The Smart Data Extractor, a Clinician Friendly Solution to Accelerate and Improve the Data Collection During Clinical Trials

Using a cross-over study design, vital signs were collected from 60 patients by 6 nurses. Each nurse was randomly assigned for manual vital sign collection in 5 patients and for automated data collection in other 5 patients. The mean time taken for vital signs information to be available in EMR was significantly (p <0.004) lower after automated data collection (158.7±67.0) than after the manual collection (4079.8±7091.8 s). The nursing satisfaction score of collecting vital signs was significantly lower (p<0.007) for the manual way (10.3±3.9) than for the automated way (16.5±3.4). We found that 30% of vital sign records were transmitted to EMR with at least one error after manual data collection whereas there wasno transmission error with automated data collection. Allparticipating nurses stated that the automated vital sign collection can improve their efficiency and save their time for direct patient care.

US medical researchers brace themselves for budget cuts

Due to the growing presence of digital technologies in factories, an increasing number of automated processes systematically record various data such as timestamps, product IDs, quantities, and defects. However, this is still not the case for many manual assembly lines, which often rely on manual data acquisition where workers, in addition to the assembly tasks, are asked to record data manually. Such manual data generation can significantly reduce the data quality and increase the variation of the data. In this paper, the authors study this problem by conducting a case study of a manufacturer of engine components based in Switzerland. The paper examines the antecedents of deviations in manually collected assembly line data—both from a technical and behavioral perspective. Based on the findings, a model is developed for the case company that compares manually collected data to the same data in the planning systems to identify and act on data discrepancies. The paper concludes with practical guidelines on how to mitigate data quality loss in manual assembly.

<p indent="0mm">A clinical trial is a key step in the process of pharmaceutical research and development (R &amp; D). It is a key indicator of the innovative potential of the pharmaceutical industry. ClinicalTrials.gov shows that the average annual growth rate of the total number of clinical trials globally in the past decade (2012–2021) was 20.7%, mainly distributed in Europe and the United States, and the total number of clinical trials registered in China accounted for 6% of the world. According to the statistics of IQVIA Institute for Human Data Science, the global proportion of early-stage R &amp; D pipelines from China-headquartered companies increased from 1% in 2005 to 12% in 2020, which was still far behind that of European and American companies. Among 871 new active substances approved for marketing globally in the past two decades, 522 were for marketing in China, with the high number driven by regulatory acceleration mechanisms from National Medical Products Administration, such as breakthrough and orphan designations and priority reviews. Considering the gap in clinical research strength between China and developed countries such as Europe and the United States, the clinical trial research capacity and level should be improved to assist China in the R &amp; D of innovative drugs. According to the Registration and Information Disclosure Platform for Drug Clinical Studies and the clinical trial institution filing management information platform in China, in the last five years (2017–2021), the average annual growth rates of the total number of new drug clinical trial registration and clinical trial units in China reached 26.9% and 12.6%, respectively. However, clinical trial resources are mainly concentrated in major institutions, municipalities, or provincial capital cities in the eastern and central regions of China, with distributions becoming increasingly polarised. Under the background of China’s new medical reforms, the strategic direction of national political support is to motivate equitable access to high-quality clinical trial resources and cross-regional collaborative development of medical institutions by means of medical unions, national clinical medical research centres, Chinese national major projects for new drug innovation and so on. In the context of this background, clinical trial research unions (CTRUs) have been built in China. A CTRU is defined as a consortium formed by medical institutions, sponsors and third-party service institutions of various levels and functions, led by a national clinical medical research centre or clinical trial medical institutions undertaking major national science and technology projects or supporting projects of national key R &amp; D plans, radiating and driving the improvement of clinical trial research capacity in multiple regions. CTRUs develop a multi-level clinical research centre system and collaborative network by vertically and horizontally combining multi-level medical institutions, sponsors and third-party service institutions. All participants of CTRUs are crucial. The leading clinical trial research medical institution, as the core, is responsible for establishing institutional system standards in CTRUs, designing and leading high-quality multi-centre clinical trials, central ethics and personality training. The major clinical trial research medical institutions are responsible for implementing high-quality, multi-centre, complex and high-risk clinical trials. The other member institutions are responsible for implementing basic clinical trials. The sponsors, contract research organizations (CROs), site management organizations (SMOs) and other enterprises are responsible for funding, supporting and promoting the construction of a clinical trial centre system and collaborative network. The specific construction contents of CTRUs include building a clinical trial research resource sharing platform, homogenising clinical trial quality management, constructing a rapid clinical trial process platform, diversified multi-level and multi-form talent training, information interconnection, deepening strategic cooperation and designing and leading high-level trials. CTRUs have established the selection criteria and assessment exit mechanism and conducted work performance assessments from the dimensions of organisation and implementation, division of labour and cooperation, the connection of clinical trial resources from top to bottom, efficiency and benefit and sustainable development to ensure their good and sustainable development. Through CTRUs, we can achieve high-quality clinical trial resource sharing, improve the clinical trial research capability of member institutions, cultivate high-level skills, such as principal investigators (PIs), sub-investigators, institutional managers and clinical research coordinators (CRCs), and promote the development of clinical trials, economically and with high-quality. Through the trial operation with one member of CTRUs, it was found that the key points should be strengthened in deepening resource sharing, implementing central ethics review, interconnecting information platforms and leading high-quality clinical trials. The construction of CTRUs is an effective means for China’s clinical trial research to solve the current problems and change from a ‘follow-on pattern’ to leading high-level and high-quality international multi-centre trials. However, the construction of CTRUs is a complex systematic project. In addition to performing an excellent job in the top-level design and overall planning, CTRUs’ specific implementation process and measures need to be continuously explored in the practice process.

The objective of this study was to investigate and improve the use of automated data collection procedures for nursing research and quality assurance. A descriptive, correlational study analyzed 44 orthopedic surgical patients who were part of an evidence-based practice (EBP) project examining post-operative oxygen therapy at a Midwestern hospital. The automation work attempted to replicate a manually-collected data set from the EBP project. Automation was successful in replicating data collection for study data elements that were available in the clinical data repository. The automation procedures identified 32 "false negative" patients who met the inclusion criteria described in the EBP project but were not selected during the manual data collection. Automating data collection for certain data elements, such as oxygen saturation, proved challenging because of workflow and practice variations and the reliance on disparate sources for data abstraction. Automation also revealed instances of human error including computational and transcription errors as well as incomplete selection of eligible patients. Automated data collection for analysis of nursing-specific phenomenon is potentially superior to manual data collection methods. Creation of automated reports and analysis may require initial up-front investment with collaboration between clinicians, researchers and information technology specialists who can manage the ambiguities and challenges of research and quality assurance work in healthcare.

Churning of the customer base is always a top issue in Banking. It is directly related to recurrent revenue, and the ever increasing acquisition costs for new customers. In a first approach, this issue is related to both the quality of service (which is mainly in the front-office, say the contact center) and the speed of service, which is mainly in the back-office. Many studies published to date on this required manual data collection. This creates in general two concerns: worker behavior may change under observation, and manual data collection is expensive and often error prone. In this paper it is shown by means of a case study for a Multi-National Bank (with 5000 employees in the back office) how automated Business Process Discovery, which is an advanced type of process mining, makes it possible to handle the above concerns. The automated data collection and the analysis, in terms of Hidden Markov Models, are key elements. Several results regarding the quality and speed of service have been obtained. Most interesting was the discovery of deeper root causes for customer attrition. Once the deficiencies in the processes are identified, appropriate process improvements can be designed and simulated based on the models emerging from process discovery. In this case study, significant quality and speed improvements as well as customer churn reductions have been obtained.

Planning and estimating earthmoving operations is a challenging assignment because of the complexities and the uncertainties involved. The analysis during planning and estimating stages can be accomplished through deterministic or stochastic methods with data provided by manual or automated data collection technologies. It can be argued that the proficiency of each analysis methodology is often dependent on the characteristics of the corresponding data collection technology. Electronic sensors, load cells, and microprocessors — collectively referred to as vehicle instrumentation — have made it possible to automatically collect field cycle time data for earthmoving operations. Although automatic data collection overcomes several drawbacks associated with manual data collection, it posses challenges of a different nature. The primary challenges are the need to handle a large volume of indiscriminate data and the need to associate the field data with the operating conditions. The methodology to handle the large volume data is discussed in Kannan et al. This paper addresses the issue of associating the cycle time data and the corresponding operating conditions through the use of images. The process of identifying the operating conditions using images and linking it to cycle time data collected by vehicle instrumentation is termed as characterization.

Manual Data Collection from a student's exam-sheets is always a tedious job which exacts ample amount of time and effort. This paper has suggested a novel approach for developing an automatic, adaptive, fast and reliable system capable of recognizing enrolment number and corresponding marks of student from answer sheet and storing it in the host computer. This system consist a hardware which picks out sheets one by one from a bundle and captures image of the front page of each answer script. This image is processed by proposed robust extraction and noise removal algorithm adaptive to environmental conditions. It is then passed through Optical Character Recognition (OCR) system which extracts characters using correlation. Accuracy of system depends on the sample space size of OCR system. In our experiment we have archived average 81% accuracy in various light and paper (Exam-Sheet) condition. We had trained the OCR with 50 samples of numerals set (0–9). In this way developed system will not only replace the traditional tiring way of manual writing of marks in database but in addition can calculate average marks of all students, ranges of marks for assigning different grade and provide grade for each student automatically

Introduction Efficiently updating construction schedules is vital for managing dynamic workflows. Traditional methods like Gantt charts rely on manual updates, but newer approaches, such as Chronographic Modelling and Dynamic Modelling of Occupancy Rate Scheduling (DMORS), require tracking spatial positioning and occupancy rates of teams and resources. Given the complexity of these tasks, emerging technologies are being adopted to enhance data collection. Methods This study reviews recent advancements in tracking construction site occupancy and spatial positioning. It categorizes these technologies into three main types: image and video capture, 3D point cloud generation, and sensor-based tracking systems. Each method is evaluated based on seven essential criteria: data collection speed, portability, accuracy, worksite impact, post-processing time, accessibility, and technology maturity. Results The research provides a selection framework for contractors, helping them choose the most suitable tools based on project size and complexity. High-precision tools like LiDAR and laser scanning are ideal for large contractors requiring detailed modelling, while smaller firms may prefer cost-effective solutions such as manual data collection with photos or videos. Discussion This research highlights that no single technology meets all needs for tracking construction site occupancy. High-precision tools offer accuracy but may disrupt work, while simpler methods are easier to use but less detailed. So, depending on the size of the contractor or the project, the best technology may vary. Conclusion Ultimately, this paper supports the integration of space planning schedules in construction management by offering a structured approach to implementing modern data capture technologies. The study also highlights future trends, advocating for multi-technology integration to improve accuracy and exploring the potential of artificial intelligence for automated data analysis.

Most of the current techniques for automating progress data collection promise to eliminate laborintensive tasks associated with manual data collection. A drawback is the necessity to add additional steps to be performed before, during, or after utilization of such technologies. Working with such featureless data and without having semantic information of the scene, geometric-reasoning is problematic and induces estimation errors. In this paper application of unordered daily progress photograph logs, available on any job site, as a data collection technique is explored. In our proposed approach, a sparse 3D geometric scene of a construction site is reconstructed and photographs are geo-registered. This allows project managers to remotely explore as-built scene and geo-registered site photographs at different stages of progress, minimize their travel time, perform remote as-built analysis and use the proposed system as a tool for contractor coordination purposes. Furthermore, the point cloud allows the planned model to be registered with the asbuilt scene, in turn supporting development of the automatic 3D recognition technique and quantification of as-built progression from the geo-registered images. We present our results on two ongoing construction projects and further discuss technical issues on developing and implementing this technology for automation and visualization of as-built construction.

A detailed case study conducted at a highway construction project demonstrated that missing and inaccurate data items result in nonvalue adding (NVA) communication loops among the construction personnel. The implications (in terms of time and cost) of extra work associated with deficiencies in manual data collection and transfer are not well quantified. In this paper, a simulation-based framework is used to model information flow processes from a job site to a field office to measure and highlight existing deficiencies, and to model and demonstrate the effect of using automated reality capture technologies (laser scanners and radio frequency identification), in streamlining the data collection process for the same project. The simulation results showed that the NVA times of each agent involved in the information flow can be reduced by utilizing data collection technologies. This framework can be used by researchers and construction practitioners to identify inefficiencies in the current information flow at sites quantitatively and understand the benefits of using automated reality capture technologies to reduce these inefficiencies.

Perioperative Cardiac Research Considerations During the Coronavirus Disease 2019 (COVID-19) Pandemic

The ethics of global clinical trials: In developing countries, participation in clinical trials is sometimes the only way to access medical treatment. What should be done to avoid exploitation of disadvantaged populations?

Various rapid propagation strategies have been discovered, which has facilitated large-scale plant reproduction and cultivar development. These methods, in many plant species, are used to rapidly generate large quantities (900 mini-tubers/m2) of high-quality propagule (free from contamination) at a relatively low cost in a small space. They are also used for plant preservation. This review article aims to provide potential applications for regeneration and clonal propagation. Plant propagation using advanced agrotechnology, such as aeroponics, is becoming increasingly popular among academics and industrialists. The advancement of asexual aeroponic propagation has been achieved through advancements in monitoring and control systems using IoT and smart sensor technology. New sensor technology systems have gained substantial interest in agriculture in recent years. It is used in agriculture to precisely arrange various operations and objectives while harnessing limited resources with minimal human intervention. Modern intelligent technologies and control systems simplify sensor data collection, making it more efficient than manual data collection, which can be slow and prone to errors. Specific ambient variables like temperature, humidity, light intensity, stock solution concentrations (nutrient water), EC (electrical conductivity), pH values, CO2 content, and atomization parameters (frequency and interval) are collected more effectively through these systems. The use of intelligent technologies provides complete control over the system. When combined with IoT, it aids in boosting crop quality and yield while also lowering production costs and providing data directly to tablets and smartphones in aeroponic propagation systems. It can potentially increase the system’s productivity and usefulness compared to the older manual monitoring and operating methods.

Financial information systems play a crucial role in managing and processing company financial data. However, manual data collection and processing often require time and are prone to errors. This study aims to optimize financial information systems by utilizing microcontrollers in the automation of data collection and processing. The method used involved the development of a microcontroller-based prototype system to automate real-time financial data monitoring and reporting. The results showed that the use of microcontrollers significantly improved the efficiency and accuracy of financial data processing, while reducing the time needed for reporting. In conclusion, a microcontroller-based system can be an effective solution to enhance the quality and speed of financial data processing in companies.