SQL Server Reporting Services Research Articles

Analysis of subjective sleep quality and influencing factors of depression in ARDS patients

BackgroundAcute Respiratory Distress Syndrome (ARDS) is a severe lung disease, and its condition can lead to depressive disorders in patients. Understanding the subjective sleep quality of ARDS patients with depressive disorder conditions and their influencing factors is essential for developing effective interventions and improving patients’ sleep quality.Subjects and MethodsSixty patients with depression were selected as the study subjects and randomly divided into experimental and control groups, with 30 people in each group. Pittsburgh Sleep Quality Index (PSQI), Symptom Checklist-90 (SCL-90), Self-Rating Anxiety Scale (SAS), Social Desirability Scale (SDS), State-Trait Anxiety Inventory (STAI), Life Event Scale (LES), and SQL Server Reporting Services (SSRS) were used for assessment. The data collected were analyzed by the relationship between PSQI and the results of the other six rating scales using SPSS23.0.ResultsSleep disorders were found in 77.8% of 60 patients with depression. The total score and factors of PSQI were significantly correlated with SCL-90 somatization, obsessive-compulsive disorder, depression, anxiety, and total mean score (P<0.05). The PSQI was positively correlated with SAI in SAS and STAI (P<0.01) but not correlated with TAI in STAI (P>0.05). The somatization disorder factor in SDS was significantly correlated with the PSQI factors and total scores except for the sleep persistence factor (P<0.05). The daytime dysfunction factor in PSQI was significantly correlated with the full score of social support (P<0.05), whereas there was no significant correlation between PSQI and LES (P>0.05).ConclusionsDepressive disorders in ARDS patients hurt their subjective sleep quality.

Teaching Case: Learning Skills of the Data Analytics Lifecycle with Microsoft Power BI and National Parks Data

Like many organizations, the U.S. National Parks Service has done an excellent job of collecting and storing data for years about variables of interest. However, there are steps beyond the collection and storage of data that must be taken to transform that data into valuable information that can inform strategic business decisions. This teaching case introduces students to the data analytics lifecycle and the various processes for preparing data to make decisions. Students gain a familiarity with the leading data analytics software Microsoft Power BI as well as Microsoft Excel and SQL Server Reporting Services (SSRS) to inform data-driven decisions. This case is relevant to undergraduate and graduate students in information systems and other disciplines prioritizing data analytics. The case was not developed in conjunction with the National Parks Service; the case uses real-world data made publicly available by the National Parks Service but the narrative and questions posed are fictional.

Improving Service Quality Using Consumers’ Complaints Data Mart which Effect on Financial Customer Satisfaction

One of the best ways to enhance the performance of all companies and manage Customer Satisfaction is to get the consumers’ complaints and analyze them in order to fix them. These complaints represent the consumers’ behavior to the companies and how these company’s response to them. Besides, customers’ satisfaction is the main goal of all companies and this goal cannot achieve if they do not handle the customers’ complaints. The paper represents a framework of complaint data mart construction where the source data are thousands of complaints about services and financial products of companies. The data mart represents the first step to implement an enterprise data warehouse (DW) to support strategic decisions. Reports are constructed to help analysts and decision-makers to support their decisions related to consumers’ complaints and how to improve service quality. Two different categories of on-line analytical processing (OLAP) reports are used, offline and web OLAP reports. The two types of reports provide a deep view of the data and present the analysts with flexible charts that can be used in supporting strategic decisions. SQL Server Management Studio (SSMS), SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS), SQL Server Reporting Services (SSRS) 2014 beside SQL Server Data Tools (SSDT) 2013 is used to build the data mart staging table, schema, cube, and OLAP reports. MS Excel Pivot table 2010 is used also to import the cube and build offline reports and implementing OLAP processes. This data mart can be utilized by consumers themselves besides decision-makers and analysts. The data mart can measure how the companies fix complaints issues and prevent them from occurring again and identify the factors that influence financial customers’ satisfaction.

The Enhancing Quality of Prescribing Practices for Older Veterans Discharged From the Emergency Department (EQUIPPED) Potentially Inappropriate Medication Dashboard: A Suitable Alternative to the In-person Academic Detailing and Standardized Feedback Reports of Traditional EQUIPPED?

PurposeThe Enhancing Quality of Prescribing Practices for Older Veterans Discharged From the Emergency Department (EQUIPPED) program is a quality improvement initiative that combines education, clinical decision support (ie, geriatric pharmacy order sets), and in-person academic detailing coupled with audit and feedback in an effort to improve appropriate prescribing to older veterans discharged from the emergency department. Although the EQUIPPED program is effective at reducing the prescribing of potentially inappropriate medications, the reliance on in-person academic detailing may be a limitation for broader dissemination. The EQUIPPED dashboard is a passive yet continuous audit and feedback mechanism developed to potentially replace the in-person academic detailing of the traditional EQUIPPED program. We describe the development process of the EQUIPPED dashboard and the key audit and feedback components found within. MethodsThe Veterans Affairs (VA) Corporate Data Warehouse (CDW) serves as the underlying data source for the EQUIPPED dashboard. SQL Server Integration Services was used to build the backend data architecture. Data were isolated from the CDW for reporting purposes using an extract, load, transform (ELT) approach. The team used SQL Server Reporting Services to produce the user interface and add interactive functionality. The team used an agile development approach when designing the user interface, engaging end users at 2 VA EQUIPPED implementation sites by providing printed screenshots of a beta version of the dashboard. FindingsThe EQUIPPED dashboard ELT process executes nightly to provide dashboard end users with a near real-time data experience and the potential for daily audit and feedback. The following dashboard components were identified as necessary for the EQUIPPED dashboard to be a suitable audit and feedback tool: key performance indicators, peer-to-peer benchmarking, individual patient or encounter drill down, educational decision support, and longitudinal performance tracking. ImplicationsTo our knowledge, the EQUIPPED dashboard is the first information display of its kind with built-in audit and feedback that has been developed for VA emergency department practitioners as the primary end users. Further investigation is warranted to determine whether the EQUIPPED dashboard is a suitable alternative to in-person academic detailing. The EQUIPPED dashboard will be leveraged in a formal implementation trial that will entail the randomization of multiple VA sites to either (1) traditional EQUIPPED with in-person academic detailing coupled with audit and feedback or (2) EQUIPPED with passive audit and feedback delivered through the EQUIPPED dashboard without in-person prescribing outreach.

A business intelligence based solution to support academic affairs: case of Taibah University

Business intelligence (BI) is a set of technologies and strategies allowing analyzing data and providing information to help in the decision-making process. Today, BI is used in many sectors and shows marvelous results. In this paper, we are interested in supporting decision-making in higher education. BI concepts when integrated to the process of academic affairs can make significant improvement. The present paper aims at describing a BI solution to support academic affairs at Taibah University. Using BI, we can take advantages of a set of analytical tools that support decision-making for different types of users (students, faculty members, administrators and decision makers). The proposed BI solution consists essentially of three mains tasks: (1) collecting data from different sources using the three operations (Extract, Transform and Load), (2) proposing a multidimensional solution that describes the academic processes, and (3) visualizing results through a set of dashboards and reports. Experiments are made using the SQL Server Data Tools. Three steps are detailed which are: SQL Server Integration Services, SQL Server Analysis Services and SQL Server Reporting Services. The proposed solution provides many statistical and predictive indicators needed in academic tasks.

Optimization and analysis of information using business intelligence techniques and reporting using dashboards

In Today’s world data is collected at an unpredictable scale from various application areas. Prior to the arrival of Big Data, all the data that was generated was handled manually. With data being produced in the range of terabytes today, that is impossible. To make the situation worse, almost 80% of the data generated by organizations is unstructured. This means that it cannot be understood in its avail- able format. It is very difficult and risky to make decisions just based on such crude data. In order to make quick, yet correct decisions, the generated data has to be optimized. This Paper discusses to create an end-to-end system to optimize approximately 6 million records of unstructured data provided as .txt files, which is in the form of strings and numbers into understandable or structured data. The next step is to analyse the structured data in order to make calculations on the given dataset. Finally, the analysed data will be represented in the form of dashboards, which are tabular reports or charts. In this Paper, unstructured data in the form of .txt files will be transformed into structured data in the form of tables through the SQL stored procedures in SQL Server Management Studio (SSMS). Along with the data, four other tables called dimensions will be created and then all five tables will then be integrated using SQL Server Integrated Ser- vices. Then an Online Analytical Processing (OLAP) cube is built over this data with product, customer, currency and time as its dimen- sions using the SQL Server Analysis Services (SSAS). At last this analysed data is then reported through dashboards through SQL Server Reporting Services (SSRS).The results of the analysed data is viewed in the form of reports and charts. These reports are customizable and a variety of operations can be performed on them as required by an organization. Since these reports are short and informative, they will be easy to understand and will provide for easier and correct decision making.

The impacts of a pharmacist-managed outpatient clinic and chemotherapy-directed electronic order sets for monitoring oral chemotherapy.

Objectives Patients treated with oral chemotherapy appear to have less contact with the treating providers. As a result, safety, adherence, medication therapy monitoring, and timely follow-up may be compromised. The trend of treating cancer with oral chemotherapy agents is on the rise. However, standard clinical guidance is still lacking for prescribing, monitoring, patient education, and follow-up of patients on oral chemotherapy across the healthcare settings. The purpose of this project is to establish an oral chemotherapy monitoring clinic, to create drug and lab specific provider order sets for prescribing and lab monitoring, and ultimately to ensure safe and effective treatment of the veterans we serve. Methods A collaborative agreement was reached among oncology pharmacists, a pharmacy resident, two oncologists, and a physician assistant to establish a pharmacist-managed oral chemotherapy monitoring clinic at the VA Sierra Nevada Healthcare System. Drug-specific electronic order sets for prescribing and lab monitoring were created for initiating new drug therapy and prescription renewal. The order sets were created to be provider-centric, minimizing clicks needed to order necessary medications and lab monitoring. A standard progress note template was developed for documenting interventions made by the clinic. Patients new to an oral chemotherapy regimen were first counseled by an oncology pharmacist. The patients were then enrolled into the oral chemotherapy monitoring clinic for subsequent follow up and pharmacist interventions. Further, patients lacking monitoring or missing provider appointments were captured through a Clinical Dashboard developed by the US Department of Veterans Affairs (VA) Regional Office (VISN21) using SQL Server Reporting Services. Between September 2014 and April 2015, a total of 68 patients on different oral chemotherapy agents were enrolled into the clinic. Results Out of the 68 patients enrolled into the oral chemotherapy monitoring clinic, 31 patients (45%) were identified as having a therapy-related problem with their oral chemotherapy regimen on a gross measure for safety and appropriateness of medication management during the course of eight months follow-up between September 2014 and April 2015. In addition, the clinic helped to reestablish care for three patients (4.4%) who were lost to follow-up. The clinic identified 12 patients (17.6%) non-adherent to their prescribed regimen in some degree, where patients were suspected to miss doses due to delay in refilling prescriptions at least three days later than the expected date. However, these patients denied non-adherence. Among them, six patients (8.8%) were truly non-adherent. These patients stated that they had missed at least one day of therapy or were not taking the medication as prescribed. Medication regimen errors were discovered for five patients, accounting for a 7.3% medication-related error rate. Finally, seven patients (10.3%) were found to have an adverse reaction attributed to their oral chemotherapy. Two of them (2.9%) developed severe adverse reactions (Grade 3 and 4), which required hospitalization or immediate dose de-escalation. Conclusions The pilot clinic was able to identify current deficiencies and gaps in our practice settings for managing oral chemotherapy in a Veterans population. The oral chemotherapy monitoring clinic played a proactive role to identify preventable medication errors, monitor medication therapy, improve adherence, manage adverse drug reactions and re-establish care for patients who were lost to follow-up. The results suggest that close monitoring and follow-up of patients on oral chemotherapy is crucial to achieve therapeutic goals, improve patient safety and adherence, and to reduce drug adverse events and health care cost.

Use of ETL Subsystems for Real-Time Data-Warehouse using MS SQL Server Tool

Data warehousing is gaining in eminence as organizations become awake of the benefits of decision oriented and business intelligence oriented data bases. Informed decision-making is required for competitive success in the new global marketplace, which is fraught with uncertainty and rapid technology changes. Achieving Real-Time Data Warehousing is highly dependent on the choice of a process in data warehousing technology known as Extract, Transform, and Load (ETL). This process involves: 1) Extracting data from outside sources; 2) Transforming it to fit operational needs; and 3) Loading it into the end target (database or data warehouse). Not all ETL’s are equal when it comes to quality and performance. As such, optimizing the ETL processes for real time decision making is becoming ever increasingly crucial to today's decision-making process. An effective ETL leads to effective business decisions and yields extraordinary decision-making outcomes. This study overviews the theory behind ETL and raises a research vision for its evolution, with the aim of improving the difficult but necessary data management work required for the development of advanced analytics and business intelligence. SQL Server Integration services (SSIS-MS Sql server tool) is a platform for data integration and workflow applications. It features a fast and flexible data-warehousing tool used for data extraction, transformation, and loading (ETL). The tool may also be used to automate maintenance of SQL Server databases and updates to multidimensional cube data. SQL Server Reporting Services (SSRS- MS Sql server tool) is a server-based report generation software system from Microsoft. Administered via a web interface, it can be used to prepare and deliver a variety of interactive and printed reports.

Business Intelligence, Analytics And Data Visualization: A Heat Map Project Tutorial

Business intelligence and analytics (BI&A) initiatives are helping countless organizations harness and interpret the vast amount of information available in the world today. The explosion of BI&A in industry has fueled the high demand for knowledge workers with advanced analytical skills. The purpose of this paper is to introduce a data visualization project tutorial for Information Systems (IS) education. The applied BI&A tutorial was designed to help students learn how to create and analyze a heat map using SQL Server Data Tools (SSDT) and SQL Server Reporting Services (SSRS). Students learn how to make decisions based on large amounts of data by presenting it in visual form. This tutorial exposes students to the decision-making power derived from data visualization. Utilizing the 5E Instructional Model, the tutorial assists in the development of BI&A professionals who can quickly make sense of mass amounts of data, identify trends buried within data sets, and are skilled in making sound decisions that add value to organizations.

Combining Business Intelligence And Stock Market Data: A Primer For Data Analytics And Business Intelligence

<span style="font-family: Times New Roman; font-size: small;"> </span><p style="margin: 0in 0.5in 0pt; text-align: justify; mso-pagination: none; mso-layout-grid-align: none;" class="MsoNormal"><span style="font-size: 10pt; mso-bidi-font-style: italic;"><span style="font-family: Times New Roman;">Business Intelligence (BI) has become indispensible to modern business decision-making. Organizations rely on BI to interpret the mass amounts of data circulating throughout the world. However, integration of BI into university business programs does not parallel industry demands. The purpose of this paper is to introduce an innovative business intelligence project tutorial for Information Systems (IS) education. The applied tutorial was designed to help students learn how to design and publish a report using SQL Server Reporting Services to analyze current stock market data. This tutorial exposes students to the decision-making power derived from raw data analysis and assists in development of business professionals who can maximize profitability through effective use of business intelligence.</span></span></p><span style="font-family: Times New Roman; font-size: small;"> </span>