scholarly journals Factors Influencing Data Management Models Selection

2020 ◽  
Author(s):  
Gholam Shaykhian ◽  
Mohd Khairi
Keyword(s):  
Data Management ◽  
Management Models ◽  
Factors Influencing

Exploring Factors Influencing Big Data and Analytics Adoption in Healthcare Management

2020 ◽  
pp. 413-428
Author(s):  
Sampson Abeeku Edu ◽  
Divine Q. Agozie
Keyword(s):  
Big Data ◽  
Data Management ◽  
Patient Management ◽  
Technological Development ◽  
Healthcare Management ◽  
Quality Cost ◽  
Healthcare Data ◽  
Management Quality ◽  
Factors Influencing ◽  
Quality Decision

Demand for improvement in healthcare management in the areas of quality, cost, and patient care has been on the upsurge because of technology. Incessant application and new technological development to manage healthcare data significantly led to leveraging on the use of big data and analytics (BDA). The application of the capabilities from BDA has provided healthcare institutions with the ability to make critical and timely decisions for patients and data management. Adopting BDA by healthcare institutions hinges on some factors necessitating its application. This study aims to identify and review what influences healthcare institutions towards the use of business intelligence and analytics. With the use of a systematic review of 25 articles, the study identified nine dominant factors driving healthcare institutions to BDA adoption. Factors such as patient management, quality decision making, disease management, data management, and promoting healthcare efficiencies were among the highly ranked factors influencing BDA adoption.

Exploring Factors Influencing Big Data and Analytics Adoption in Healthcare Management

2022 ◽  
pp. 1433-1449
Author(s):  
Sampson Abeeku Edu ◽  
Divine Q. Agozie
Keyword(s):  
Big Data ◽  
Data Management ◽  
Patient Management ◽  
Technological Development ◽  
Healthcare Management ◽  
Quality Cost ◽  
Healthcare Data ◽  
Management Quality ◽  
Factors Influencing ◽  
Quality Decision

Demand for improvement in healthcare management in the areas of quality, cost, and patient care has been on the upsurge because of technology. Incessant application and new technological development to manage healthcare data significantly led to leveraging on the use of big data and analytics (BDA). The application of the capabilities from BDA has provided healthcare institutions with the ability to make critical and timely decisions for patients and data management. Adopting BDA by healthcare institutions hinges on some factors necessitating its application. This study aims to identify and review what influences healthcare institutions towards the use of business intelligence and analytics. With the use of a systematic review of 25 articles, the study identified nine dominant factors driving healthcare institutions to BDA adoption. Factors such as patient management, quality decision making, disease management, data management, and promoting healthcare efficiencies were among the highly ranked factors influencing BDA adoption.

scholarly journals Data utilisation and factors influencing the performance of the health management information system in Tanzania

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Leonard E. G. Mboera ◽  
Susan F. Rumisha ◽  
Doris Mbata ◽  
Irene R. Mremi ◽  
Emanuel P. Lyimo ◽  
...  
Keyword(s):  
Health Care ◽  
Information System ◽  
Data Management ◽  
Management Information System ◽  
Health Management ◽  
Health Care Facilities ◽  
Health Management Information System ◽  
Management Information ◽  
Factors Influencing ◽  
Care Facilities

Abstract Background Health Management Information System (HMIS) is a set of data regularly collected at health care facilities to meet the needs of statistics on health services. This study aimed to determine the utilisation of HMIS data and factors influencing the health system’s performance at the district and primary health care facility levels in Tanzania. Methods This cross-sectional study was carried out in 11 districts and involved 115 health care facilities in Tanzania. Data were collected using a semi-structured questionnaire administered to health workers at facility and district levels and documented using an observational checklist. Thematic content analysis approach was used to synthesise and triangulate the responses and observations to extract essential information. Results A total of 93 healthcare facility workers and 13 district officials were interviewed. About two-thirds (60%) of the facility respondents reported using the HMIS data, while only five out of 13 district respondents (38.5%) reported analysing HMIS data routinely. The HMIS data were mainly used for comparing performance in terms of services coverage (53%), monitoring of disease trends over time (50%), and providing evidence for community health education and promotion programmes (55%). The majority (41.4%) of the facility’s personnel had not received any training on data management related to HMIS during the past 12 months prior to the survey. Less than half (42%) of the health facilities had received supervisory visits from the district office 3 months before this assessment. Nine district respondents (69.2%) reported systematically receiving feedback on the quality of their reports monthly and quarterly from higher authorities. Patient load was described to affect staff performance on data collection and management frequently. Conclusion Inadequate analysis and poor data utilisation practices were common in most districts and health facilities in Tanzania. Inadequate human and financial resources, lack of incentives and supervision, and lack of standard operating procedures on data management were the significant challenges affecting the HMIS performance in Tanzania.

A Survey of Mobile Internet Data Management: Models and Searching Methods

2011 ◽  
Vol 109 ◽  
pp. 738-742 ◽  
Author(s):  
Hui Zheng ◽  
Yuan Xu
Keyword(s):  
Data Management ◽  
State Of The Art ◽  
The State ◽  
Mobile Internet ◽  
Point Of View ◽  
Application Area ◽  
Management Models ◽  
Open Issues

We survey the state-of-the-art in mobile internet data management. We focus on the models and searching methods presented in the literature. We explore the models and searching methods in mobile internet data management from two perspectives: application area and research content. We outline the entire landscape of the models and searching methods of mobile internet data management from researcher’s point of view. Some researchers have proposed the models and searching methods to achieve the data management of mobile internet. But there are many open issues which still need to be addressed.

Intelligente Druckluftsysteme und Datenmodellierung*/Standardized data management for smart compressed air systems – Conceptual implementation of aggregated environmental production data according to ISO 20140

2019 ◽  
Vol 109 (06) ◽  
pp. 510-514
Author(s):  
M. Reisinger ◽  
C. Dierolf ◽  
C. Schneider ◽  
A. Sauer ◽  
G. Hörcher
Keyword(s):  
Energy Efficiency ◽  
Data Management ◽  
Manufacturing Systems ◽  
Compressed Air ◽  
Production Data ◽  
Use Case ◽  
Factors Influencing ◽  
System Components ◽  
Air System

Der Beitrag präsentiert zunächst ein Klassifikationsschema für Druckluftsystem-Komponenten. Darauffolgend wird die Normenreihe ISO 20140 als Basis für ein standardisiertes Datenmanagement vorgestellt. Die Normenreihe beschreibt eine Methode für die Erfassung, Aggregation und Bewertung der Energieeffizienz und weiterer umweltrelevanter Faktoren von Fertigungssystemen. Dargestellt wird die Konzeption eines Datenmodellierung-Anwendungsfalls eines pneumatischen Systems in Form eines Demonstrators.   This paper introduces a schemata for the classification of compressed air system components. It presents the standard ISO 20140 as a standardized data modeling technique. The standard represents a method for evaluating energy efficiency and other factors influencing the environment of manufacturing systems. The presented use case is a pneumatic sub system of a smart compressed air system demonstrator.

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