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

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

Cancer Epidemiol Biomarkers Prev

Background:Large-scale cancer epidemiology cohorts (CECs) have successfully collected, analyzed, and shared patient-reported data for years. CECs increasingly need to make their data more Findable, Accessible, Interoperable, and Reusable, or FAIR. How CECs should approach this transformation is unclear.Methods:The California Teachers Study (CTS) is an observational CEC of 133,477 participants followed since 1995\u20131996. In 2014, we began updating our data storage, management, analysis, and sharing strategy. With the San Diego Supercomputer Center, we deployed a new infrastructure based on a Data Warehouse, to integrate and manage data; and a secure and shared workspace with documentation, software, and analytic tools that facilitate collaboration and accelerate analyses.Results:Our new CTS infrastructure includes a Data Warehouse and data marts, which are focused subsets from the Data Warehouse designed for efficiency. The secure CTS workspace utilizes a Remote Desktop service that operates within a HIPAA and FISMA compliant platform. Our infrastructure offers broad access to CTS data; includes statistical analysis and data visualization software and tools; flexibly manages other key data activities (e.g., cleaning, updates, & data sharing); and will continue to evolve to advance FAIR principles.Conclusion:Our scalable infrastructure provides the security, authorization, data model, metadata, and analytic tools needed to manage, share, and analyze CTS data in ways that are consistent with the NCI\u2019s Cancer Research Data Commons Framework.Impact:The CTS\u2019s implementation of new infrastructure in an ongoing CEC demonstrates how population sciences can explore and embrace new cloud-based and analytics infrastructure to accelerate cancer research and translation.HHSN261201800032C/CA/NCI NIH HHSUnited States/HHSN261201800009C/CA/NCI NIH HHSUnited States/NU58DP006344/DP/NCCDPHP CDC HHSUnited States/HHSN261201800015I/CA/NCI NIH HHSUnited States/HHSN261201800032I/CA/NCI NIH HHSUnited States/P30 CA033572/CA/NCI NIH HHSUnited States/HHSN261201800015C/CA/NCI NIH HHSUnited States/HHSN261201800009I/CA/NCI NIH HHSUnited States/U01 CA199277/CA/NCI NIH HHSUnited States/UM1 CA164917/CA/NCI NIH HHSUnited States/P30 CA023100/CA/NCI NIH HHSUnited States/R01 CA077398/CA/NCI NIH HHSUnited States

Usability analysis of contending electronic health record systems

In this paper, we report measured usability of two leading EHR systems during procurement. A total of 18 users participated in paired-usability testing of three scenarios: ordering and managing medications by an outpatient physician, medicine administration by an inpatient nurse and scheduling of appointments by nursing staff. Data for audio, screen capture, satisfaction rating, task success and errors made was collected during testing. We found a clear difference between the systems for percentage of successfully completed tasks, two different satisfaction measures and perceived learnability when looking at the results over all scenarios. We conclude that usability should be evaluated during procurement and the difference in usability between systems could be revealed even with fewer measures than were used in our study. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe