logistics regression model is also used with cubic splines for retaining flexibility in the model. However, choosing the functional form of the logistic regression model has been a question since the effectiveness of PSM depends on how accurately the PS been estimated. In many situations, the linearity assumption of linear logistic regression may not hold and non-linear relation between the logit and the covariates may be appropriate. One can estimate PS using machine learning techniques such as random forest, neural network etc for more accuracy in non-linear situation. In this study, an attempt has been made to compare the efficacy of Generalized Additive Model (GAM) in various linear and non-linear settings and compare its performance with usual logistic regression. GAM is a non-parametric technique where functional form of the covariates can be unspecified and a flexible regression model can be fitted. In this study various simple and complex models have been considered for treatment under several situations (small/large sample, low/high number of treatment units) and examined which method leads to more covariate balance in the matched dataset. It is found that logistic regression model is impressively robust against inclusion quadratic and interaction terms and reduces mean difference in treatment and control set equally efficiently as GAM does. GAM provided no significantly better covariate balance than logistic regression in both simple and complex models. The analysis also suggests that larger proportion of controls than treatment units leads to better balance for both of the methods.

Keywords: accuracy, covariate balances, generalized additive model, logistic regression, non-linearity, propensity score matching

Procedia PDF Downloads 350
895 Examining Pre-Consumer Textile Waste Recycling, Barriers to Implementation, and Participant Demographics: A Review of Literature

Authors: Madeline W. Miller

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The global textile industry produces pollutants in the form of liquid discharge, solid waste, and emissions into the natural environment. Textile waste resulting from garment production and other manufacturing processes makes a significant contribution to the amount of waste landfilled globally. While the majority of curbside and other convenient recycling methods cater to post-consumer paper and plastics, pre-consumer textile waste is often discarded with trash and is commonly classified as ‘other’ in municipal solid waste breakdowns. On a larger scale, many clothing manufacturers and other companies utilizing textiles have not yet identified or began using the most sustainable methods for discarding their post-industrial, pre-consumer waste. To lessen the amount of waste sent to landfills, there are post-industrial, pre-consumer textile waste recycling methods that can be used to give textiles a new life. This process requires that textile and garment manufacturers redirect their waste to companies that use industrial machinery to shred or fiberize these materials in preparation for their second life. The goal of this literature review is to identify the recycling and reuse challenges faced by producers within the clothing and textile industry that prevent these companies from utilizing the described recycling methods, causing them to opt for landfill. The literature analyzed in this review reflects manufacturer sentiments toward waste disposal and recycling. The results of this review indicate that the cost of logistics is the determining factor when it comes to companies recycling their pre-consumer textile waste and that the most applicable and successful textile waste recycling methods require a company separate from the manufacturer to account for waste production, provide receptacles for waste, arrange waste transport, and identify a secondary use for the material at a price-point below that of traditional waste disposal service.

Keywords: leadership demographics, post-industrial textile waste, pre-consumer textile waste, industrial shoddy

Procedia PDF Downloads 130
894 Integrating Natural Language Processing (NLP) and Machine Learning in Lung Cancer Diagnosis

Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

Procedia PDF Downloads 64
893 Investigating the Factors Affecting Generalization of Deep Learning Models for Plant Disease Detection

Authors: Praveen S. Muthukumarana, Achala C. Aponso

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A large percentage of global crop harvest is lost due to crop diseases. Timely identification and treatment of crop diseases is difficult in many developing nations due to insufficient trained professionals in the field of agriculture. Many crop diseases can be accurately diagnosed by visual symptoms. In the past decade, deep learning has been successfully utilized in domains such as healthcare but adoption in agriculture for plant disease detection is rare. The literature shows that models trained with popular datasets such as PlantVillage does not generalize well on real world images. This paper attempts to find out how to make plant disease identification models that generalize well with real world images.

Keywords: agriculture, convolutional neural network, deep learning, plant disease classification, plant disease detection, plant disease diagnosis

Procedia PDF Downloads 126
892 An Exploration of the Emergency Staff’s Perceptions and Experiences of Teamwork and the Skills Required in the Emergency Department in Saudi Arabia

Authors: Sami Alanazi

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Teamwork practices have been recognized as a significant strategy to improve patient safety, quality of care, and staff and patient satisfaction in healthcare settings, particularly within the emergency department (ED). The EDs depend heavily on teams of interdisciplinary healthcare staff to carry out their operational goals and core business of providing care to the serious illness and injured. The ED is also recognized as a high-risk area in relation to service demand and the potential for human error. Few studies have considered the perceptions and experiences of the ED staff (physicians, nurses, allied health professionals, and administration staff) about the practice of teamwork, especially in Saudi Arabia (SA), and no studies have been conducted to explore the practices of teamwork in the EDs. Aim: To explore the practices of teamwork from the perspectives and experiences of staff (physicians, nurses, allied health professionals, and administration staff) when interacting with each other in the admission areas in the ED of a public hospital in the Northern Border region of SA. Method: A qualitative case study design was utilized, drawing on two methods for the data collection, comprising of semi-structured interviews (n=22) with physicians (6), nurses (10), allied health professionals (3), and administrative members (3) working in the ED of a hospital in the Northern Border region of SA. The second method is non-participant direct observation. All data were analyzed using thematic analysis. Findings: The main themes that emerged from the analysis were as follows: the meaningful of teamwork, reasons of teamwork, the ED environmental factors, the organizational factors, the value of communication, leadership, teamwork skills in the ED, team members' behaviors, multicultural teamwork, and patients and families behaviors theme. Discussion: Working in the ED environment played a major role in affecting work performance as well as team dynamics. However, Communication, time management, fast-paced performance, multitasking, motivation, leadership, and stress management were highlighted by the participants as fundamental skills that have a major impact on team members and patients in the ED. It was found that the behaviors of the team members impacted the team dynamics as well as ED health services. Behaviors such as disputes among team members, conflict, cooperation, uncooperative members, neglect, and emotions of the members. Besides that, the behaviors of the patients and their accompanies had a direct impact on the team and the quality of the services. In addition, the differences in the cultures have separated the team members and created undesirable gaps such the gender segregation, national origin discrimination, and similarity and different in interests. Conclusion: Effective teamwork, in the context of the emergency department, was recognized as an essential element to obtain the quality of care as well as improve staff satisfaction.

Keywords: teamwork, barrier, facilitator, emergencydepartment

Procedia PDF Downloads 117
891 An Evaluation of Rational Approach to Management by Objectives in Construction Contracting Organisation

Authors: Zakir H. Shaik, Punam L. Vartak

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Management By Objectives (MBO) is a management technique in which objectives of an organisation are conveyed to the employees to establish the individual goals. These objectives and goals are then monitored and assessed jointly by management and the employee time to time. This tool can be used for planning, monitoring as well as for performance appraisal. The success of an organisation is largely dependent on its’s Vision. Thus, it is of paramount importance to achieve the realm of vision through a mission which is well crafted within the organisation to address the objectives. The success of the mission depends upon how realistic and action oriented philosophical approach, an organisation caters to; and how the individual goals are set to track and meet the objectives. Thus, focused and passionate efforts of the team, assigned for the mission, are an absolute obligation for achieving the vision of any organisation. Any construction site is generally a controlled disorder having huge investments, resources and logistics involved. The Construction progression is time-consuming with many isolated as well as interconnected activities. Traditional MBO approach can be unsuccessful if planning and control is non-realistic and inflexible. Moreover, the Construction Industry is far behind understanding these concepts. It is important to address the employee engagement in defining and creating awareness to achieve the targets. Besides, current economic environment and competitive world demands refined management tools to achieve profit, growth and survival of the business. Therefore, the necessity of rational MBO becomes vital part towards the success of an organisation. This paper details about the philosophical assumptions to develop the grounded theory in lieu of achieving objectives through RATIONAL MBO approach in Construction Contracting Organisations. The goals and objectives of the Construction Contracting Organisations can be achieved efficiently by adopting this RATIONAL MBO approach, as those are based on realistic, logical and balanced assumptions.

Keywords: growth, leadership, management by objectives, Management By Objectives (MBO), profit, rational

Procedia PDF Downloads 135
890 Digitalized Cargo Coordination to Eliminate Emissions in the Shipping Ecosystem: A System Dynamical Approach

Authors: Henry Schwartz, Bogdan Iancu, Magnus Gustafsson, Johan Lilius

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The shipping sector generates significant amounts of carbon emissions on annual basis. The excess amount of carbon dioxide is harmful for both the environment and the society, and partly for that reason, there is acute interest to decrease the volume of anthropogenic carbon dioxide emissions in shipping. The usage of the existing cargo carrying capacity can be maximized, and the share of time used in actual transportation operations could be increased if the whole transportation and logistics chain was optimized with the aid of information sharing done through a centralized marketplace and an information-sharing platform. The outcome of this change would be decreased carbon dioxide emission volumes produced per each metric ton of cargo transported by a vessel. Cargo coordination is a platform under development that matches the need for waterborne transportation services with the ships that operate at a given moment in time. In this research, the transition towards adopting cargo coordination is modelled with system dynamics. The model encompasses the complex supply-demand relationships of ship operators and cargo owners. The built scenarios predict the pace at which different stakeholders start using the digitalized platform and by doing so reduce the amount of annual CO2 emissions generated. To improve the reliability of the results, various sensitivity analyses considering the pace of transition as well as the overall impact on the environment (carbon dioxide emissions per amount of cargo transported) are conducted. The results of the study can be used to support investors and politicians in decision making towards more environmentally sustainable solutions. In addition, the model provides concepts and ideas for a wider discussion considering the paths towards carbon neutral transportation.

Keywords: carbon dioxide emissions, energy efficiency, sustainable transportation, system dynamics

Procedia PDF Downloads 129
889 The Lived Experience of Pregnant Saudi Women Carrying a Fetus with Structural Abnormalities

Authors: Nasreen Abdulmannan

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Fetal abnormalities are categorized as a structural abnormality, non-structural abnormality, or a combination of both. Fetal structural abnormalities (FSA) include, but are not limited, to Down syndrome, congenital diaphragmatic hernia, and cleft lip and palate. These abnormalities can be detected in the first weeks of pregnancy, which is almost around 9 - 20 weeks gestational. Etiological factors for FSA are unknown; however, transmitted genetic risk can be one of these factors. Consanguineous marriage often referred to as inbreeding, represents a significant risk factor for FSA due to the increased likelihood of deleterious genetic traits shared by both biological parents. In a country such as the Kingdom of Saudi Arabia (KSA), consanguineous marriage is high, which creates a significant risk of children being born with congenital abnormalities. Historically, the practice of consanguinity occurred commonly among European royalty. For example, Great Britain’s Queen Victoria married her German first cousin, Prince Albert of Coburg. Although a distant blood relationship, the United Kingdom’s Queen Elizabeth II married her cousin, Prince Philip of Greece and Denmark—both of them direct descendants of Queen Victoria. In Middle Eastern countries, a high incidence of consanguineous unions still exists, including in the KSA. Previous studies indicated that a significant gap exists in understanding the lived experiences of Saudi women dealing with an FSA-complicated pregnancy. Eleven participants were interviewed using a semi-structured interview format for this qualitative phenomenological study investigating the lived experiences of pregnant Saudi women carrying a child with FSA. This study explored the gaps in current literature regarding the lived experiences of pregnant Saudi women whose pregnancies were complicated by FSA. In addition, the researcher acquired knowledge about the available support and resources as well as the Saudi cultural perspective on FSA. This research explored the lived experiences of pregnant Saudi women utilizing Giorgi’s (2009) approach to data collection and data management. Findings for this study cover five major themes: (1) initial maternal reaction to the FSA diagnosis per ultrasound screening; (2) strengthening of the maternal relationship with God; (3) maternal concern for their child’s future; (4) feeling supported by their loved ones; and (5) lack of healthcare provider support and guidance. Future research in the KSA is needed to explore the network support for these mothers. This study recommended further clinical nursing research, nursing education, clinical practice, and healthcare policy/procedures to provide opportunities for improvement in nursing care and increase awareness in KSA society.

Keywords: fetal structural abnormalities, psychological distress, health provider, health care

Procedia PDF Downloads 140
888 Remote Patient Monitoring for Covid-19

Authors: Launcelot McGrath

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The Coronavirus disease 2019 (COVID-19) has spread rapidly around the world, resulting in high mortality rates and very large numbers of people requiring medical treatment in ICU. Management of patient hospitalisation is a critical aspect to control this disease and reduce chaos in the healthcare systems. Remote monitoring provides a solution to protect vulnerable and elderly high-risk patients. Continuous remote monitoring of oxygen saturation, respiratory rate, heart rate, and temperature, etc., provides medical systems with up-to-the-minute information about their patients' statuses. Remote monitoring also limits the spread of infection by reducing hospital overcrowding. This paper examines the potential of remote monitoring for Covid-19 to assist in the rapid identification of patients at risk, facilitate the detection of patient deterioration, and enable early interventions.

Keywords: remote monitoring, patient care, oxygen saturation, Covid-19, hospital management

Procedia PDF Downloads 88
887 Functionalized Nanoparticles for Biomedical Applications

Authors: Temesgen Geremew

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Functionalized nanoparticles have emerged as a revolutionary class of materials with immense potential in various biomedical applications. These engineered nanoparticles possess unique properties tailored to interact with biological systems, offering unprecedented opportunities in drug delivery, imaging, diagnostics, and therapy. This research delves into the design, synthesis, and characterization of functionalized nanoparticles for targeted biomedical applications. The primary focus lies on developing nanoparticles with precisely controlled size, surface chemistry, and biocompatibility for specific medical purposes. The research will also explore the crucial interaction of these nanoparticles with biological systems, encompassing cellular uptake, biodistribution, and potential toxicity evaluation. The successful development of functionalized nanoparticles holds the promise to revolutionize various aspects of healthcare. This research aspires to contribute significantly to this advancement by providing valuable insights into the design and application of these versatile materials within the ever-evolving field of biomedicine.

Keywords: nanoparticles, biomedicals, cancer, biocompatibility

Procedia PDF Downloads 46
886 Cardiovascular Disease Prediction Using Machine Learning Approaches

Authors: P. Halder, A. Zaman

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It is estimated that heart disease accounts for one in ten deaths worldwide. United States deaths due to heart disease are among the leading causes of death according to the World Health Organization. Cardiovascular diseases (CVDs) account for one in four U.S. deaths, according to the Centers for Disease Control and Prevention (CDC). According to statistics, women are more likely than men to die from heart disease as a result of strokes. A 50% increase in men's mortality was reported by the World Health Organization in 2009. The consequences of cardiovascular disease are severe. The causes of heart disease include diabetes, high blood pressure, high cholesterol, abnormal pulse rates, etc. Machine learning (ML) can be used to make predictions and decisions in the healthcare industry. Thus, scientists have turned to modern technologies like Machine Learning and Data Mining to predict diseases. The disease prediction is based on four algorithms. Compared to other boosts, the Ada boost is much more accurate.

Keywords: heart disease, cardiovascular disease, coronary artery disease, feature selection, random forest, AdaBoost, SVM, decision tree

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885 Ion Beam Sputtering Deposition of Inorganic-Fluoropolymer Nano-Coatings for Real-Life Applications

Authors: M. Valentini, D. Melisi, M. A. Nitti, R A. Picca, M. C. Sportelli, E. Bonerba, G. Casamassima, N. Cioffi, L. Sabbatini, G. Tantillo, A. Valentini

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In recent years antimicrobial coatings are receiving increasing attention due to their high demand in medical applications as well as in healthcare and hygiene. Research and technology are constantly involved to develop advanced finishing which can provide bacteriostatic growth without compromising the other typical properties of a textile as durability and non-toxicity, just to cite a few. Here we report on the antimicrobial coatings obtained, at room temperature and without the use of solvents, by means of the ion beam co-sputtering technique of an Ag target and a polytetrafluoroethylene one. In particular, such method allows to conjugate the well-known antimicrobial action of silver with the anti-stain and water-repellent properties of the fluoropolymer. Moreover, different Ag nanoparticle loadings (φ) were prepared by tuning the material deposition conditions achieving a fine control on film thickness and their antimicrobial/anti-stain properties.

Keywords: antimicrobial, ion beam sputtering, nanocoatings, anti-stain

Procedia PDF Downloads 377
884 Development of a Nurse Led Tranexamic Acid Administration Protocol for Trauma Patients in Rural South Africa

Authors: Christopher Wearmouth, Jacob Smith

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Administration of tranexamic acid (TXA) reduces all-cause mortality in trauma patients when given within 3 hours of injury. Due to geographical distance and lack of emergency medical services patients often present late, following trauma, to our emergency department. Additionally, we found patients that may have benefited from TXA did not receive it, often due to lack of staff awareness, staff shortages out of hours and lack of equipment for delivering infusions. Our objective was to develop a protocol for nurse-led administration of TXA in the emergency department. We developed a protocol using physiological observations along with criteria from the South African Triage Scale to allow nursing staff to identify patients with, or at risk of, significant haemorrhage. We will monitor the use of the protocol to ensure appropriate compliance and for any adverse events reported.

Keywords: emergency department, emergency nursing, rural healthcare, tranexamic acid, trauma, triage

Procedia PDF Downloads 208
883 An Empirical Examination on the Relationships between Organizational Justice, Affective Commitment and Absenteeism

Authors: Emine Öğüt, Mehtap Öztürk, Adem Öğüt

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Affective commitment is defined as a strong belief in and acceptance of the organization’s goals and values. Organizational justice is an antecedent of the organizational commitment and it has the potential to create powerful benefits for organizations and employees alike. When perceived unfairness among employees increases, affective commitment decreases and absenteeism increases accordingly. In this research, relationships between organizational justice perception, affective commitment and absenteeism is analysed. In this regard, a field study has been conducted over the physicians working in the hospitals of the Health Ministry and University Hospitals in the province of Konya. The partial least squares (PLS) method is used to analyse the survey data. The findings of the research shows that there is a positive statistically significant relationship between organizational justice perception and affective commitment while there is a negative statistically significant relationship between organizational justice and absenteeism.

Keywords: organizational justice, affective commitment, absenteeism, healthcare management

Procedia PDF Downloads 463
882 Evaluation of the Sterilization Practice in Liberal Dental Surgeons at Sidi Bel Abbes- Algeria

Authors: A. Chenafa, S. Boulenouar, M. Zitouni, M. Boukouria

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The sterilization of medical devices constitutes for all the medical professions, an inescapable obligation. It has for objective to prevent the infectious risk, both for the patient and for the medical team. The Dental surgeon as every healthcare professional has to master perfectly this subject and to train his staff to the various techniques of sterilization. It is the only way to assure the patients all the security for which they are entitled to wait when they undergo a dental care. It’s for it, that we undertook to lead an investigation aiming at estimating the sterilization practice at the dental surgeon of Sidi bel Abbes. The survey result showed a youth marked with the profession with a majority use of autoclave with cycle B and an almost total absence of the sterilization controls (test of Bowie and Dick). However, the majority of the dentists control and validate their sterilizers. Finally, our survey allowed us to describe some practices which must be improved regarding control, regarding qualification and regarding staff training. And suggestions were made in this sense.

Keywords: dental surgeon, medical devices, sterilization, survey

Procedia PDF Downloads 385
881 Automatic Queuing Model Applications

Authors: Fahad Suleiman

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Queuing, in medical system is the process of moving patients in a specific sequence to a specific service according to the patients’ nature of illness. The term scheduling stands for the process of computing a schedule. This may be done by a queuing based scheduler. This paper focuses on the medical consultancy system, the different queuing algorithms that are used in healthcare system to serve the patients, and the average waiting time. The aim of this paper is to build automatic queuing system for organizing the medical queuing system that can analyses the queue status and take decision which patient to serve. The new queuing architecture model can switch between different scheduling algorithms according to the testing results and the factor of the average waiting time. The main innovation of this work concerns the modeling of the average waiting time is taken into processing, in addition with the process of switching to the scheduling algorithm that gives the best average waiting time.

Keywords: queuing systems, queuing system models, scheduling algorithms, patients

Procedia PDF Downloads 333
880 Text Mining Past Medical History in Electrophysiological Studies

Authors: Roni Ramon-Gonen, Amir Dori, Shahar Shelly

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Background and objectives: Healthcare professionals produce abundant textual information in their daily clinical practice. The extraction of insights from all the gathered information, mainly unstructured and lacking in normalization, is one of the major challenges in computational medicine. In this respect, text mining assembles different techniques to derive valuable insights from unstructured textual data, so it has led to being especially relevant in Medicine. Neurological patient’s history allows the clinician to define the patient’s symptoms and along with the result of the nerve conduction study (NCS) and electromyography (EMG) test, assists in formulating a differential diagnosis. Past medical history (PMH) helps to direct the latter. In this study, we aimed to identify relevant PMH, understand which PMHs are common among patients in the referral cohort and documented by the medical staff, and examine the differences by sex and age in a large cohort based on textual format notes. Methods: We retrospectively identified all patients with abnormal NCS between May 2016 to February 2022. Age, gender, and all NCS attributes reports were recorded, including the summary text. All patients’ histories were extracted from the text report by a query. Basic text cleansing and data preparation were performed, as well as lemmatization. Very popular words (like ‘left’ and ‘right’) were deleted. Several words were replaced with their abbreviations. A bag of words approach was used to perform the analyses. Different visualizations which are common in text analysis, were created to easily grasp the results. Results: We identified 5282 unique patients. Three thousand and five (57%) patients had documented PMH. Of which 60.4% (n=1817) were males. The total median age was 62 years (range 0.12 – 97.2 years), and the majority of patients (83%) presented after the age of forty years. The top two documented medical histories were diabetes mellitus (DM) and surgery. DM was observed in 16.3% of the patients, and surgery at 15.4%. Other frequent patient histories (among the top 20) were fracture, cancer (ca), motor vehicle accident (MVA), leg, lumbar, discopathy, back and carpal tunnel release (CTR). When separating the data by sex, we can see that DM and MVA are more frequent among males, while cancer and CTR are less frequent. On the other hand, the top medical history in females was surgery and, after that, DM. Other frequent histories among females are breast cancer, fractures, and CTR. In the younger population (ages 18 to 26), the frequent PMH were surgery, fractures, trauma, and MVA. Discussion: By applying text mining approaches to unstructured data, we were able to better understand which medical histories are more relevant in these circumstances and, in addition, gain additional insights regarding sex and age differences. These insights might help to collect epidemiological demographical data as well as raise new hypotheses. One limitation of this work is that each clinician might use different words or abbreviations to describe the same condition, and therefore using a coding system can be beneficial.

Keywords: abnormal studies, healthcare analytics, medical history, nerve conduction studies, text mining, textual analysis

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879 Building Transparent Supply Chains through Digital Tracing

Authors: Penina Orenstein

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In today’s world, particularly with COVID-19 a constant worldwide threat, organizations need greater visibility over their supply chains more than ever before, in order to find areas for improvement and greater efficiency, reduce the chances of disruption and stay competitive. The concept of supply chain mapping is one where every process and route is mapped in detail between each vendor and supplier. The simplest method of mapping involves sourcing publicly available data including news and financial information concerning relationships between suppliers. An additional layer of information would be disclosed by large, direct suppliers about their production and logistics sites. While this method has the advantage of not requiring any input from suppliers, it also doesn’t allow for much transparency beyond the first supplier tier and may generate irrelevant data—noise—that must be filtered out to find the actionable data. The primary goal of this research is to build data maps of supply chains by focusing on a layered approach. Using these maps, the secondary goal is to address the question as to whether the supply chain is re-engineered to make improvements, for example, to lower the carbon footprint. Using a drill-down approach, the end result is a comprehensive map detailing the linkages between tier-one, tier-two, and tier-three suppliers super-imposed on a geographical map. The driving force behind this idea is to be able to trace individual parts to the exact site where they’re manufactured. In this way, companies can ensure sustainability practices from the production of raw materials through the finished goods. The approach allows companies to identify and anticipate vulnerabilities in their supply chain. It unlocks predictive analytics capabilities and enables them to act proactively. The research is particularly compelling because it unites network science theory with empirical data and presents the results in a visual, intuitive manner.

Keywords: data mining, supply chain, empirical research, data mapping

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878 Xiaflex (Collagenase) Impact on the Management of Dupuytren's Disease: Making the Case for Treatment in a Public Healthcare System

Authors: Anthony Barker, Roland Jiang

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Dupuytren’s contractures are a debilitating condition affecting the palmar fascia of the hand reducing its function. This case series looks at the minimally-invasive technique of Xiaflex injections and the outcome in a public health setting. 15 patients undertook collagenase injection (Xiaflex, C. histolyticum) injection over the period from September 2015 to May 2017 at Fairfield Hospital, NSW. Their reported outcome post injection and in follow-up was recorded as well as their satisfaction and likelihood to request the procedure in the future. Other treatment modalities include percutaneous needle aponeurotomy, limited palmar fasciotomy, and palmar fasciectomy. A literature review of cost-effectiveness was performed to compare Xiaflex suitability for waitlist reduction in a public setting given average waiting times in the public setting extend past 365 days.

Keywords: Dupuytrens Disease, xiaflex, collagenase, plastic surgery

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877 Seroprevalence of Cytomegalovirus among Pregnant Women in Islamabad, Pakistan

Authors: Hassan Waseem

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Cytomegalovirus (CMV) is ubiquitously distributed viral agent responsible for different clinical manifestations that may vary according to the immunologic status of the patient. CMV can cause morbidity and mortality among fetuses and patients with compromised immune system. A cross-sectional study was carried out in Islamabad to investigate the prevalence and risk factors associated with CMV infection among pregnant women. Blood samples of 172 pregnant women visiting Mother and Child Healthcare, Pakistan Institute of Medical Sciences (PIMS) Islamabad were taken. In present study, serum samples of the women were checked for CMV-specific IgG and IgM antibodies by enzyme linked immunosorbent assay (ELISA). Clinical, obstetrical and socio-demographical characteristics of the women were collected by using structured questionnaires. Out of 172 pregnant women included in the study, 171 (99.4%) were CMV specific IgG positive and 30 (17.4%) were found positive for CMV-IgM antibodies. The CMV has taken an endemic form in Pakistan so, routine screening of CMV among pregnant women is recommended.

Keywords: Cytomegalovirus, blood transfusion, ELISA, seroprevalence

Procedia PDF Downloads 346
876 How COVID 19 Changed Policy Makers Behavior toward Environmental Policy

Authors: Ammar Alrefaei

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The COVID-19 pandemic changed human life. The vast majority of the COVID effect was on the healthcare sector, but its impact on the global economy cannot be denied. In the field of environment, the pandemic may have a more significant impact on the environment than all environmental activity and policies of recent years. The pandemic consequences for the environment may be far more unpredictable than one might assume. In view of this, it is imperative for legislators from different states to be prepared to apply adequate measures to counteract such consequences. This article aimed to examine the obstacles to implementing effective environmental policies after the COVID-19 pandemic using different examples from different countries. Also, how adopting new initiatives, such as the Saudi Green Initiative and the Middle East Green Initiative, can help policymakers and legislators adopt new laws and policies. In addition, this paper reviewed the developing dangers to environmental protection after the pandemic and analyzed the major challenges to instrument active environmental policies during COVID-19 and in the world after COVID.

Keywords: environmental policy, environment law, green initiative, COVID 19

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875 Complicated Sinusitis with Sphenopalatine Artery Thrombosis in a Covid-19 Patient

Authors: Sara Mahmood, Omar Ahmed, Youssef Aladham, Moustafa Abdelnaby

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The varied complications of COVID-19 present an ongoing challenge to healthcare professionals. A rare presentation of complicated sinusitis with pre-septal cellulitis and hard palatal necrosis in a COVID-19 patient, was reported. A 52-year-old male was admitted to the hospital with typical COVID manifestations where he had two successive COVID-19 positive swabs. During his admission, he developed symptoms of right orbital complications of sinusitis along with both clinical and radiological evidence of ipsilateral hard palatal necrosis. Imaging confirmed a diagnosis of right pan-sinusitis complicated with right pre-septal infection and hard palatal bony defect on the same side. Intra-operatively, the sphenopalatine artery was found to be thrombosed. This case focuses on the possible association between these manifestations and the known thromboembolic complications of COVID-19. Ongoing management of such complicated rare cases should be through a multidisciplinary team.

Keywords: COVID-19, sinusitis, sphenopalatine artery, thrombosis

Procedia PDF Downloads 151
874 A Numerical Computational Method of MRI Static Magnetic Field for an Ergonomic Facility Design Guidelines

Authors: Sherine Farrag

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Magnetic resonance imaging (MRI) presents safety hazards, with the general physical environment. The principal hazard of the MRI is the presence of static magnetic fields. Proper architectural design of MRI’s room ensure environment and health care staff safety. This research paper presents an easy approach for numerical computation of fringe static magnetic fields. Iso-gauss line of different MR intensities (0.3, 0.5, 1, 1.5 Tesla) was mapped and a polynomial function of the 7th degree was generated and tested. Matlab script was successfully applied for MRI SMF mapping. This method can be valid for any kind of commercial scanner because it requires only the knowledge of the MR scanner room map with iso-gauss lines. Results help to develop guidelines to guide healthcare architects to design of a safer Magnetic resonance imaging suite.

Keywords: designing MRI suite, MRI safety, radiology occupational exposure, static magnetic fields

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873 A Feasibility Study of Waste (d) Potential: Synergistic Effect Evaluation by Co-digesting Organic Wastes and Kinetics of Biogas Production

Authors: Kunwar Paritosh, Sanjay Mathur, Monika Yadav, Paras Gandhi, Subodh Kumar, Nidhi Pareek, Vivekanand Vivekanand

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A significant fraction of energy is wasted every year managing the biodegradable organic waste inadequately as development and sustainability are the inherent enemies. The management of these waste is indispensable to boost its optimum utilization by converting it to renewable energy resource (here biogas) through anaerobic digestion and to mitigate greenhouse gas emission. Food and yard wastes may prove to be appropriate and potential feedstocks for anaerobic co-digestion for biogas production. The present study has been performed to explore the synergistic effect of co-digesting food waste and yard trimmings from MNIT campus for enhanced biogas production in different ratios in batch tests (37±10C, 90 rpm, 45 days). The results were overwhelming and showed that blending two different organic waste in proper ratio improved the biogas generation considerably, with the highest biogas yield (2044±24 mLg-1VS) that was achieved at 75:25 of food waste to yard waste ratio on volatile solids (VS) basis. The yield was 1.7 and 2.2 folds higher than the mono-digestion of food or yard waste (1172±34, 1016±36mLg-1VS) respectively. The increase in biogas production may be credited to optimum C/N ratio resulting in higher yield. Also Adding TiO2 nanoparticles showed virtually no effect on biogas production as sometimes nanoparticles enhance biogas production. ICP-MS, FTIR analysis was carried out to gain an insight of feedstocks. Modified Gompertz and logistics models were applied for the kinetic study of biogas production where modified Gompertz model showed goodness-of-fit (R2=0.9978) with the experimental results.

Keywords: anaerobic co-digestion, biogas, kinetics, nanoparticle, organic waste

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872 Migration, Accessing Health Services and Mental Health Outcomes: Evidence From Microdata Analysis

Authors: Suzan Odabasi

Abstract:

Suicide attempts and mental health problems among immigrants have been increasing and have become important public health concerns during the last century. Immigrants may face more difficulties in society because of social conflict, language barriers, inadequate social support, socioeconomic problems, and delay in accessing help. The limited number of research has shown that: first-generation migrants may be at higher risk of mental disorders and a higher prevalence of suicide attempts. The main aim of the proposed work is to identify to what degree each of these pressures is causing higher suicides currently observed. In addition, a comparison will be conducted between females and males and also rural and urban areas for which recent data are available. Specifically, this study investigates how accessing mental health services, the uninsured population rate, socioeconomic factors, and being an immigrant affect Turkish immigrants’ mental health and suicide attempts.

Keywords: access to healthcare, immigration, health economics, mental health economics

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871 A Heart Arrhythmia Prediction Using Machine Learning’s Classification Approach and the Concept of Data Mining

Authors: Roshani S. Golhar, Neerajkumar S. Sathawane, Snehal Dongre

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Background and objectives: As the, cardiovascular illnesses increasing and becoming cause of mortality worldwide, killing around lot of people each year. Arrhythmia is a type of cardiac illness characterized by a change in the linearity of the heartbeat. The goal of this study is to develop novel deep learning algorithms for successfully interpreting arrhythmia using a single second segment. Because the ECG signal indicates unique electrical heart activity across time, considerable changes between time intervals are detected. Such variances, as well as the limited number of learning data available for each arrhythmia, make standard learning methods difficult, and so impede its exaggeration. Conclusions: The proposed method was able to outperform several state-of-the-art methods. Also proposed technique is an effective and convenient approach to deep learning for heartbeat interpretation, that could be probably used in real-time healthcare monitoring systems

Keywords: electrocardiogram, ECG classification, neural networks, convolutional neural networks, portable document format

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870 Correlation between Early Government Interventions in the Northeastern United States and COVID-19 Outcomes

Authors: Joel Mintz, Kyle Huntley, Waseem Wahood, Samuel Raine, Farzanna Haffizulla

Abstract:

The effect of different state government interventions on COVID-19 health outcomes is currently unknown. Stay at home (SAH) orders, all non-essential business closures and school closures in the Northeastern US were examined. A linear correlation between the peak number of new daily COVID-19 positive tests, hospitalizations and deaths per capita and the elapsed time between government issued guidance and a fixed number of COVID-19 deaths in each state was performed. Earlier government interventions were correlated with lower peak healthcare burden. Statewide closures of schools and non-essential businesses showed significantly greater (p<.001) correlation to peak COVID-19 disease burden as compared to a statewide SAH. The implications of these findings require further study to determine the effectiveness of these interventions.

Keywords: Coronavirus, epidemiology, government intervention, public health, social distancing

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869 Integrated Care on Chronic Diseases in Asia-Pacific Countries

Authors: Chang Liu, Hanwen Zhang, Vikash Sharma, Don Eliseo Lucerno-Prisno III, Emmanuel Yujuico, Maulik Chokshi, Prashanthi Krishnakumar, Bach Xuan Tran, Giang Thu Vu, Kamilla Anna Pinter, Shenglan Tang

Abstract:

Background and Aims: Globally, many health systems focus on hospital-based healthcare models targeting acute care and disease treatment, which are not effective in addressing the challenges of ageing populations, chronic conditions, multi-morbidities, and increasingly unhealthy lifestyles. Recently, integrated care programs on chronic diseases have been developed, piloted, and implemented to meet such challenges. However, integrated care programs in the Asia-Pacific region vary in the levels of integration from linkage to coordination to full integration. This study aims to identify and analyze existing cases of integrated care in the Asia-Pacific region and identify the facilitators and barriers in order to improve existing cases and inform future cases. Methods: The study is a comparative study, with a combination approach of desk-based research and key informant interviews. The selected countries included in this study represent a good mix of lower-middle income countries (the Philippines, India, Vietnam, and Fiji), upper-middle income country (China), and high-income country (Singapore) in the Asia-Pacific region. Existing integrated care programs were identified through the scoping review approach. Trigger, history, general design, beneficiaries, and objectors were summarized with barriers and facilitators of integrated care based on key informant interviews. Representative case(s) in each country were selected and comprehensively analyzed through deep-dive case studies. Results: A total of 87 existing integrated care programs on chronic diseases were found in all countries, with 44 in China, 21 in Singapore, 12 in India, 5 in Vietnam, 4 in the Philippines, and 1 in Fiji. 9 representative cases of integrated care were selected for in-depth description and analysis, with 2 in China, the Philippines, and Vietnam, and 1 in Singapore, India, and Fiji. Population aging and the rising chronic disease burden have been identified as key drivers for almost all the six countries. Among the six countries, Singapore has the longest history of integrated care, followed by Fiji, the Philippines, and China, while India and Vietnam have a shorter history of integrated care. Incentives, technologies, education, and performance evaluation would be crucial for developing strategies for implementing future programs and improve already existing programs. Conclusion: Integrated care is important for addressing challenges surrounding the delivery of long-term care. To date, there is an increasing trend of integrated care programs on chronic diseases in the Asia-Pacific region, and all six countries in our study set integrated care as a direction for their health systems transformation.

Keywords: integrated healthcare, integrated care delivery, chronic diseases, Asia-Pacific region

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868 Poor Medical Waste Management (MWM) Practices and Its Risks to Human Health and the Environment

Authors: Babanyara Y. Y., Ibrahim D. B., Garba T., Bogoro A. G., Abubakar, M. Y.

Abstract:

Medical care is vital for our life, health, and well-being. However, the waste generated from medical activities can be hazardous, toxic, and even lethal because of their high potential for diseases transmission. The hazardous and toxic parts of waste from healthcare establishments comprising infectious, medical, and radioactive material as well as sharps constitute a grave risks to mankind and the environment, if these are not properly treated/disposed or are allowed to be mixed with other municipal waste. In Nigeria, practical information on this aspect is inadequate and research on the public health implications of poor management of medical wastes is few and limited in scope. Findings drawn from Literature particularly in the third world countries highlights financial problems, lack of awareness of risks involved in MWM, lack of appropriate legislation and lack of specialized MWM staff. The paper recommends how MWM practices can be improved in medical facilities.

Keywords: environmental pollution, infectious, management, medical waste, public health

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867 Response of Post-harvest Treatments on Shelf Life, Biochemical and Microbial Quality of Banana Variety Red Banana

Authors: Karishma Sebastian, Pavethra A., Manjula B. S., K. N. Satheeshan, Jenita Thinakaran

Abstract:

Red Banana is a popular variety of banana with strong market demand. Its ripe fruits are less resistant to transportation, complicating logistics. Moreover, as it is a climacteric fruit, its post-harvest shelf life is limited. The current study aimed to increase the postharvest shelf life of Red Banana fruits by adopting different postharvest treatments. Fruit bunches of Red Banana were harvested at the mature green stage, separated into hands, precooled, subjected to 12 treatments, and stored in Corrugated Fibre Board boxes till the end of shelf life under ambient conditions. Fruits coated with 10% bee wax + 0.5% clove oil (T₄), fruits subjected to coating with 10% bee wax and packaging with potassium permanganate (T₉), and fruits dipped in hot water at 50°C for 10 minutes and packaging with potassium permanganate (T₁₁) registered the highest shelf life of 18.67 days. The highest TSS of 26.33°Brix was noticed in fruits stored with potassium permanganate (T₈) after 12.67 days of storage, and lowest titratable acidity of 0.19%, and the highest sugar-acid ratio of 79.76 was noticed in control (T₁₂) after 11.33 days of storage. Moreover, the highest vitamin C content (7.74 mg 100 g⁻¹), total sugar content (18.47%), reducing sugar content (15.49%), total carotenoid content (24.13 µg 100 g-¹) was noticed in treatments T₇ (hot water dipping at 50 °C for 10 minutes) after 17.67 days, T₁₀ (coating with 40% aloe vera extract and packaged with potassium permanganate) after 13.33 days, T₄ (coating with 10% bee wax + 0.5% clove oil) after 18.67 days and T₉ (coating with 10% bee wax + potassium permanganate) after 18.67 days of storage respectively. Furthermore, the lowest fungal and bacterial counts were observed in treatments T₂ (dipping in 30ppm sodium hypochlorite solution), T₇ (hot water dipping at 50 °C for 10 minutes), T₉ (coating with 10% bee wax + potassium permanganate), and T₁₀ (coating with 40% aloe vera extract + potassium permanganate).

Keywords: bee wax, post-harvest treatments, potassium permanganate, Red Banana, shelf life

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