scholarly journals HOSPITAL PHARMACY MANAGEMENT SYSTEM

2018 ◽  
Vol 44 (2) ◽  
pp. 27-34
Author(s):  
Saja Khudhur
Keyword(s):  
Data Management ◽  
Graphical User Interface ◽  
Hospital Pharmacy ◽  
Management System ◽  
Sql Server ◽  
Data Management System ◽  
Management Systems ◽  
Electronic Technology ◽  
Data Management Systems ◽  
Pharmacy System

Generally, the electronic technology has been implemented to automate the traditional systems. So, differentcopy of management systems in different scope were presented. These systems include the services provided to company as wellas people, such as, healthcare. The traditional data management systems for pharmacy as example, suffer fromthe capacity, time consuming, medicines accessibility, managing the medicines store as well as the need of qualifiedstaff according to the requirements of employer expectations. In this paper, a hospital e-pharmacy system is proposed in order to facilitate the job, outdo the mentioned problems. A data management system to the Iraqi hospital's pharmacy is proposed which is divided into two main parts: database, and Graphical User Interface (GUI) frames. The database built using SQL Server contains the pharmacy information relatedto the medicines, patient information….etc. the GUI frames ease the use of the proposed system by unskilled users. Theproposal system is responsible on monitoring and controlling the work of pharmacy in hospital in terms of management ofmedicine issuing ordering and hospital reports.

scholarly journals A Coding-Free Software Framework for Developing Lightweight Web Data Management Systems

2020 ◽  
Vol 10 (3) ◽  
pp. 865
Author(s):  
Can Yang ◽  
Shiying Pan ◽  
Runmin Li ◽  
Yu Liu ◽  
Lizhang Peng
Keyword(s):  
Data Management ◽  
Management System ◽  
System Development ◽  
Data Management System ◽  
Management Systems ◽  
Web Data ◽  
Data Management Systems ◽  
Construction Methods ◽  
Free Construction ◽  
Web Data Management

Increasingly more enterprises are intending to deploy data management systems in the cloud. However, the complexity of software development significantly increases both time and learning costs of data management system development. In this paper, we investigate the coding-free construction of a data management system based on Software-as-a-Service (SaaS) architecture, in which a practical application platform and a set of construction methods are proposed. Specifically, by extracting the common features of data management systems, we design a universal web platform to quickly generate and publish customized system instances. Then, we propose a method to develop a lightweight data management system using a specific requirements table in a spreadsheet. The corresponding platform maps the requirements table into a system instance by parsing the table model and implementing the objective system in the running stage. Finally, we implement the proposed framework and deploy it on the web. The empirical results demonstrate the feasibility and availability of the coding-free method for developing lightweight web data management systems.

scholarly journals THESE MODELS NEED ENTERPRISE DATA MANAGEMENT!

10.28945/3651 ◽  
2017 ◽  
Vol 6 ◽  
pp. 01
Author(s):  
Jay Hoecker ◽  
Debbie Bernal ◽  
Alex Brito ◽  
Arda Ergonen ◽  
Richard Stiftinger
Keyword(s):  
Life Cycle ◽  
Data Management ◽  
Management System ◽  
Current Data ◽  
Scientific Models ◽  
Data Management System ◽  
Management Systems ◽  
Data Management Systems ◽  
Technology Platform

The current data management systems for the life cycle of scientific models needed an upgrade. What technology platform offered the best option for an Enterprise Data Management system?

scholarly journals Requirements of Health Data Management Systems for Biomedical Care and Research: Scoping Review (Preprint)

2019 ◽  
Author(s):  
Leila Ismail ◽  
Huned Materwala ◽  
Achim P Karduck ◽  
Abdu Adem
Keyword(s):  
Big Data ◽  
Data Management ◽  
Real Time ◽  
Management System ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Data ◽  
Data Management System ◽  
Management Systems ◽  
Data Management Systems

BACKGROUND Over the last century, disruptive incidents in the fields of clinical and biomedical research have yielded a tremendous change in health data management systems. This is due to a number of breakthroughs in the medical field and the need for big data analytics and the Internet of Things (IoT) to be incorporated in a real-time smart health information management system. In addition, the requirements of patient care have evolved over time, allowing for more accurate prognoses and diagnoses. In this paper, we discuss the temporal evolution of health data management systems and capture the requirements that led to the development of a given system over a certain period of time. Consequently, we provide insights into those systems and give suggestions and research directions on how they can be improved for a better health care system. OBJECTIVE This study aimed to show that there is a need for a secure and efficient health data management system that will allow physicians and patients to update decentralized medical records and to analyze the medical data for supporting more precise diagnoses, prognoses, and public insights. Limitations of existing health data management systems were analyzed. METHODS To study the evolution and requirements of health data management systems over the years, a search was conducted to obtain research articles and information on medical lawsuits, health regulations, and acts. These materials were obtained from the Institute of Electrical and Electronics Engineers, the Association for Computing Machinery, Elsevier, MEDLINE, PubMed, Scopus, and Web of Science databases. RESULTS Health data management systems have undergone a disruptive transformation over the years from paper to computer, web, cloud, IoT, big data analytics, and finally to blockchain. The requirements of a health data management system revealed from the evolving definitions of medical records and their management are (1) medical record data, (2) real-time data access, (3) patient participation, (4) data sharing, (5) data security, (6) patient identity privacy, and (7) public insights. This paper reviewed health data management systems based on these 7 requirements across studies conducted over the years. To our knowledge, this is the first analysis of the temporal evolution of health data management systems giving insights into the system requirements for better health care. CONCLUSIONS There is a need for a comprehensive real-time health data management system that allows physicians, patients, and external users to input their medical and lifestyle data into the system. The incorporation of big data analytics will aid in better prognosis or diagnosis of the diseases and the prediction of diseases. The prediction results will help in the development of an effective prevention plan.

Approach for a Data-Managmenet-System and a Proceeding-Model for the Development of Adaptronic Systems

2010 ◽  
Author(s):  
Roland Nattermann ◽  
Reiner Anderl
Keyword(s):  
Data Management ◽  
Simulation Model ◽  
Management System ◽  
Model Simulation ◽  
Integrated Design ◽  
System Model ◽  
Data Management System ◽  
Management Systems ◽  
Mechatronic Systems ◽  
Data Management Systems

Existing proceeding-models for the development of mechatronic systems intend a mostly independent development of single components, like the mechanic structure, the electronics, etc., starting from a combined over-all-model. Following the understanding of adaptronics as an advancement of mechatronics in the LOEWE-Center AdRIA (AdRIA – Adaptronic - Research, Innovation and Application), a clear division of the development is not advisable, because of the high structural integration of adaptronic systems. Because of this, it’s necessary to develop the whole system by using a permanent alignment of values between the single components. This high grade of data transfer and the high number of relations between the components lead to a complexity that can only be handled by the use of a Data-Management-system. An approach for a Data-Management-System for the development of adaptronic systems by the Department of Computer Integrated Design, as part of the LOEWE-Center AdRIA, intends to extend the functionality of existing Product-Data-Management-Systems. The idea is to model the over-all system in the Data-Management-System at first, using the partitioning of the system into the five elements of active structures: excitations, structural components, actuator systems, sensor systems and signal processing. Furthermore the characteristic parameters of single components and the correlations between these parameters are captured. In addition the requirements of the adaptronic systems are captured and deposited in the DataManagement-System (DM-System). An integration-layer is used, to integrate the data and models of the different disciplines to the DM-System and to the generated over-all model, during the development of the adaptronic system. The database of the DM-System contains a standardized over-all-simulation model of the adaptronic system, which uses the same partitioning of the system to the different elements as the overall-system model. The consistent structure of the system-model, simulation-model and integration-layer is used for an automated over-all-simulation of the adaptronic system. Using the automated over-all-simulation changes in the adaptronic-system-behavior can be calculated, when changes in single components become available. Using the deposited requirements, these changes can also be valued. An analysis of existing proceeding-models for the development of mechatronic systems shows, that they are only partly suitable for use in the given approach. Therefore a new proceeding model was developed as an advancement of existing models. The new model shows an equitable solution for the development of adaptronic structures by using Data-Management-Systems.

scholarly journals Requirements of Health Data Management Systems for Biomedical Care and Research: Scoping Review

10.2196/17508 ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. e17508
Author(s):  
Leila Ismail ◽  
Huned Materwala ◽  
Achim P Karduck ◽  
Abdu Adem
Keyword(s):  
Big Data ◽  
Data Management ◽  
Real Time ◽  
Management System ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Data ◽  
Data Management System ◽  
Management Systems ◽  
Data Management Systems

Background Over the last century, disruptive incidents in the fields of clinical and biomedical research have yielded a tremendous change in health data management systems. This is due to a number of breakthroughs in the medical field and the need for big data analytics and the Internet of Things (IoT) to be incorporated in a real-time smart health information management system. In addition, the requirements of patient care have evolved over time, allowing for more accurate prognoses and diagnoses. In this paper, we discuss the temporal evolution of health data management systems and capture the requirements that led to the development of a given system over a certain period of time. Consequently, we provide insights into those systems and give suggestions and research directions on how they can be improved for a better health care system. Objective This study aimed to show that there is a need for a secure and efficient health data management system that will allow physicians and patients to update decentralized medical records and to analyze the medical data for supporting more precise diagnoses, prognoses, and public insights. Limitations of existing health data management systems were analyzed. Methods To study the evolution and requirements of health data management systems over the years, a search was conducted to obtain research articles and information on medical lawsuits, health regulations, and acts. These materials were obtained from the Institute of Electrical and Electronics Engineers, the Association for Computing Machinery, Elsevier, MEDLINE, PubMed, Scopus, and Web of Science databases. Results Health data management systems have undergone a disruptive transformation over the years from paper to computer, web, cloud, IoT, big data analytics, and finally to blockchain. The requirements of a health data management system revealed from the evolving definitions of medical records and their management are (1) medical record data, (2) real-time data access, (3) patient participation, (4) data sharing, (5) data security, (6) patient identity privacy, and (7) public insights. This paper reviewed health data management systems based on these 7 requirements across studies conducted over the years. To our knowledge, this is the first analysis of the temporal evolution of health data management systems giving insights into the system requirements for better health care. Conclusions There is a need for a comprehensive real-time health data management system that allows physicians, patients, and external users to input their medical and lifestyle data into the system. The incorporation of big data analytics will aid in better prognosis or diagnosis of the diseases and the prediction of diseases. The prediction results will help in the development of an effective prevention plan.

Clinical Trial Data Management Software: A Review of the Technical Features

2019 ◽  
Vol 14 (3) ◽  
pp. 160-172 ◽  
Author(s):  
Aynaz Nourani ◽  
Haleh Ayatollahi ◽  
Masoud Solaymani Dodaran
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Data Management ◽  
Clinical Data ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Data Management System ◽  
Management Systems ◽  
Data Management Systems ◽  
Technical Features

Background:Data management is an important, complex and multidimensional process in clinical trials. The execution of this process is very difficult and expensive without the use of information technology. A clinical data management system is software that is vastly used for managing the data generated in clinical trials. The objective of this study was to review the technical features of clinical trial data management systems.Methods:Related articles were identified by searching databases, such as Web of Science, Scopus, Science Direct, ProQuest, Ovid and PubMed. All of the research papers related to clinical data management systems which were published between 2007 and 2017 (n=19) were included in the study.Results:Most of the clinical data management systems were web-based systems developed based on the needs of a specific clinical trial in the shortest possible time. The SQL Server and MySQL databases were used in the development of the systems. These systems did not fully support the process of clinical data management. In addition, most of the systems lacked flexibility and extensibility for system development.Conclusion:It seems that most of the systems used in the research centers were weak in terms of supporting the process of data management and managing clinical trial's workflow. Therefore, more attention should be paid to design a more complete, usable, and high quality data management system for clinical trials. More studies are suggested to identify the features of the successful systems used in clinical trials.

A Review of Clinical Data Management Systems Used in Clinical Trials

2019 ◽  
Vol 14 (1) ◽  
pp. 10-23 ◽  
Author(s):  
Aynaz Nourani ◽  
Haleh Ayatollahi ◽  
Masoud Solaymani Dodaran
Keyword(s):  
Clinical Trials ◽  
Data Management ◽  
Clinical Data ◽  
Data Management System ◽  
Management Systems ◽  
Management Process ◽  
Data Management Systems ◽  
Ovid Medline ◽  
Research Findings ◽  
Accuracy Of Results

Background:A clinical data management system is a software supporting the data management process in clinical trials. In this system, the effective support of clinical data management dimensions leads to the increased accuracy of results and prevention of diversion in clinical trials. The aim of this review article was to investigate the dimensions of data management in clinical data management systems.Methods:This study was conducted in 2017. The used databases included Web of Science, Scopus, Science Direct, ProQuest, Ovid Medline and PubMed. The search was conducted over a period of 10 years from 2007 to 2017. The initial number of studies was 101 reaching 19 in the final stage. The final studies were described and compared in terms of the year, country and dimensions of the clinical data management process in clinical trials.Results:The research findings indicated that none of the systems completely supported the data management dimensions in clinical trials. Although these systems were developed for supporting the clinical data management process, they were similar to electronic data capture systems in many cases. The most significant dimensions of data management in such systems were data collection or entry, report, validation, and security maintenance.Conclusion:Seemingly, not sufficient attention has been paid to automate all dimensions of the clinical data management process in clinical trials. However, these systems could take positive steps towards changing the manual processes of clinical data management to electronic processes.

scholarly journals Data management for early hearing detection and intervention in South Africa

2017 ◽  
Vol 19 (1) ◽  
Author(s):  
Selvarani Moodley ◽  
Claudine Storbeck
Keyword(s):  
South Africa ◽  
Data Management ◽  
Quality Care ◽  
Data Management System ◽  
Management Systems ◽  
International Studies ◽  
Public And Private ◽  
Developed Country ◽  
Data Management Systems ◽  
Newborn Hearing

Introduction: Internationally, newborn hearing screening is becoming part of standard neonatal healthcare service guidelines for the implementation of early hearing detection and intervention (EHDI) initiatives, including screening, diagnosis, data management and intervention. Data management includes the processes of data collection and storage thereof, as well as analysis and interpretation of data to guide the future planning, implementation and evaluation of EHDI programmes. There have been limited studies on data management in the South African EHDI context.Methods: The aim of this study was to determine the type of data management systems in use in South Africa and whether they allow for cross-disciplinary sharing and evaluation of the EHDI processes. A survey instrument on the management of EHDI data was developed and sent to HI HOPES referral agents in both public and private sectors.Results: A return rate of 80% was achieved, with 19 (59%) public sector and 13 (41%) private sector audiologists participating in the study. The data revealed that there was no uniform data management system in use nationally, and no consistent shared system within the public or private sectors. The majority of respondents (44%) used a paper-based system for data recording. No institutions were using data management systems that enabled sharing of information with other medical professionals.Conclusion: Data management and tracking of the pathway from screening to diagnosis to intervention is necessary to ensure quality care and outcomes for children identified with hearing loss. International studies reveal the importance of effective implementation of data management systems; however, to date these have focussed on developed country contexts. Data management challenges identified in this study reflect international challenges as well as challenges unique to a developing country context.

Research on Planning Approval Data Interoperation Implementation between AutoCAD and ArcSDE

2012 ◽  
Vol 263-266 ◽  
pp. 1897-1901
Author(s):  
Yan Hong Zou ◽  
Wen Feng Xi
Keyword(s):  
Data Management ◽  
Relational Database ◽  
Management System ◽  
Database Management ◽  
Sql Server ◽  
Data Management System ◽  
Data Format ◽  
Load Planning ◽  
Attribute Data

In planning approval field, AutoCAD data format is used widely, for it is convenient to map.However, for massive planning approval data, it is difficult to query the corresponding spatial and attribute characteristics by AutoCAD files. While ArcSDE can unifiedly manange spatial and attribute data, whose strong database management is inherited from relational database. To address planning approval field interoperation problem between AutoCAD and ArcSDE in planning approval data management system, we took redevelopment from AutoCAD to ArcSDE database based on C#.Net and Object SDK. The data could be operated by Down-Load and Up-Load module in the system. Down-Load module was developed to load planning data from ArcSDE to AutoCAD, Up-Load module was to store AutoCAD format data into ArcSDE.As a case study,the modules were integrated into the Planning Approval Data Management System for Yueyang city and the data interoperation was very fast and easy between AutoCAD and ArcSDE+SQL Server 2008. The results show that the implemented interoperation function is convenient for planning approval data management.

scholarly journals Ontology-Based Clustering in a Peer Data Management System

2012 ◽  
Vol 3 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Carlos Eduardo Santos Pires ◽  
Rocir Marcos Leite Santiago ◽  
Ana Carolina Salgado ◽  
Zoubida Kedad ◽  
Mokrane Bouzeghoub
Keyword(s):  
Data Management ◽  
Data Management System ◽  
Management Systems ◽  
Challenging Problem ◽  
Data Management Systems ◽  
Stage Of Development ◽  
Data Source ◽  
Peer Data Management ◽  
P2p Applications ◽  
Current Stage

Peer Data Management Systems (PDMSs) are advanced P2P applications in which each peer represents an autonomous data source making available an exported schema to be shared with other peers. Query answering in PDMSs can be improved if peers are efficiently disposed in the overlay network according to the similarity of their content. The set of peers can be partitioned into clusters, so as the semantic similarity among the peers participating into the same cluster is maximal. The creation and maintenance of clusters is a challenging problem in the current stage of development of PDMSs. This work proposes an incremental peer clustering process. The authors present a PDMS architecture designed to facilitate the connection of new peers according to their exported schema described by an ontology. The authors propose a clustering process and the underlying algorithm. The authors present and discuss some experimental results on peer clustering using the approach.

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