Author(s):  
Veerarsak Likhitruangsilp ◽  
Hang T. T. Le ◽  
Nobuyoshi Yabuki ◽  
Photios G. Ioannou

Life-cycle analysis (LCCA) has become a necessary tool for green procurement for construction projects in many countries. Calculating life-cycle costs (LCCs) requires a variety of data that need to be gathered from diverse sources throughout the project life span. This information is usually stored in paper-based documents and are not well organized. These practices cause poor-quality data, which lead to incorrect results. Consequently, current LCCA in practice is extremely challenging. Data management is a major component for executing this sustainable development concept. This paper develops a relational database management system (RDBMS) that can support in calculating the LCCs of building projects. The system is structured to manage a large volume of design and construction data in a multi-parametric form. This allows users to integrate the proposed system with other modern construction platforms, especially building information modeling (BIM). In this paper, Autodesk Revit, a most widely-used BIM software, is adopted for authoring BIM models of a building and estimating relevant costs. Microsoft Access is used for developing a database management system (DBMS), which is designed to collaborate with the BIM models for LCCA. The system can significantly expedite the LCCA for a building with minimal errors and mistakes in data management and accurate LCCs.


2021 ◽  
pp. 47-78
Author(s):  
Jagdish Chandra Patni ◽  
Hitesh Kumar Sharma ◽  
Ravi Tomar ◽  
Avita Katal

2017 ◽  
pp. 1-6
Author(s):  
Richard Mansour ◽  
Samip Master

Purpose Quality measurement and improvement is a focus of ASCO. In the era of electronic health records (EHRs), computerized order entry, and medication administration records, quality monitoring can be an automated process. The EHR data are usually stored within tables in a relational database management system. ASCO Quality Oncology Practice Initiative measure NHL78a (hepatitis B virus antigen test and hepatitis B core antibody test within 3 months before initiation of obinutuzumab, ofatumumab, or rituximab for patients with non-Hodgkin lymphoma) presents an opportunity for automation of a quality measure using existing data in the EHR. Methods We used a locally developed Structured Query Language (SQL) language procedure in the Microsoft SQL Query Manager to access the EPIC CLARITY database. Access to the relational database management system of the EHR permits rapid case identification (the denominator set) of the unique ID of all of the patients who have received one of the target medications (ie, obinutuzumab, ofatumumab, or rituximab). Then, we went through a six-step process to find the number of patients who passed or failed the quality measure. Results When the final SQL procedure executes, it takes < 5 seconds to see the result set for a 12-month period. The procedure can be changed to incorporate a desired date range. Once the SQL procedure is created, there is essentially no labor and low costs to run the procedure at specific time intervals. Conclusion Our method of quality measurement using EHRs is cost effective, fast, and precise, and can be reproduced at other centers.


Sign in / Sign up

Export Citation Format

Share Document