Operational Data Sources Research Articles

Development of a real-world database for asthma and COPD: The SingHealth-Duke-NUS-GSK COPD and Asthma Real-World Evidence (SDG-CARE) collaboration

PurposeThe SingHealth-Duke-GlaxoSmithKline COPD and Asthma Real-world Evidence (SDG-CARE) collaboration was formed to accelerate the use of Singaporean real-world evidence in research and clinical care. A centerpiece of the collaboration was to develop a near real-time database from clinical and operational data sources to inform healthcare decision making and research studies on asthma and chronic obstructive pulmonary disease (COPD).MethodsOur multidisciplinary team, including clinicians, epidemiologists, data scientists, medical informaticians and IT engineers, adopted the hybrid waterfall-agile project management methodology to develop the SingHealth COPD and Asthma Data Mart (SCDM). The SCDM was developed within the organizational data warehouse. It pulls and maps data from various information systems using extract, transform and load (ETL) pipelines. Robust user testing and data verification was also performed to ensure that the business requirements were met and that the ETL pipelines were valid.ResultsThe SCDM includes 199 data elements relevant to asthma and COPD. Data verification was performed and found the SCDM to be reliable. As of December 31, 2019, the SCDM contained 36,407 unique patients with asthma and COPD across the spectrum from primary to tertiary care in our healthcare system. The database updates weekly to add new data of existing patients and to include new patients who fulfil the inclusion criteria.ConclusionsThe SCDM was systematically developed and tested to support the use RWD for clinical and health services research in asthma and COPD. This can serve as a platform to provide research and operational insights to improve the care delivered to our patients.

OLAP SYSTEMS AS THE MODERN DATA PREPARATION TOOLS FOR OUTDOOR ADVERTISING DATA MINING

Today, most organizations use databases and at worst text documents and spreadsheet files as sources for data analysis, which prevent correct and error-free analysis. At best, the data can be constantly adjusted due to ambiguities and inaccuracies. The subject of the study is the intellectual analysis of outdoor advertising data. The methodology of successful data analysis is the correct storage of data, which is the basis for clear data analysis. Modern computer systems and computer networks allow the accumulation of large arrays of data to solve problems of processing and analyzing. Unfortunately, the machine form of data presentation itself contains the information that a person needs in a hidden form, and you need to use special methods of data analysis to obtain it. In order to get what you want, you need to create not just a database, but a data warehouse with a special storage structure. Thus, the data warehouse allows you to collect data from various sources, databases, table files and other things, store them throughout history and, unlike conventional databases, allows you to create systems for fast and accurate data analysis. Data warehouse is the basis for building decision support systems. Operational data is checked, cleared and aggregated before entering the data warehouse. Such integrated data is much easier to analyze. Different sources of operational data may contain data describing the same subject area from different points of view (for example, from the point of view of accounting, inventory control, planning department, etc.). A decision made on the basis of only one point of view can be ineffective or even erroneous. The goal is to use a data warehouse to integrate information that reflects different perspectives on the same subject area. Focus on the object, which will also allow the data warehouse to store only the data you need to analyze it. It will also significantly increase the speed of data access both due to the possible redundancy of the stored information and due to the exclusion of modification operations. Conclusion: the decision support system will ensure reliable storage of large amounts of data. Tasks will also be assigned to prevent unauthorized access, data backup, archiving, etc.

Creating an analytics ecosystem to support risk-based contracting

Payers are increasingly pressing hospitals and health systems to assume more financial risk through risk-based contracts. However, very few healthcare organisations have the information they need to identify the medical spend on care delivered to patients and to tie the cost of care to clinical outcomes, capabilities that are important to managing financial risk. To successfully manage risk-based contracts, organisations must develop systems that enable insightful decision-making about risk and value. That requires a shift in basic data strategy. Most healthcare organisations today focus on maintaining information systems to manage multiple silos of clinical, operational and financial data. To succeed under value-based care, an organisation must start building an analytics ecosystem for managing patient populations. A healthcare analytics ecosystem is an interconnected set of clinical, financial and operational data sources and analytic capabilities that enable provider organisations to plan, negotiate and manage risk-based contracts. To build an analytics ecosystem, healthcare leaders must translate data into actionable information, leverage that information to create clinical programmes that generate new knowledge, and use analytical tools to turn knowledge into insights that drive strategic decision-making and action.

Improving Clinic Operational Efficiency and Utilization with RTLS.

New sources of operational data are leading to novel healthcare delivery system design and opportunities to support operational planning and decision-making. Technologies such as real time locating systems (RTLS) provide a unique view and understanding of how healthcare delivery settings behave and respond to operational design changes. In this paper RTLS data from an outpatient clinical setting is leveraged to identify the appropriate number of scheduled providers in order to improve the utilization of the clinical space while balancing the negative effects of clinic congestion. The approaches presented pair historical utilization rates for the clinical space with scheduled provider and patient volumes to support scheduling decisions in an operationally flexible clinic design. These historical data are augmented with clinic staff observation logs to identify target utilization rates as well as high congestion levels. Results are presented for two approaches: one where utilization of clinical space is a key performance metric and another where the decision-maker may be risk averse toward the use of provider time and use a probabilistic approach to determine provider staffing levels.

Operational Loss Data Collection: A Literature Review

This paper is the first to provide a comprehensive overview of the worldwide operational loss data collection exercises (LDCEs) of internal loss, external loss, scenario analysis and business environment and internal control factors (BEICFs). Based on analyzing operational risk-related articles from 2002 to March 2017 and surveying a large amount of other information, various sources of operational risk data are classified into five types, i.e. individual banks, regulatory authorities, consortia of financial institutions, commercial vendors and researchers. Then by reviewing operational risk databases from these five data sources, we summarized and described 32 internal databases, 26 external databases, 7 scenario databases and 1 BEICFs database. We also find that compared with developing countries, developed countries have performed relatively better in operational risk LDCEs. Besides, the two subjective data elements of scenario analysis and BEICFs are less used than the two objective data elements of internal and external loss data in operational risk estimation.

Identifying experts for engineering changes using product data analytics

This paper aims to provide an expert identification procedure in an organization where design engineers share an integrated product data management (PDM) database for their product development and engineering changes (ECs). To identify experts for ECs, the procedure follows a product data analytics (PDA) approach that uses PDM databases as its operational data source to analyze different aspects of product development processes managed by PDM systems. It also employs a two-phase analysis procedure that considers the artefact and actor networks of the PDM system and participating engineers. The procedure also introduces EC history-centered multidimensional data analysis and social network analysis (SNA) for the two phases, respectively. To demonstrate the feasibility of the procedure, this study implemented it using a research-purpose PDM system, extract-transform-load (ETL) module, data cube with on-line analytical processing and SNA engines. It also provides a product design example with multiple engineering changes applied to the implemented prototype system as proof of the implementation and the procedure.

Clinical Data Models at University Hospitals of Geneva.

In order to reuse data for clinical research it is then necessary to overcome two main challenges - to formalize data sources and to increase the portability. Once the challenge is resolved, it then will allow research applications to reuse clinical data. In this paper, three data models such as entity-attribute-value, ontological and data-driven are described. Their further implementation at University Hospitals of Geneva (HUG) in the data integration methodologies for operational healthcare data sources of the European projects such as DebugIT and EHR4CR and national project the Swiss Transplant Cohort Study are explained. In these methodologies the clinical data are either aligned according to standardised terminologies using different processing techniques or transformed and loaded directly to data models. Then these models are compared and discussed based on the quality criteria. The comparison shows that the described data models are strongly dependent on the objectives of the projects.

Traditional Enterprise Business Intelligence Software Compared to Software as a Service Business Intelligence

IntroductionBusiness Intelligence is a broad field of study. The major thrust of business intelligence theory analyzes certain factors to make highquality decisions. These factors include customers, competitors, business partners, economic environment and internal operations. Business Intelligence has the power to change the way people work, so that enterprises can compete more effectively and efficiently.A successful business intelligence solutions and successful implementation holds the key to the technological innovations together with the people, processes and culture of an organization to achieve a competitive and profitable BI strategy.Business Intelligence (BI) is the process of acquiring operational data and the use of this data in order to make the best business decisions. BI software takes the information bulk of the operational data sources of an enterprise, integrates and analyzes them and presents timely verdicts in the form of reports, alerts and dashboards.These reports, alerts and dashboards provide a more accurate and actionable view of the situation of a company.The success of a BI solution is ultimately in how well it helps both business and technical users in an organization to meet their missioncritical objectives: meeting or exceeding the revenue figures, looking for ways to reduce costs across the organization and maximize profitability by identifying the most profitable customers, products, services or programs. In fact, a BI solution should be able to identify otherwise unknown situations.Within an organization a BI solution helps users meet the following critical business objectives: meeting or exceeding the revenue figures, maximizing profitability by identifying the most profitable programs, looking for ways to reduce costs across the organization, creating a 360-degree view directed to customers.A business intelligence solution allows business teams the correlation of data across the enterprise to understand and to synthesize comprehensive analysis of the results, trends and expected results in order to understand the implications of this insight on corporate goals. BI users have an understanding of what is happening and what future actions need to be taken.2.Business Intelligence SolutionsBusiness intelligence (BI) solutions have already proven their worth a lot of times in the past. The reason for their popularity can be explained by various new developments.Especially the following trends play a major role: on the one hand, the emergence of new valuable and relevant data sources, on the other hand, a new generation of BI visualization tools based on HTML5 and JavaScript, and finally the removal of many traditional ERP functionalities and the growth of new enterprise capabilities in the cloud.Here are a few questions that can be easily answered by BI:Sales and marketing issues:* Which customers does a company want to target group?* What caused the change in the pipeline?* Which are a company's most profitable campaigns per region?* What's the turnover after an announcement of a new product release?* What is the most profitable source of revenue and how has it changed over time?Operational Questions* Which vendors have the best provision of time and budget?* How much additional staff is needed per store during the holidays?* What order fulfillment process is highly inefficient?Financial Questions* What are the costs for new products?* What is the expected annual profit / loss on the basis of current marketing and sales activities predictions?* What are the forecasts compared to the annual plan?* What are the current trends in cash flow, accounts payable and accounts receivable, and how are they comparing to the plan?Business Performance Questions* What are the main risk factors affecting the company's ability to meet the annual profit goals? …

Application of Data Warehouse in Real Life: State-of-the-art Survey from User Preferences’ Perspective

In recent years, due to increase in data complexity and manageability issues, data warehousing has attracted a great deal of interest in real life applications especially in business, finance, healthcare and industries. As the importance of retrieving the information from knowledge-base cannot be denied, data warehousing is all about making the information available for decision making. Data warehouse is accepted as the heart of the latest decision support systems. Due to the eagerness of data warehouse in real life, the need for the design and implementation of data warehouse in different applications is becoming crucial. Information from operational data sources are integrated by data warehousing into a central repository to start the process of analysis and mining of integrated information and primarily used in strategic decision making by means of online analytical processing techniques (OLAP). Despite the applications of data warehousing techniques in number of areas, there is no comprehensive literature review for it. This survey paper is an effort to present the applications of data warehouse in real life. It focuses to help the scholars knowing the analysis of data warehouse applications in number of domains. This survey provides applications, case studies and analysis of data warehouse used in various domains based on user preferences.

Dinámica atmosférica y oceánica en algunos sitios del Área de Conservación Guanacaste (ACG), Costa Rica

<p>Se estudiaron las condiciones oceanográficas y meteorológicas en cuatro regiones relacionadas con parques nacionales en el Área de Conservación Guanacaste. Parámetros como temperatura, salinidad y oxígeno disuelto se estudiaron a través de la columna de agua en al menos un ciclo de marea en las bahías Tamarindo, Culebra, Santa Elena en el Pacífico Norte de Costa Rica, y en Bahía Ballena en la parte externa del Golfo de Nicoya. Al mismo tiempo se midieron corrientes marinas y viento local, de manera que se pudieran evaluar las fuerzas dinámicas en estos sitios. Se consultaron variables ambientales desde fuentes de datos operacionales alrededor de Costa Rica, como las corrientes marinas superficiales y el patrón de viento al frente de esta región, para conocer las condiciones prevalecientes y su relación con la dinámica de las variables medidas en cada sitio. Se encontró que cuando el viento sopla fuerte sobre esta zona, una condición típica de la época seca en el Pacífico Norte de Costa Rica, las corrientes marinas son intensas al frente de la Península de Nicoya. Esta es una característica coherente con las condiciones oceanográficas existentes cuando estos vientos se generan mar adentro al frente del Golfo de Papagayo. Estas fuerzas externas influencian la circulación de bahías abiertas sobre esta región, como en la Bahía de Tamarindo. El viento local y la marea son componentes que modulan la circulación local, especialmente en bahías muy cerradas como la Bahía de Santa Elena y Bahía Ballena. La estructura de la capa de agua en Bahía Ballena muestra una estratificación de corrientes, evidenciando una condición estuarina típica de la presencia de diferentes campos termohalinos dominados por las fuerzas de marea.</p>

A simple, transparent and rational weighting approach to combining different operational risk data sources

According to the Basel financial regulation, the use of external data is indispensable to the implementation of an Advanced Measurement Approach (AMA) for calculating operational risk capital. This paper aims to provide a simple solution to perhaps the most challenging step of building any capital model for Operational Risk – the integration of internal and external data. We propose a generic weighting function based on a non-parametric approach that can be used to weight the different distributions, in line with the regulatory requirements under the AMA. After analysing the different driving factors and considering the desired sensitivities of the weights, we build and calibrate a weighting function to match all the necessary and relevant conditions. This approach is completely flexible (it can be used with different constraints or it can be adapted so that its sensitivity depends on the risk profile of the bank) while simultaneously being highly tractable.

Pseudo‐Zernike‐based multi‐pass automatic target recognition from multi‐channel synthetic aperture radar

The capability to exploit multiple sources of information is of fundamental importance in a battlefield scenario. Information obtained from different sources, and separated in space and time, provides the opportunity to exploit diversities to mitigate uncertainty. In this study, the authors address the problem of automatic target recognition (ATR) from synthetic aperture radar platforms. The author's approach exploits both channel (e.g. polarisation) and spatial diversity to obtain suitable information for such a critical task. In particular they use the pseudo-Zernike moments (pZm) to extract features representing commercial vehicles to perform target identification. The proposed approach exploits diversities and invariant properties of pZm leading to high confidence ATR, with limited computational complexity and data transfer requirements. The effectiveness of the proposed method is demonstrated using real data from the Gotcha dataset, in different operational configurations and data source availability.

A Simple, Transparent and Rational Weighting Approach to Combine Different Operational Risk Data Sources

According to the Basel financial regulation, the use of external data is indispensable to the implementation of an Advanced Measurement Approach (AMA) for calculating operational risk capital. This paper aims to provide a simple solution to perhaps the most challenging step of building any capital model for Operational Risk – the integration of internal and external data. We propose a generic weighting function based on a non-parametric approach that can be used to weight the different distributions, in line with the regulatory requirements under the AMA. After analysing the different driving factors and considering the desired sensitivities of the weights, we build and calibrate a weighting function to match all the necessary and relevant conditions. This approach is completely flexible (it can be used with different constraints or it can be adapted so that its sensitivity depends on the risk profile of the bank) while simultaneously being highly tractable.

Framework of behavioral indicators for outcome evaluation of TB health promotion: a Delphi study of TB suspects and Tb patients.

BackgroundHealth promotion for prevention and control of Tuberculosis (TB) is implemented worldwide because of its importance, but few reports have evaluated its impact on behavior due to a lack of standard outcome indicators. The objective of this study was to establish a framework of behavioral indicators for outcome evaluation of TB health promotion among TB suspects and patients.MethodsA two-round modified Delphi method involving sixteen TB control experts was used to establish a framework of behavioral indicators for outcome evaluation of TB health promotion targeted at TB suspects and patients.ResultsSixteen of seventeen invited experts in TB control (authority score of 0.91 on a 1.0 scale) participated in round 1 survey. All sixteen experts also participated in a second round survey. After two rounds of surveys and several iterations among the experts, there was consensus on a framework of indicators for measuring outcomes of TB health promotion for TB suspects and patients. For TB suspects, the experts reached consensus on 2 domains (“Healthcare seeking behavior” and “Transmission prevention”), 3 subdomains (“Seeking care after onset of TB symptoms”, “Pathways of seeking care” and “Interpersonal contact etiquette”), and 8 indicators (including among others, “Length of patient delay”). For TB patients, consensus was reached on 3 domains (“Adherence to treatment”, “Healthy lifestyle” and “Transmission prevention”), 8 subdomains (including among others, “Adherence to their medication”), and 14 indicators (including “Percentage of patients who adhered to their medication”). Operational definitions and data sources were provided for each indicator.ConclusionsThe findings of this study provide the basis for debate among international experts on a framework for achieving global consensus on outcome indicators for TB health promotion interventions targeted at TB patients and suspects. Such consensus will help to increase effectiveness of TB health promotion, while ensuring international comparability of outcome data.

Prototype of a Web ETL Tool

Extract, transform and load (ETL) is a process that makes it possible to extract data from operational data sources, to transform data in the way needed for data warehousing purposes and to load data into a data warehouse (DW). ETL process is the most important part when building the data warehouse. Because the ETL process is a very complex and time consuming, this paper presents a prototype of a web ETL tool that offers step-by-step guidance through the entire process to the end user. This ETL tool is designed as a web application so users can save time (and space) required for installation purposes.

Fact – Centered ETL: A Proposal for Speeding Business Analytics up

Abstract In real-time Business Analytics scenarios, like Business Activity Monitoring (BAM) or Operational Intelligence, speeding up ETL is fundamental to provide business users with up-to-date data in order to support decision-making process and to optimize business operations. Conventional ETL processes extract data from heterogeneous operational data sources, transform them according to some predefined semantic and syntactic rules, and finally load results into a new (relational or data warehouse) model, in order to be processed both for business monitoring or analysis. Usually the whole loading process is presented as a technical aspect, far from business facts. In real-time Business Analytics context the traditional ETL approach can still be valid, provided that ETL process has to be designed in a way that circumscribes individual business events and facts and makes them independent one from another. In this context we propose a control-flow- based approach to ETL process modeling, which starts from business facts identification, and represents ETL processes using BPMN notation, which is the foundation for machine-readable code. Our main contribution consists in a proposal for structuring ETL processes and related objects and in its application to a business case.

Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory: Table 1

Widespread sharing of data from electronic health records and patient-reported outcomes can strengthen the national capacity for conducting cost-effective clinical trials and allow research to be embedded within routine care delivery. While pragmatic clinical trials (PCTs) have been performed for decades, they now can draw on rich sources of clinical and operational data that are continuously fed back to inform research and practice. The Health Care Systems Collaboratory program, initiated by the NIH Common Fund in 2012, engages healthcare systems as partners in discussing and promoting activities, tools, and strategies for supporting active participation in PCTs. The NIH Collaboratory consists of seven demonstration projects, and seven problem-specific working group 'Cores', aimed at leveraging the data captured in heterogeneous 'real-world' environments for research, thereby improving the efficiency, relevance, and generalizability of trials. Here, we introduce the Collaboratory, focusing on its Phenotype, Data Standards, and Data Quality Core, and present early observations from researchers implementing PCTs within large healthcare systems. We also identify gaps in knowledge and present an informatics research agenda that includes identifying methods for the definition and appropriate application of phenotypes in diverse healthcare settings, and methods for validating both the definition and execution of electronic health records based phenotypes.

The Use of Secondary Source Data for Measuring Performance in Operations Management Research

Secondary data sources are invaluable resources for spurring the growth of the second generation of empirical science in operations management (OM), as attested by their rise in published work over the past several years. Researchers utilizing secondary data are, by definition, exploiting data which was not collected specifically for the research question under study. In this paper, we offer a summary of the advantages and shortcomings of using secondary source data in order to provide a road map for improving its use in scholarly OM empirical research. In doing so, we offer a critical review of secondary data measures that have been used to evaluate performance in the extant OM literature from 1990-2013, covering five top-tier OM journals. In addition, we discuss the most frequently used sources of secondary operational performance data, and also highlight rich data sources not yet utilized in published OM work. Further, we present a conceptual typology of secondary data types and sources. Finally, we provide several suggestions and offer guidance to OM researchers who are conducting or considering conducting research using secondary source data.

On-Demand ELT Architecture for Right-Time BI

In a typical BI infrastructure, data, extracted from operational data sources, is transformed, cleansed, and loaded into a data warehouse by a periodic ETL process, typically executed on a nightly basis, i.e., a full day’s worth of data is processed and loaded during off-hours. However, it is desirable to have fresher data for business insights at near real-time. To this end, the authors propose to leverage a data warehouse’s capability to directly import raw, unprocessed records and defer the transformation and data cleaning until needed by pending reports. At that time, the database’s own processing mechanisms can be deployed to process the data on-demand. Event-processing capabilities are seamlessly woven into our proposed architecture. Besides outlining an overall architecture, the authors also developed a roadmap for implementing a complete prototype using conventional database technology in the form of hierarchical materialized views.

Interoperability driven integration of biomedical data sources.

In this paper, we introduce a data integration methodology that promotes technical, syntactic and semantic interoperability for operational healthcare data sources. ETL processes provide access to different operational databases at the technical level. Furthermore, data instances have they syntax aligned according to biomedical terminologies using natural language processing. Finally, semantic web technologies are used to ensure common meaning and to provide ubiquitous access to the data. The system's performance and solvability assessments were carried out using clinical questions against seven healthcare institutions distributed across Europe. The architecture managed to provide interoperability within the limited heterogeneous grid of hospitals. Preliminary scalability result tests are provided.