Implementing data-driven decision support system based on independent educational data mart

<span>Decision makers in the educational field always seek new technologies and tools, which provide solid, fast answers that can support decision-making process. They need a platform that utilize the students’ academic data and turn them into knowledge to make the right strategic decisions. In this paper, a roadmap for implementing a data driven decision support system (DSS) is presented based on an educational data mart. The independent data mart is implemented on the students’ degrees in 8 subjects in a private school (Al-Iskandaria Primary School in Basrah province, Iraq). The DSS implementation roadmap is started from pre-processing paper-based data source and ended with providing three categories of online analytical processing (OLAP) queries (multidimensional OLAP, desktop OLAP and web OLAP). Key performance indicator (KPI) is implemented as an essential part of educational DSS to measure school performance. The static evaluation method shows that the proposed DSS follows the privacy, security and performance aspects with no errors after inspecting the DSS knowledge base. The evaluation shows that the data driven DSS based on independent data mart with KPI, OLAP is one of the best platforms to support short-to-long term academic decisions.</span>

Study of Slowly Changing Dimension to Dependent Data Mart to Manage Finance Data

Financial institutions face many challenges of managing and marketing campaigns which leads in its data warehouse. The management of marketing campaign leads in dependent data mart with real time updating and recording difficulties especially when many campaigns are running parallel ways. To securing the customers from being contacted too often for sales-based marketing contacts, the concept of novelty skeleton are introduced to clamp the customers who have been targeted in Sales based campaign for a specified time period. During the novelty Frame, the customer cannot be targeted by other Sales based campaign categorized under the same channel. The introduction of novelty skeleton has increased the difficulties of campaign management and data management. The difficulties of data management include timely update and robust storage system of campaign leads. In this paper, we explained represent the concept of slowly changing dimension on dependent data mart and also studied how it can be used in the data mart of financial institutions to update and maintain marketing campaign records of customers.

Dashboard Business Intelligence Vusialisasi Data Akreditasi Sekolah Pada SMP Negeri 1 Sembawa

This study aims to create a business intelligence dashboard to support school accreditation at SMP Negeri 1 Sembawa in the form of data visualization. In this study, several business intelligence dashboards were produced with the aim of providing convenience for schools in supporting the school accreditation process. Business intelligence is a tool to manage data and perform data analysis that can support school accreditation which will be collected into a data mart and the analysis process is carried out in the form of a cube. For the ETL process using the help of SQL Server Management Studio and SQL Server Development software and as a design for the dashboard creation using the Power BI software. This research has several dashboards including student achievement dashboard based on achievement level, student achievement dashboard based on year of achievement, and student achievement based on graduation which can be seen by year and gender of students.

Natural Language Processing Enabling COVID-19 Predictive Analytics to Support Data-Driven Patient Advising and Pooled Testing

Objective The COVID-19 pandemic response at MUSC included virtual care visits for patients with suspected SARS-CoV-2 infection. The telehealth system used for these visits only exports a text note to integrate with the EHR, but structured and coded information about COVID-19 (e.g., exposure, risk factors, symptoms) was needed to support clinical care and early research as well as predictive analytics for data-driven patient advising and pooled testing. Methods To capture COVID-19 information from multiple sources, a new data mart and a new Natural Language Processing (NLP) application prototype were developed. The NLP application combined reused components with dictionaries and rules crafted by domain experts. It was deployed as a web service for hourly processing of new data from patients assessed or treated for COVID-19. The extracted information was then used to develop algorithms predicting SARS-CoV-2 diagnostic test results based on symptoms and exposure information. Results The dedicated data mart and NLP application were developed and deployed in a mere 10-day sprint in March 2020. The NLP application was evaluated with good accuracy (85.8% recall and 81.5% precision). The SARS-CoV-2 testing predictive analytics algorithms were configured to provide patients with data-driven COVID-19 testing advices with a sensitivity of 81-92% and to enable pooled testing with a negative predictive value of 90-91% reducing the required tests to about 63%. Conclusion SARS-CoV-2 testing predictive analytics and NLP successfully enabled data-driven patient advising and pooled testing.

E-Records Integrity – Data Warehouse And Data Mart Layer

Once a vast quantity of data is being generated and stored, it is becoming important to preserve the integrity of the information that’s collected. Understanding the basics of data integrity (DI) and how it works is the initial step in retaining the reliability of the data and keeping it safe. This article provides the DI issues in a Data...

O uso de Data Mart para apoio à tomada de decisão na Gestão de Pessoas em uma Instituição Federal de Ensino

Business Intelligence (BI) é composto por um banco de dados multidimensional, orientado por assunto, não volátil, histórico, decisório e variável em relação ao tempo. Ao aplicar o uso do Data Mart para uma Instituição Federal de Ensino no setor de gestão de pessoas, essa pesquisa trabalhou com os dados para desenvolver indicadores para a tomada de decisão. Questões como falta de sinergia entre as bases de dados existentes, qualidade dos dados fornecidos e impossibilidade da emissão de relatórios gerenciais em tempo hábil, foram tratados nesse estudo. Concluiu-se que a aplicação e implantação de BI através de Data Mart pode gerar dados precisos, solucionar os problemas e fornecer indicadores de desempenho.

The Digital Analytic Patient Reviewer (DAPR) for COVID-19 Data Mart Validation

Objective: To provide high-quality data for COVID-19 research, we validated COVID-19 clinical indicators and 22 associated computed phenotypes, which were derived by machine learning algorithms, in the Mass General Brigham (MGB) COVID-19 Data Mart. Materials and Methods: Fifteen reviewers performed a manual chart review for 150 COVID-19 positive patients in the data mart. To support rapid chart review for a wide range of target data, we offered the Digital Analytic Patient Reviewer (DAPR). DAPR is a web-based chart review tool that integrates patient notes and provides note search functionalities and a patient-specific summary view linked with relevant notes. Within DAPR, we developed a COVID-19 validation task-oriented view and information extraction logic, enabled fast access to data, and considered privacy and security issues. Results: The concepts for COVID-19 positive cohort, COVID-19 index date, COVID-19 related admission, and the admission date were shown to have high values in all evaluation metrics. For phenotypes, the overall specificities, PPVs, and NPVs were high. However, sensitivities were relatively low. Based on these results, we removed 3 phenotypes from our data mart. In the survey about using the tool, participants expressed positive attitudes towards using DAPR for chart review. They assessed the validation was easy and DAPR helped find relevant information. Some validation difficulties were also discussed. Discussion and Conclusion: DAPR's patient summary view accelerated the validation process. We are in the process of automating the workflow to use DAPR for chart reviews. Moreover, we will extend its use case to other domains.

Temporal Trends in Prescribing of Bone-Directed Therapies in the United States, 2009–2020

Multiple new osteoporosis therapies, including some with novel mechanisms of action, have been introduced in the past decade. However, little is known about temporal trends in prescribing these new medications. Using claims data from the Clinformatics Data Mart (Optum, Inc.), we determined the number of enrolled individuals over age 50 who were prescribed any osteoporosis medication during each quarter between January 1, 2009 and March 31, 2020. Of all individuals receiving therapy, we then calculated the percent receiving each medication. In subgroup analyses, we limited the population to (1) only those with ICD codes for osteoporosis in the current or previous three quarters and without codes for active malignancy during the current or previous quarter, and (2) only those with ICD codes from an oncology provider for active malignancy likely to metastasize to bone during the current or previous quarter. In the all-user cohort, a total of 15.48 million unique prescription or medication administration claims from 1.46 million unique individuals during the study period were analyzed. Of these, 89% were women and 71% over the age of 65, with a mean age of 69. Alendronate was the most common medication used, representing &gt;50% of all treated individuals, and its use increased over time. Percent of users receiving zoledronic acid doubled during this period but remained &lt;5%. Use of other bisphosphonates declined steadily. By comparison, after its approval in 2010, denosumab use increased rapidly, reaching 10% of users in 2015 and 15% of users in 2018. Percent of individuals treated with raloxifene declined after 2013. Use of teriparatide, abaloparatide, and romosozumab remained less than 2% throughout the study period. Trends in the osteoporosis cohort paralleled those in the all-user cohort. In the malignancy cohort, alendronate and zoledronic acid were each used in approximately 30% of individuals at the onset of the study, and both declined over the decade. By contrast, denosumab use rapidly increased after introduction and surpassed use of either bisphosphonate by 2013. Denosumab use continued to increase over time and reached approximately 50% of all bone-directed medication use in the malignancy cohort. Use of other medications, mainly alternate bisphosphonates, was low and declined throughout the study period. In a privately insured cohort between 2009–2020, denosumab use increased significantly in both osteoporosis and malignancy populations, outpacing gains in use of other agents, despite guidelines suggesting that either bisphosphonates or denosumab could be considered first-line therapy for osteoporosis.