A Perinatal Database Management System

1981 ◽  
Vol 20 (03) ◽  
pp. 133-141 ◽  
Author(s):  
R. Kooi ◽  
S. Pillay ◽  
J. Hirsch ◽  
I. Zador ◽  
L. Chik ◽  
...  
Keyword(s):  
Management System ◽  
Database Management ◽  
Database Management System ◽  
Research Activity ◽  
Obstetric Services ◽  
Computer Based ◽  
Relational Database Management ◽  
Ongoing Evaluation ◽  
Answering Questions ◽  
Perinatal Database

Obstetric services have long recognized the need for ongoing evaluation of their experiences. Manual »departmental statistics« systems sufficed, but with the advent of more sophisticated care, perinatal re-gionalization and increased research activity, the potential usefulness of computer technology became obvious. At Cleveland Metropolitan General Hospital, a laboratory computer based patient information file system was designed and implemented beginning in 1974. Over the succeeding six years, data have been collected and stored for all delivered pregnancies. There are now over 61,000 files for more than 20,000 consecutively delivered patients. The system provides over 40,000 clinical reports per year. However, the use of a file-oriented system has limited our ability to respond to specific research queries. The application of a relational database management system, INGRES, for perinatal information is reported here. Examples of its use for efficiently »answering questions« are presented, as are guidelines for the development and implementation of computer-based perinatal record systems.

A Perinatal Database Management System for Ambulatory Care

1982 ◽  
Vol 61 (s109) ◽  
pp. 40-41
Author(s):  
W. E. Hammond ◽  
William W. Stead ◽  
Mark J. Straube ◽  
Frederick R. Jelovsek
Keyword(s):  
Ambulatory Care ◽  
Management System ◽  
Database Management ◽  
Database Management System ◽  
Perinatal Database

Development of a Relational Accident Database Management System for Mexican Federal Roads

2000 ◽  
Vol 1717 (1) ◽  
pp. 84-93
Author(s):  
Alberto Mendoza ◽  
Aristóteles Uribe ◽  
Claudia Z. Gil ◽  
Emilio Mayoral
Keyword(s):  
Geographic Information Systems ◽  
Management System ◽  
Database Management ◽  
Road Network ◽  
Data Exchange ◽  
Information Sources ◽  
Database Management System ◽  
The Internet ◽  
Computer Based ◽  
The Ideal

Two years ago, the Mexican Transportation Institute began to develop a computer-based management system of the information collected by various organizations about accidents occurring on the Federal Road Network. This system combines the information gathered by these organizations with the purpose of completing and validating the data so that tools can be developed for processing and analyzing the validated data and the processed data and developed tools can be made available to users. It was decided to support the development of such efforts on computer databases already being generated, on database processing and management software, on geographic information systems, and on remote data-exchange systems (e.g., the Internet). The progress made so far in the development of the computer system is reviewed. The system has been named the “Relational Accident Database Management System for Mexican Federal Roads” (SAIACF, in Spanish). The information sources beneficial to this project are identified and analyzed. The ideal scheme conceived for the integration of the various information sources is presented, and the SAIACF system is outlined. Some of the results obtained after its application to the information corresponding to 1997 are shown. Also, the element that was generated to make the information and the tools available to users is described, and conclusions are drawn.

MonogDB

2017 ◽  
pp. 64-91
Author(s):  
Sonali Tidke
Keyword(s):  
Management System ◽  
Database Management ◽  
Query Language ◽  
Database Management System ◽  
Management Model ◽  
Nosql Databases ◽  
Performance Indexes ◽  
And Behavior ◽  
Relational Database Management ◽  
Partitioning Technique

MongoDB is a NoSQL type of database management system which does not adhere to the commonly used relational database management model. MongoDB is used for horizontal scaling across a large number of servers which may have tens, hundreds or even thousands of servers. This horizontal scaling is performed using sharding. Sharding is a database partitioning technique which partitions large database into smaller parts which are easy to manage and faster to access. There are hundreds of NoSQL databases available in the market. But each NoSQL product is different in terms of features, implementations and behavior. NoSQL and RDBMS solve different set of problems and have different requirements. MongoDB has a powerful query language which extends SQL to JSON enabling developers to take benefit of power of SQL and flexibility of JSON. Along with support for select/from/where type of queries, MongoDB supports aggregation, sorting, joins as well as nested array and collections. To improve query performance, indexes and many more features are also available.

Relational Database Management System

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

Automating Quality Oncology Practice Initiative Quality Measure NHL78a

2017 ◽  
pp. 1-6
Author(s):  
Richard Mansour ◽  
Samip Master
Keyword(s):  
Hepatitis B ◽  
Relational Database ◽  
Management System ◽  
Database Management ◽  
Quality Measurement ◽  
Database Management System ◽  
Quality Measure ◽  
Relational Database Management System ◽  
Oncology Practice ◽  
Relational Database Management

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