SearcharxivSearch

arXiv · 1405.4597

Recognition and Ranking Critical Success Factors of Business Intelligence in Hospitals -- Case Study: Hasheminejad Hospital

Abstract

Business Intelligence, not as a tool of a product but as a new approach is propounded in organizations to make tough decisions in business as shortly as possible. Hospital managers often need business intelligence in their fiscal, operational, and clinical reports and indices. The main goal of recognition and ranking CSF is implementation of a business intelligent system in hospitals to increase success factor of application of business intelligence in health and treatment sector. This paper is an application and descriptive-analytical one, in which we use questionnaires to gather data and we used SPSS and LISREL to analyze them. Its statistical society is managers and personnel of Hasheminejad hospital and case studies are selected by Cochran formula. The findings show that all three organizational, process, and technological factors equally affect implementation of business intelligence based on Yeoh & Koronis approach, where the assumptions are based upon it. The proposed model for CSFs of business intelligence in hospitals include: declaring perspective, goals and strategies, development of human and financial resources, clarification of organizational culture, documentation and process mature, management support, etc. Business intelligence implementation is affected by different components. Center of Hasheminejad hospital BI system as a leader in providing quality health care, partially succeeded to take advantage of the benefits the organization in passing the information revolution but the development of this system to achieve intelligent hospital and its certainty is a high priority, thus it can`t be said that the hospital-wide BI system is quite favorable. In this regard, it can be concluded that Hasheminejad hospital requires practical model for business intelligence systems development.

Explore related subjects

Keep this discovery

BibTeXRIS

Marjan Naderinejad, Mohammad Jafar Tarokh, Alireza Poorebrahimi. 2014-05-19. Recognition and Ranking Critical Success Factors of Business Intelligence in Hospitals -- Case Study: Hasheminejad Hospital. https://doi.org/10.5121/ijcsit.2014.6208

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Circular Economy Synergies and Trade-offs in Data Centres

This report analyses data centre (DC) sustainability and circularity, revealing existing synergies and trade-offs: The PUE is too coarse, mixing cooling and power provisioning. It wrongly attributes server fan consumption and transformation losses to IT energy. It does not measure compute but infrastructure efficiency, which is already outstanding. Compute energy, however, is exploding. Better energy metrics for DCs would thus cover i) compute efficiency, ii) transformation efficiency, and iii) cooling overhead. Trade-offs exist between cooling energy and water as well as on-site and upstream water: Consuming water on-site lowers the cooling energy, which also lowers the water consumed upstream in power generation. For 'wet' electricity, there is little competition: It is worth spending more on-site energy to save both electricity and related upstream water. For 'dry' electricity, there is a trade-off. Waste heat recovery brings energy circularity but has limited uses and is not the same energy quality, a fact not reflected by current metrics. A better metric would consider the avoided energy through heat recovery instead of the amount recovered. Material circularity can be achieved by interpreting the 9R framework in the context of DCs. Circularity-enhancing measures can be categorised into product design, process design and business models, choice of materials, and operating conditions. Together, they have effects across all circularity levels. The relation between DCs and the power grid is complex. Modern DCs present new challenges for the grid. Mitigation includes battery storage and onsite generation. These measures have, in turn, further consequences, both beneficial and detrimental. They can offer grid flexibility as well as innovations in the field of energy. But they also bring noise, pollution, and GHGs, and compete with the energy sector for resources.

cs.OH

Digital Twin Modeling of a Highly Automated Agricultural Tractor

In efforts to increase research efficiency and availability, a digital twin of our research tractor (AMX G-trac) is created, focusing especially on the CAN communication for data reading and actuation command following the ISOBUS protocol. Mevea Simulation Software is utilized as the foundation, providing the kinematic model and visuals, while Python is used to read and write CAN messages over a Kvaser CanKing virtual CAN channel. Various performance tests involving straight line and turning behavior are performed in both the digital twin simulation and in the real world to measure similarity. Results indicate that the Mevea model behaves very comparable in its lateral dynamics, often within 5-10 percent, but requires better data to fully capture the longitudinal aspects like acceleration. The final model described in this paper sets the table for a second iteration to include more tractor functions such as hydraulics and tractor-implement dynamics.

cs.OH

Risk-based Design for Sustainability in Cloud Systems: Insights from an Experts' Survey

Cloud Systems' Sustainability is critical in Cloud Computing, especially with the growing demand in many industries. Sustainability risks in Cloud Computing can be tricky, mostly because of system complexity and their impact on performance. Thus, this research focuses on Risk-based design (RBD) and how it can support the early identification of possible risks for Cloud System Sustainability, as well as respective mitigation strategies of each risk. In order to successfully identify risks of Cloud System Sustainability, an Expert Survey is conducted including experts with different roles from different industries, to identify possible sustainability risks of a Cloud System on all different levels. Thematic analysis of the responses resulted in a categorization of risks, as well as in the identification of mitigation strategies and factors affecting each risk. Such findings can be helpful for researchers and practitioners that utilize RBD when building sustainable Cloud systems.

cs.OH