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Background Research addressing value in healthcare requires a measure of cost. While there are many sources and types of cost data, each has strengths and weaknesses. Many researchers appear to create study-specific cost datasets, but the explanations of their costing methodologies are not always clear, causing their results to be difficult to interpret. Our solution, described in this paper, was to use widely accepted costing methodologies to create a service-level, standardized healthcare cost data warehouse from an institutional perspective that includes all professional and hospital-billed services for our patients. Methods The warehouse is based on a National Institutes of Research–funded research infrastructure containing the linked health records and medical care administrative data of two healthcare providers and their affiliated hospitals. Since all patients are identified in the data warehouse, their costs can be linked to other systems and databases, such as electronic health records, tumor registries, and disease or treatment registries. Results We describe the two institutions’ administrative source data; the reference files, which include Medicare fee schedules and cost reports; the process of creating standardized costs; and the warehouse structure. The costing algorithm can create inflation-adjusted standardized costs at the service line level for defined study cohorts on request. Conclusion The resulting standardized costs contained in the data warehouse can be used to create detailed, bottom-up analyses of professional and facility costs of procedures, medical conditions, and patient care cycles without revealing business-sensitive information. After its creation, a standardized cost data warehouse is relatively easy to maintain and can be expanded to include data from other providers. Individual investigators who may not have sufficient knowledge about administrative data do not have to try to create their own standardized costs on a project-by-project basis because our data warehouse generates standardized costs for defined cohorts upon request.

Organizations now use warehouse efficiency as a centre of expertise or a strategic weapon. A warehouse that works well can meet customer needs quickly and helps a business do better. So, the goal of this study is to look at how the attributes of a warehouse affect warehouse efficiency. This study looks at two attributes about warehouses: their layout and warehouse operations. A literature review was first conducted to find the role of warehouse attributes (layout and operation) in warehouse efficiency to draw lessons from the literature. The articles that were published between 2019 and 2022 were examined. The authors evaluated the studies' eligibility, retrieved data from the studies that were included, and assessed the study's quality and bias risk. Several studies showed that the attributes of a warehouse make a big difference in how well it works by showing the good effects on efficiency. Also, a warehouse is more efficient when it is set up in a way that makes it easy to meet customer needs quickly. Along with how the warehouse is set up, warehouse operations are a key part of making it more efficient. Layout and operations work together to make a warehouse more efficient as a whole.

Big data has the potential to create significant value in health care by improving outcomes while lowering costs. Big data's defining features include the ability to handle massive data volume and variety at high velocity. New, flexible, and easily expandable information technology (IT) infrastructure, including so-called data lakes and cloud data storage and management solutions, make big-data analytics possible. However, most health IT systems still rely on data warehouse structures. Without the right IT infrastructure, analytic tools, visualization approaches, work flows, and interfaces, the insights provided by big data are likely to be limited. Big data's success in creating value in the health care sector may require changes in current polices to balance the potential societal benefits of big-data approaches and the protection of patients' confidentiality. Other policy implications of using big data are that many current practices and policies related to data use, access, sharing, privacy, and stewardship need to be revised.

With the rapid growth of the Internet of Things (IoT) and the emergence of big data, handling massive amounts of data has become a major challenge. Traditional approaches involve sending raw data to cloud data centers for cleaning, processing, and interpretation using data warehouse tools. However, this study introduces BlueEdge, a fog edge mobile application that aims to shift the cleaning and preprocessing tasks from the cloud to the edge. We compare BlueEdge with four popular data cleaning tools (WinPure, DoubleTake, WizSame, and DQGlobal) that operate within data warehouse architectures, such as Hadoop servers. The comparison considers criteria such as time consumption, resource utilization (memory and CPU), and tool performance. BlueEdge utilizes Natural Language Processing (NLP) techniques, including those from the Natural Language Toolkit (NLTK) and Python packages, to connect with a real-time database. As shown in our results, the accuracy values that BlueEdge showed ranged between 72 and 95% across 6 categories of name-based duplicate detection tasks, proving its competitive performance in mobile edge environments. The validation of the framework was done using a larger dataset of 146 error cases with statistically significant values having confidence interval of between 3.4% to 5.8. Statistical comparison indicates consistently significant changes ( p < 0.05) compared to baseline settings of four commercial tools with large effect sizes ( Cohen d: 0.89- 1.34). BlueEdge takes care of data duplication elimination services such as using different spelling and pronunciation (78.4%, CI: 73.1–83.7%), misspellings (72.0%, CI: 66.2–77.8%), name abbreviations (90.5%, CI: 86.1–94.9%), honorific prefixes (95.2%, CI: 91.8–98.6%), common nicknames (76.2%, C The reliable performance of edge-based data cleaning is verified through cross-validation analysis (81.7% ± 2.3%), the results of which prove the consistency of its activity. Additionally, BlueEdge utilizes a minimal bandwidth of only 5000 bytes per edge on mobile phones, unlike data warehouses that require 10,000–60,000 bytes on Hadoop machines. Additionally, BlueEdge is designed to reduce the time taken for data cleaning to 1 s at the data edge, unlike the standard 4–30 s it normally takes for data warehouses. The blue edge is easy to use without authorization of the mobile devices, where the application is conducted free of charge. The framework was validated through controlled experimental testing and real-world deployment at an IT services company, achieving an overall ITSQM quality score of 8.9/10 and demonstrating practical effectiveness in organizational settings. This foundation has been further enhanced with neural network-based classification approaches, which are currently under peer review.

Background Heart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, management, and mining of large volumes of structured and semi-structured data, such as complex healthcare data. Applying these advances to complex healthcare data has led to the development of risk prediction models to help identify patients who would benefit most from disease management programs in an effort to reduce readmissions and healthcare cost, but the results of these efforts have been varied. The primary aim of this study was to develop a 30-day readmission risk prediction model for heart failure patients discharged from a hospital admission. Methods We used longitudinal electronic medical record data of heart failure patients admitted within a large healthcare system. Feature vectors included structured demographic, utilization, and clinical data, as well as selected extracts of un-structured data from clinician-authored notes. The risk prediction model was developed using deep unified networks (DUNs), a new mesh-like network structure of deep learning designed to avoid over-fitting. The model was validated with 10-fold cross-validation and results compared to models based on logistic regression, gradient boosting, and maxout networks. Overall model performance was assessed using concordance statistic. We also selected a discrimination threshold based on maximum projected cost saving to the Partners Healthcare system. Results Data from 11,510 patients with 27,334 admissions and 6369 30-day readmissions were used to train the model. After data processing, the final model included 3512 variables. The DUNs model had the best performance after 10-fold cross-validation. AUCs for prediction models were 0.664 ± 0.015, 0.650 ± 0.011, 0.695 ± 0.016 and 0.705 ± 0.015 for logistic regression, gradient boosting, maxout networks, and DUNs respectively. The DUNs model had an accuracy of 76.4% at the classification threshold that corresponded with maximum cost saving to the hospital. Conclusions Deep learning techniques performed better than other traditional techniques in developing this EMR-based prediction model for 30-day readmissions in heart failure patients. Such models can be used to identify heart failure patients with impending hospitalization, enabling care teams to target interventions at their most high-risk patients and improving overall clinical outcomes.

For the taxed goods, the actual freight is generally determined by multiplying the allocated freight for each KG and actual outgoing weight based on the outgoing order number on the outgoing bill. Considering the conventional logistics is insufficient to cope with the rapid response of e-commerce orders to logistics requirements, this work discussed the implementation of data mining technology in bonded warehouse inbound and outbound goods trade. Specifically, a bonded warehouse decision-making system with data warehouse, conceptual model, online analytical processing system, human-computer interaction module and WEB data sharing platform was developed. The statistical query module can be used to perform statistics and queries on warehousing operations. After the optimization of the whole warehousing business process, it only takes 19.1 hours to get the actual freight, which is nearly one third less than the time before optimization. This study could create a better environment for the development of China's processing trade.

Recent developments in Big Data in financial industry has created a huge opportunity for design and development of effective aggregation (higher level) analytical measures (Fund, Portfolio, Sector, Industry etc.). Lack of these aggregated measures will jeopardize organization’s ability to provide the financial services promised to clients. Vendor solutions and existing academic research (Data Cube, OLAP) can provide these aggregated measures but are expensive, time consuming and not practical to implement for a small to mid-size investment organization. Our proposed solution using rule-based architecture is cost effective, efficient and building block for “Rapid Application and Decision Support Systems on Big Data”. Our new approach “Selective Dimensional Cuboids” provides a simple but robust solution with flexibility for future expansion into data mining, portfolio trend analysis and cycle forecasting. The solution is easily portable to any dimensional data set.

In the era of big data, organizations face critical decisions when selecting between data lakes and data warehouses to meet their analytics requirements. This article presents a comprehensive comparative analysis of these two predominant data management architectures, emphasizing their structural differences, functional capabilities, and suitability for diverse analytics workloads. Data lakes offer scalable, cost-effective storage for raw, unstructured, and semi-structured data, supporting advanced analytics and machine learning applications. In contrast, data warehouses provide optimized, schema-on-write frameworks for fast querying and reliable reporting on structured data. Through detailed examination of architectural designs, integration with big data tools including Hadoop, Spark, and Kafka, and evaluations based on performance, scalability, cost, and governance, this paper provides organizations with evidence-based guidance to align their data strategies with business objectives. Case studies from healthcare and retail sectors illustrate practical implications of each approach, while emerging trends such as lakehouse architectures, AI integration, blockchain security, edge computing, and quantum computing highlight future directions. The findings support for a hybrid data management solution that leverages the strengths of both data lakes and warehouses to enable robust, scalable, and innovative big data analytics.

Purpose – Warehouse picking is often referred to as the most labour-intensive, expensive and time consuming operation in manual warehouses. These factors are becoming even more crucial due to recent trends in manufacturing and warehousing requiring the processing of orders that are always smaller and needed in a shorter time. For this reason, in recent years more efficient and better performing systems have been developed, employing various technological solutions that can support pickers during their work. The purpose of this paper is to introduce a comparison of five paperless picking systems (i.e. barcodes handheld, RFID tags handheld, voice picking, traditional pick-to-light, RFID pick-to-light). Design/methodology/approach – Warehouse picking is often referred to as the most labour-intensive, expensive and time consuming operation in manual warehouses. These factors are becoming even more crucial due to recent trends in manufacturing and warehousing requiring the processing of orders that are always smaller and needed in a shorter time. For this reason, in recent years more efficient and better performing systems have been developed, employing various technological solutions that can support pickers during their work. The present paper introduces a comparison of five paperless picking systems (i.e. barcodes handheld, RFID tags handheld, voice picking, traditional pick-to-light, RFID pick-to-light. Findings – The proposed approach contributes to the understanding of the performance of different technologies in different application fields; some solutions are more suitable for a low-level warehouse, others bring greater benefits in the case of picking from multilevel shelving. Originality/value – The study concerns an issue that until now has received very little attention in the literature. It compares some traditional solutions with some innovative ones by an economic evaluation. The presented hourly cost function also takes into account the different errors arising and their probability of occurrence.

It has been widely assumed that better management and coordination of care for chronic conditions and high-risk patients would be the leading mechanisms for achieving savings in accountable care organizations (ACOs), specifically by reducing acute care needs through enhanced outpatient and preventive care. We examined the extent to which changes in spending and hospitalizations for ACO patients in the Medicare Shared Savings Program (MSSP) have been consistent with this expectation. By 2014, participation in the MSSP was associated with significant reductions in total Medicare fee-for-service spending for ACO patients but with proportionately smaller reductions in hospitalizations and some increases in hospitalizations for ambulatory care-sensitive conditions. In addition, spending reductions were not clearly concentrated among high-risk patients: Reductions for those patients accounted for only 38 percent of the total reduction among ACOs entering the MSSP in 2012, and reductions among 2013 MSSP entrants were almost entirely concentrated among lower-risk patients. These findings suggest that, on average, care coordination and management efforts focused on ambulatory care-sensitive conditions and high-risk patients have not been the major drivers of early savings in the MSSP.