Search results for: crisis management process

Authors: Tianqiao Zhang, Ye Tian, Yanliang Yin, Yichao Tian, Suzhai Tian, Weige Sun, Shuhui Gong, Limei Tang, Ruoliang Tang

Abstract:

Introduction: Healthcare workers, especially the nurses all over the world, are highly vulnerable to work-related musculoskeletal disorders (WMSDs), experiencing high rates of neck, shoulder, and low back injuries, due to the unfavorable working conditions. To reduce WMSDs among nursing personnel, many workplace interventions have been developed and implemented. Unfortunately, the ongoing Covid-19 (SARS-CoV-2) pandemic has posed great challenges to the ergonomic practices and interventions in healthcare facilities, particularly the hospitals, since current Covid-19 mitigation measures, such as social distancing and working remotely, has substantially minimized in-person gatherings and trainings. On the other hand, hospitals throughout the world have been short-staffed, resulting in disturbance of shift scheduling and more importantly, the increased job demand among the available caregivers, particularly the doctors and nurses. With the latest development in communication technology, remote intervention measures have been developed as an alternative, without the necessity of in-person meetings. The Omaha System (OS) is a standardized classification system for nursing practices, including a problem classification system, an intervention system, and an outcome evaluation system. This paper describes the development of an OS-based ergonomic intervention program. Methods: First, a comprehensive literature search was performed among worldwide electronic databases, including PubMed, Web of Science, Cochrane Library, China National Knowledge Infrastructure (CNKI), between journal inception to May 2020, resulting in a total of 1,418 scientific articles. After two independent screening processes, the final knowledge pool included eleven randomized controlled trial studies to develop the draft of the intervention program with Omaha intervention subsystem as the framework. After the determination of sample size needed for statistical power and the potential loss to follow-up, a total of 94 nurses from eight clinical departments agreed to provide written, informed consent to participate in the study, which were subsequently assigned into two random groups (i.e., intervention vs. control). A subgroup of twelve nurses were randomly selected to participate in a semi-structured interview, during which their general understanding and awareness of musculoskeletal disorders and potential interventions was assessed. Then, the first draft was modified to reflect the findings from these interviews. Meanwhile, the tentative program schedule was also assessed. Next, two rounds of consultation were conducted among experts in nursing management, occupational health, psychology, and rehabilitation, to further adjust and finalize the intervention program. The control group had access to all the information and exercise modules at baseline, while an interdisciplinary research team was formed and supervised the implementation of the on-line intervention program through multiple social media groups. Outcome measures of this comparative study included biomechanical load assessed by the Quick Exposure Check and stresses due to awkward body postures. Results and Discussion: Modification to the draft included (1) supplementing traditional Chinese medicine practices, (2) adding the use of assistive patient handling equipment, and (3) revising the on-line training method. Information module should be once a week, lasting about 20 to 30 minutes, for a total of 6 weeks, while the exercise module should be 5 times a week, each lasting about 15 to 20 minutes, for a total of 6 weeks.

Keywords: ergonomic interventions, musculoskeletal disorders (MSDs), omaha system, nurses, Covid-19

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Authors: Mohamed Sameer Mughal, Andrew D. Ross, Damian J. Fearon

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Background: The fast-paced changing landscape of UK Higher Education Institution (HEIs) is poised by changes and challenges affecting Middle Managers (MM) in their job roles. MM contribute to the success of HEIs by balancing the equilibrium and pass organization strategies from senior staff towards operationalization directives to junior staff. However, this study showcased from the data analyzed during the semi structured interviews; MM job role is becoming more complex due to changes and challenges creating colossal pressures and workloads in day-to-day working. Current development programmes provisions by Human Resources (HR) departments in such HEIs are not feasible, applicable, and matching the true essence and requirements of MM who suggest that programmes offered by HR are too generic to suit their precise needs and require tailor made espousal to work effectively in their pertinent job roles. Methodologies: This study aims to capture demands of MM Development Needs (DN) by means of a conceptual model as conclusive part of the research that is divided into 2 phases. Phase 1 initiated by carrying out 2 pilot interviews with a retired Emeritus status professor and HR programmes development coordinator. Key themes from the pilot and literature review subsidized into formulation of 22 set of questions (Kvale and Brinkmann) in form of interviewing questionnaire during qualitative data collection. Data strategy and collection consisted of purposeful sampling of 12 semi structured interviews (n=12) lasting approximately an hour for all participants. The MM interviewed were at faculty and departmental levels which included; deans (n=2), head of departments (n=4), subject leaders (n=2), and lastly programme leaders (n=4). Participants recruitment was carried out via emails and snowballing technique. The interviews data was transcribed (verbatim) and managed using Computer Assisted Qualitative Data Analysis using Nvivo ver.11 software. Data was meticulously analyzed using Miles and Huberman inductive approach of positivistic style grounded theory, whereby key themes and categories emerged from the rich data collected. The data was precisely coded and classified into case studies (Robert Yin); with a main case study, sub cases (4 classes of MM) and embedded cases (12 individual MMs). Major Findings: An interim conceptual model emerged from analyzing the data with main concepts that included; key performance indicators (KPI’s), HEI effectiveness and outlook, practices, processes and procedures, support mechanisms, student events, rules, regulations and policies, career progression, reporting/accountability, changes and challenges, and lastly skills and attributes. Conclusion: Dynamic elements affecting MM includes; increase in government pressures, student numbers, irrelevant development programmes, bureaucratic structures, transparency and accountability, organization policies, skills sets… can only be confronted by employing structured development programmes originated by HR that are not provided generically. Future Work: Stage 2 (Quantitative method) of the study plans to validate the interim conceptual model externally through fully completed online survey questionnaire (Bram Oppenheim) from external HEIs (n=150). The total sample targeted is 1500 MM. Author contribution focuses on enhancing management theory and narrow the gap between by HR and MM development programme provision.

Keywords: development needs (DN), higher education institutions (HEIs), human resources (HR), middle managers (MM)

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Authors: Medha Iyer, Anshul Paul, Aunnesha Bhowmick, Anahita Banerjee, Sana Prasad, Anoushka Singal, Lauren Sinopoli, Pooja Bapat, Shivi Jain

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In Himachal Pradesh, India, a majority of the population lives in rural villages spread throughout its hilly regions; many of these households rely on subsistence farming as their main source of livelihood. The student-run non-profit organization affiliated with this study, Project RISHI (Rural India Social and Health Improvement), works to promote sustainable development practices in Bharog Baneri, a gram panchayat, or union, of villages in Himachal Pradesh. In 2017, an established rainwater harvesting (RWH) project group within Project RISHI had surveyed many families, finding that the most common issue regarding food and water access was a lack of accessible water sources for agricultural use in the dry season. After a prototype build in 2018, the group built 6 systems for eligible residents that demonstrated need in 2019. Subsequently, the project went through an evaluation period, including self-evaluation of project goals and post-impact surveying of system recipients. The group used the social impact assessment model to optimize the implementation of domestic RWH systems in Bharog Baneri. Assessing implementation after in-person builds produced three pillars of focus — system design, equitable recipient selection, and community involvement. After two years of remote involvement during COVID-19, the group prepared to visit Bharog Baneri to build 10 new systems in the Summer 2022. First, the group created a more durable and cost-effective design that could withstand debris and heavy rains to prevent gutter failure. The domestic system design is a rooftop RWH catchment system with two tanks attached, an overflow pipe, debris filtration, and a spigot for accessibility. The group also developed a needs-based eligibility methodology with assistance from village leaders and surveying in Bharog Baneri and set up the groundwork for a future community board. COVID-19 has strengthened remote work, telecommunications, and other organizational support systems. As sustainable development evolves to encompass these practices in a post-pandemic world, the potential for new RWH system design and implementation processes has emerged as well. This raises the question: how can a social impact assessment of rural RWH projects inform an integrated approach to post-pandemic RWH system practices? The objective of this exploratory study is to investigate and evaluate a novel remote build infrastructure that brings access to reliable and sustainable sources of water for agricultural use. To construct the remote build approach, the group identified and assigned a point of contact who was experienced with previous RWH system builds. The recipients were selected based on demonstrated need and ease of building. The contact visited each of the houses and coordinated supplier relations and transportation of the materials in accordance with the participatory approach to sustainable development. Over the course of two months, the group completed four system builds with the resulting infrastructure. The infrastructure adhered to the social impact assessment model by centering supplier relations, material transportation, and construction logistics within the community. The conclusion of this exploration is that post-pandemic rural RWH practices should be rooted in strengthening villager communication and utilizing local assets. Through this, non-profit organizations can incorporate remote build strategies into their long-term goals.

Keywords: capturing run-off from rooftops, domestic rainwater harvesting, Implementation approaches and strategies, rainwater harvesting and management in rural sectors

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Authors: Oluwaseun Adeleke, Samuel O. Ikani, Simeon Christian Chukwu, Fidelis Edet, Anthony Nwala, Mopelola Raji, Simeon Christian Chukwu

Abstract:

Background: Health service providers are offered in-service training periodically to strengthen their ability to deliver services that are ethical, quality, timely and safe. Not all capacity-building courses have successfully resulted in intended service delivery outcomes because of poor training content, design, approach, and ambiance. The Delivering Innovations in Selfcare (DISC) project developed a Moment of Truth innovation, which is a proven training model focused on improving consumer/provider interaction that leads to an increase in the voluntary uptake of subcutaneous depot medroxyprogesterone acetate (DMPA-SC) self-injection among women who opt for injectable contraception. Methodology: Six months after training on a moment of truth (MoT) training manual, the project conducted two intensive rounds of qualitative data collection and triangulation that included provider, client, and community mobilizer interviews, facility observations, and routine program data collection. Respondents were sampled according to a convenience sampling approach, and data collected was analyzed using a codebook and Atlas-TI. Providers and clients were interviewed to understand their experience, perspective, attitude, and awareness about the DMPA-SC self-inject. Data were collected from 12 health facilities in three states – eight directly trained and four cascades trained. The research team members came together for a participatory analysis workshop to explore and interpret emergent themes. Findings: Quality-of-service delivery and performance outcomes were observed to be significantly better in facilities whose providers were trained directly trained by the DISC project than in sites that received indirect training through master trainers. Facilities that were directly trained recorded SI proportions that were twice more than in cascade-trained sites. Direct training comprised of full-day and standalone didactic and interactive sessions constructed to evoke commitment, passion and conviction as well as eliminate provider bias and misconceptions in providers by utilizing human interest stories and values clarification exercises. Sessions also created compelling arguments using evidence and national guidelines. The training also prioritized demonstration sessions, utilized job aids, particularly videos, strengthened empathetic counseling – allaying client fears and concerns about SI, trained on positioning self-inject first and side effects management. Role plays and practicum was particularly useful to enable providers to retain and internalize new knowledge. These sessions provided experiential learning and the opportunity to apply one's expertise in a supervised environment where supportive feedback is provided in real-time. Cascade Training was often a shorter and abridged form of MoT training that leveraged existing training already planned by master trainers. This training was held over a four-hour period and was less emotive, focusing more on foundational DMPA-SC knowledge such as a reorientation to DMPA-SC, comparison of DMPA-SC variants, counseling framework and skills, data reporting and commodity tracking/requisition – no facility practicums. Training on self-injection was not as robust, presumably because they were not directed at methods in the contraceptive mix that align with state/organizational sponsored objectives – in this instance, fostering LARC services. Conclusion: To achieve better performance outcomes, consideration should be given to providing training that prioritizes practice-based and emotive content. Furthermore, a firm understanding and conviction about the value training offers improve motivation and commitment to accomplish and surpass service-related performance outcomes.

Keywords: training, performance outcomes, innovation, family planning, contraception, DMPA-SC, self-care, self-injection.

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Authors: Guilherme Giacomini, Ana Luiza Menegatti Pavan, Allan Felipe Fattori Alves, Marcela de Oliveira, Fernando Antonio Bacchim Neto, José Ricardo de Arruda Miranda, Seizo Yamashita, Diana Rodrigues de Pina

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The maxillary sinus (MS), part of the paranasal sinus complex, is one of the most enigmatic structures in modern humans. The literature has suggested that MSs function as olfaction accessories, to heat or humidify inspired air, for thermoregulation, to impart resonance to the voice and others. Thus, the real function of the MS is still uncertain. Furthermore, the MS anatomy is complex and varies from person to person. Many diseases may affect the development process of sinuses. The incidence of rhinosinusitis and other pathoses in the MS is comparatively high, so, volume analysis has clinical value. Providing volume values for MS could be helpful in evaluating the presence of any abnormality and could be used for treatment planning and evaluation of the outcome. The computed tomography (CT) has allowed a more exact assessment of this structure, which enables a quantitative analysis. However, this is not always possible in the clinical routine, and if possible, it involves much effort and/or time. Therefore, it is necessary to have a convenient, robust, and practical tool correlated with the MS volume, allowing clinical applicability. Nowadays, the available methods for MS segmentation are manual or semi-automatic. Additionally, manual methods present inter and intraindividual variability. Thus, the aim of this study was to develop an automatic tool to quantity the MS volume in CT scans of paranasal sinuses. This study was developed with ethical approval from the authors’ institutions and national review panels. The research involved 30 retrospective exams of University Hospital, Botucatu Medical School, São Paulo State University, Brazil. The tool for automatic MS quantification, developed in Matlab®, uses a hybrid method, combining different image processing techniques. For MS detection, the algorithm uses a Support Vector Machine (SVM), by features such as pixel value, spatial distribution, shape and others. The detected pixels are used as seed point for a region growing (RG) segmentation. Then, morphological operators are applied to reduce false-positive pixels, improving the segmentation accuracy. These steps are applied in all slices of CT exam, obtaining the MS volume. To evaluate the accuracy of the developed tool, the automatic method was compared with manual segmentation realized by an experienced radiologist. For comparison, we used Bland-Altman statistics, linear regression, and Jaccard similarity coefficient. From the statistical analyses for the comparison between both methods, the linear regression showed a strong association and low dispersion between variables. The Bland–Altman analyses showed no significant differences between the analyzed methods. The Jaccard similarity coefficient was > 0.90 in all exams. In conclusion, the developed tool to quantify MS volume proved to be robust, fast, and efficient, when compared with manual segmentation. Furthermore, it avoids the intra and inter-observer variations caused by manual and semi-automatic methods. As future work, the tool will be applied in clinical practice. Thus, it may be useful in the diagnosis and treatment determination of MS diseases. Providing volume values for MS could be helpful in evaluating the presence of any abnormality and could be used for treatment planning and evaluation of the outcome. The computed tomography (CT) has allowed a more exact assessment of this structure which enables a quantitative analysis. However, this is not always possible in the clinical routine, and if possible, it involves much effort and/or time. Therefore, it is necessary to have a convenient, robust and practical tool correlated with the MS volume, allowing clinical applicability. Nowadays, the available methods for MS segmentation are manual or semi-automatic. Additionally, manual methods present inter and intraindividual variability. Thus, the aim of this study was to develop an automatic tool to quantity the MS volume in CT scans of paranasal sinuses. This study was developed with ethical approval from the authors’ institutions and national review panels. The research involved 30 retrospective exams of University Hospital, Botucatu Medical School, São Paulo State University, Brazil. The tool for automatic MS quantification, developed in Matlab®, uses a hybrid method, combining different image processing techniques. For MS detection, the algorithm uses a Support Vector Machine (SVM), by features such as pixel value, spatial distribution, shape and others. The detected pixels are used as seed point for a region growing (RG) segmentation. Then, morphological operators are applied to reduce false-positive pixels, improving the segmentation accuracy. These steps are applied in all slices of CT exam, obtaining the MS volume. To evaluate the accuracy of the developed tool, the automatic method was compared with manual segmentation realized by an experienced radiologist. For comparison, we used Bland-Altman statistics, linear regression and Jaccard similarity coefficient. From the statistical analyses for the comparison between both methods, the linear regression showed a strong association and low dispersion between variables. The Bland–Altman analyses showed no significant differences between the analyzed methods. The Jaccard similarity coefficient was > 0.90 in all exams. In conclusion, the developed tool to automatically quantify MS volume proved to be robust, fast and efficient, when compared with manual segmentation. Furthermore, it avoids the intra and inter-observer variations caused by manual and semi-automatic methods. As future work, the tool will be applied in clinical practice. Thus, it may be useful in the diagnosis and treatment determination of MS diseases.

Keywords: maxillary sinus, support vector machine, region growing, volume quantification

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Authors: Ali Kazemi

Abstract:

Those within the volatile and interconnected international economic markets, appropriately predicting market trends, hold substantial fees for traders and financial establishments. Traditional device mastering strategies have made full-size strides in forecasting marketplace movements; however, monetary data's complicated and networked nature calls for extra sophisticated processes. This observation offers a groundbreaking method for monetary marketplace prediction that leverages the synergistic capability of Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks. Our suggested algorithm is meticulously designed to forecast the traits of inventory market indices and cryptocurrency costs, utilizing a comprehensive dataset spanning from January 1, 2015, to December 31, 2023. This era, marked by sizable volatility and transformation in financial markets, affords a solid basis for schooling and checking out our predictive version. Our algorithm integrates diverse facts to construct a dynamic economic graph that correctly reflects market intricacies. We meticulously collect opening, closing, and high and low costs daily for key inventory marketplace indices (e.g., S&P 500, NASDAQ) and widespread cryptocurrencies (e.g., Bitcoin, Ethereum), ensuring a holistic view of marketplace traits. Daily trading volumes are also incorporated to seize marketplace pastime and liquidity, providing critical insights into the market's shopping for and selling dynamics. Furthermore, recognizing the profound influence of the monetary surroundings on financial markets, we integrate critical macroeconomic signs with hobby fees, inflation rates, GDP increase, and unemployment costs into our model. Our GCN algorithm is adept at learning the relational patterns amongst specific financial devices represented as nodes in a comprehensive market graph. Edges in this graph encapsulate the relationships based totally on co-movement styles and sentiment correlations, enabling our version to grasp the complicated community of influences governing marketplace moves. Complementing this, our LSTM algorithm is trained on sequences of the spatial-temporal illustration discovered through the GCN, enriched with historic fee and extent records. This lets the LSTM seize and expect temporal marketplace developments accurately. Inside the complete assessment of our GCN-LSTM algorithm across the inventory marketplace and cryptocurrency datasets, the version confirmed advanced predictive accuracy and profitability compared to conventional and opportunity machine learning to know benchmarks. Specifically, the model performed a Mean Absolute Error (MAE) of 0.85%, indicating high precision in predicting day-by-day charge movements. The RMSE was recorded at 1.2%, underscoring the model's effectiveness in minimizing tremendous prediction mistakes, which is vital in volatile markets. Furthermore, when assessing the model's predictive performance on directional market movements, it achieved an accuracy rate of 78%, significantly outperforming the benchmark models, averaging an accuracy of 65%. This high degree of accuracy is instrumental for techniques that predict the course of price moves. This study showcases the efficacy of mixing graph-based totally and sequential deep learning knowledge in economic marketplace prediction and highlights the fee of a comprehensive, records-pushed evaluation framework. Our findings promise to revolutionize investment techniques and hazard management practices, offering investors and economic analysts a powerful device to navigate the complexities of cutting-edge economic markets.

Keywords: financial market prediction, graph convolutional networks (GCNs), long short-term memory (LSTM), cryptocurrency forecasting

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Authors: Anisa Suraya Ab Razak, Izza Hayat

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Introduction: The diagnosis and management of severe hyponatremia in neuropsychiatric patients present a significant challenge to physicians. Several factors contribute, including diagnostic shadowing and attributing abnormal behavior to intellectual disability or psychiatric conditions. Hyponatraemia is the commonest electrolyte abnormality in the inpatient population, ranging from mild/asymptomatic, moderate to severe levels with life-threatening symptoms such as seizures, coma and death. There are several documented fatal case reports in the literature of severe hyponatremia secondary to psychogenic polydipsia, often diagnosed only in autopsy. This paper presents a case study of acute severe hyponatremia in a neuropsychiatric patient with early diagnosis and admission to intensive care. Case study: A 21-year old Caucasian male with known epilepsy and learning disability was admitted from residential living with generalized tonic-clonic self-terminating seizures after refusing medications for several weeks. Evidence of superficial head injury was detected on physical examination. His laboratory data demonstrated mild hyponatremia (125 mmol/L). Computed tomography imaging of his brain demonstrated no acute bleed or space-occupying lesion. He exhibited abnormal behavior - restlessness, drinking water from bathroom taps, inability to engage, paranoia, and hypersexuality. No collateral history was available to establish his baseline behavior. He was loaded with intravenous sodium valproate and leveritircaetam. Three hours later, he developed vomiting and a generalized tonic-clonic seizure lasting forty seconds. He remained drowsy for several hours and regained minimal recovery of consciousness. A repeat set of blood tests demonstrated profound hyponatremia (117 mmol/L). Outcomes: He was referred to intensive care for peripheral intravenous infusion of 2.7% sodium chloride solution with two-hourly laboratory monitoring of sodium concentration. Laboratory monitoring identified dangerously rapid correction of serum sodium concentration, and hypertonic saline was switched to a 5% dextrose solution to reduce the risk of acute large-volume fluid shifts from the cerebral intracellular compartment to the extracellular compartment. He underwent urethral catheterization and produced 8 liters of urine over 24 hours. Serum sodium concentration remained stable after 24 hours of correction fluids. His GCS recovered to baseline after 48 hours with improvement in behavior -he engaged with healthcare professionals, understood the importance of taking medications, admitted to illicit drug use and drinking massive amounts of water. He was transferred from high-dependency care to ward level and was initiated on multiple trials of anti-epileptics before achieving seizure-free days two weeks after resolution of acute hyponatremia. Conclusion: Psychogenic polydipsia is often found in young patients with intellectual disability or psychiatric disorders. Patients drink large volumes of water daily ranging from ten to forty liters, resulting in acute severe hyponatremia with mortality rates as high as 20%. Poor outcomes are due to challenges faced by physicians in making an early diagnosis and treating acute hyponatremia safely. A low index of suspicion of water intoxication is required in this population, including patients with known epilepsy. Monitoring urine output proved to be clinically effective in aiding diagnosis. Early referral and admission to intensive care should be considered for safe correction of sodium concentration while minimizing risk of fatal complications e.g. central pontine myelinolysis.

Keywords: epilepsy, psychogenic polydipsia, seizure, severe hyponatremia

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Authors: Mariza Kaskara, Stella Girtsou, Maria Prodromou, Alexia Tsouni, Christodoulos Mettas, Stavroula Alatza, Kyriaki Fotiou, Marios Tzouvaras, Charalampos Kontoes, Diofantos Hadjimitsis

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Natural hazard assessment and monitoring are crucial components in managing the risks associated with fires, floods, and geohazards, particularly in regions prone to these natural disasters, such as Greece and Cyprus. Recent advancements in technology led to the development of state-of-the-art systems for assessing and monitoring these hazards. These technologies, developed by the BEYOND Center of Excellence of the National Observatory of Athens, have been successfully applied in Greece and are now set to be transferred to Cyprus. The implementation of these advanced technologies in Greece has significantly improved the country's ability to respond to these natural hazards. Enhancing disaster resilience is crucial as it significantly improves our ability to predict, prepare for, and mitigate the impacts of natural disasters, ultimately saving lives and reducing economic losses. For wildfire risk assessment, a scalar wildfire occurrence risk index has been created based on the predictions of machine learning models. Our objective was to train an ML model that learns to derive a fire susceptibility score when given as input a vector of features assigned to certain spatiotemporal coordinates. Predicting fire danger is crucial for the sustainable management of forest fires as it provides essential information for designing effective prevention measures and facilitating response planning for potential fire incidents. For flood risk assessment, a multi-faceted approach has been employed, including the application of remote sensing techniques, the collection and processing of data from population, buildings, technical studies and field visits, as well as hydrological and hydraulic simulations. All input data are used to create precise flood hazard maps according to various flooding scenarios, detailed flood vulnerability and flood exposure maps, which finally produce the flood risk map. Critical points are identified, and mitigation measures are proposed for the worst-case scenario, namely, refuge areas are defined, and escape routes are designed. Flood risk maps can assist in raising awareness and save lives. For geohazards monitoring (e.g., landslides, subsidence), synthetic aperture radar (SAR) and optical satellite imagery have been combined with geomorphological and meteorological data and other landslide/ground deformation contributing factors. To monitor critical infrastructures, including dams, advanced InSAR (Interferometric SAR) methodologies are used for identifying surface movements through time. Monitoring these hazards provides valuable information for understanding processes and could lead to early warning systems to protect people and infrastructure. The success of these systems in Greece has paved the way for their transfer to Cyprus to enhance Cyprus's capabilities in natural hazard assessment and monitoring. This transfer is being made through knowledge transfer activities, fostering continuous collaboration between Greek and Cypriot experts. Furthermore, small demonstration actions are implemented to showcase the effectiveness of these technologies in real-world scenarios. In conclusion, the transfer of advanced natural hazard assessment technologies from Greece to Cyprus represents a significant step forward in enhancing the entire region's resilience to disasters. The EXCELSIOR project, funding this opportunity, is committed to empowering Cyprus with the tools and expertise needed to effectively manage and mitigate the risks associated with these natural hazards. Acknowledgment: Authors acknowledge the 'EXCELSIOR': ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project.

Keywords: earth observation, monitoring, natural hazards, remote sensing

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Authors: David J. Foran, Nhan Do, Samuel Ajjarapu, Wenjin Chen, Tahsin Kurc, Joel H. Saltz

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The large-scale data and computational requirements of investigators throughout the clinical and research communities demand an informatics infrastructure that supports both existing and new investigative and translational projects in a robust, secure environment. In some subspecialties of medicine and research, the capacity to generate data has outpaced the methods and technology used to aggregate, organize, access, and reliably retrieve this information. Leading health care centers now recognize the utility of establishing an enterprise-wide, clinical data warehouse. The primary benefits that can be realized through such efforts include cost savings, efficient tracking of outcomes, advanced clinical decision support, improved prognostic accuracy, and more reliable clinical trials matching. The overarching objective of the work presented here is the development and implementation of a flexible Intelligent Retrieval and Interrogation System (IRIS) that exploits the combined use of computational imaging, genomics, and data-mining capabilities to facilitate clinical assessments and translational research in oncology. The proposed System includes a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide insight into the underlying tumor characteristics that are not be apparent by human inspection alone. A key distinguishing feature of the System is a configurable Extract, Transform and Load (ETL) interface that enables it to adapt to different clinical and research data environments. This project is motivated by the growing emphasis on establishing Learning Health Systems in which cyclical hypothesis generation and evidence evaluation become integral to improving the quality of patient care. To facilitate iterative prototyping and optimization of the algorithms and workflows for the System, the team has already implemented a fully functional Warehouse that can reliably aggregate information originating from multiple data sources including EHR’s, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology PAC systems, Digital Pathology archives, Unstructured Clinical Documents, and Next Generation Sequencing services. The System enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information about patient tumors individually or as part of large cohorts to identify patterns that may influence treatment decisions and outcomes. The CRDW core system has facilitated peer-reviewed publications and funded projects, including an NIH-sponsored collaboration to enhance the cancer registries in Georgia, Kentucky, New Jersey, and New York, with machine-learning based classifications and quantitative pathomics, feature sets. The CRDW has also resulted in a collaboration with the Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) at the U.S. Department of Veterans Affairs to develop algorithms and workflows to automate the analysis of lung adenocarcinoma. Those studies showed that combining computational nuclear signatures with traditional WHO criteria through the use of deep convolutional neural networks (CNNs) led to improved discrimination among tumor growth patterns. The team has also leveraged the Warehouse to support studies to investigate the potential of utilizing a combination of genomic and computational imaging signatures to characterize prostate cancer. The results of those studies show that integrating image biomarkers with genomic pathway scores is more strongly correlated with disease recurrence than using standard clinical markers.

Keywords: clinical data warehouse, decision support, data-mining, intelligent databases, machine-learning.

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