Data Management in a Community-Based Birth Cohort: What the SEMILLA Study Teaches Us

Management of information systems can be divided into strategic, tactical, and operational management.Strategic management of information systems deals with the information processing of a healthcare facility as a whole. It comprises the planning of the information system’s architecture and of the organization of information management. The tasks of strategic management are planning an information system and its architecture, directing its establishment and its operation, and monitoring its development and operation with respect to the planned objectives. The strategic information management plan represents the long-term planning of the information system of a healthcare facility.Tactical management of information systems deals with particular functions, application components, or physical data processing systems that are introduced, removed, or changed. Usually, these activities are done in the form of projects.Operational management of information systems is responsible for operating the components of the information system. It ensures the smooth operation of the systems in accordance with the strategic information management plan of the healthcare facility.IT governance deals with the organizational structures for decision-making in the management of information systems. Typical organizational structures include the role of a chief information officer who focuses on the strategic management of information systems, an information management board, and a department of information management in charge of the strategic, tactical, and operational management.Management of information systems must find a balance between often conflicting goals, for example, between homogeneity and heterogeneity of the information system architecture or between functional leanness and functional redundancy.

<p>Integration of data management systems is a persistent problem in European projects that span multiple agencies. Months, if not years of projects are often expended on the integration of disparate database structures, data types, methodologies and outputs. Moreover, this work is usually confined to a single effort, meaning it is needlessly repeated on subsequent projects. The legacy effect of removing these barriers could therefore yield monetary and time savings for all involved, far beyond a single cross-jurisdictional project. </p><p>The European Union’s INTERREG VA Programme has funded the COMPASS project to better manage marine protected areas (MPA) in peripheral areas. Involving five organisations, spread across two nations, the project has developed a cross-border network for marine monitoring. Three of those organisations are UK-based and bound for Brexit (the Agri-Food and Biosciences Institute, Marine Scotland Science and the Scottish Association of Marine Science). With that network under construction, significant efforts have been placed on harmonizing data management processes and procedures between the partners. </p><p>A data management quality management framework (DM-QMF) was introduced to guide this harmonization and ensure adequate quality controls would be enforced. As lead partner on data management, the Irish Marine Institute (MI) initially shared guidelines for infrastructure, architecture and metadata. The implementation of those requirements were then left to the other four partners, with the MI acting as facilitator. This led to the following being generated for each process in the project:</p><p>Data management plan: Information on how and what data were to be generated as well as where it would be stored. </p><p>Flow diagrams: Diagrammatic overview of the flow of data through the project. </p><p>Standard Operating Procedures: Detailed explanatory documents on the precise workings of a process.</p><p>Data management processes were allowed to evolve naturally out of a need to adhere to this set standard. Organisations were able to work within their operational limitations, without being required to alter their existing procedures, but encouraged to learn from each other. Very quickly it was found that there were similarities in processes, where previously it was thought there were significant differences. This process of sharing data management information has created mutually benefiting synergies and enabled the convergence of procedures within the separate organisations. </p><p>The downstream data management synergies that COMPASS has produced have already taken effect. Sister INTERREG VA projects, SeaMonitor and MarPAMM, have felt the benefits. The same data management systems cultivated as part of the COMPASS project are being reused, while the groundwork in creating strong cross boundary channels of communication and cooperation are saving significant amounts of time in project coordination.</p><p>Through data management, personal and institutional relationships have been strengthened, both of which should persist beyond the project terminus in 2021, well into a post-Brexit Europe. The COMPASS project has been an exemplar of how close collaboration can persist and thrive in a changing political environment, in spite of the ongoing uncertainty surrounding Brexit.</p>

Despite the ever increasing pervasion of Information Systems (IS) in business processes the industry lacks a focus on sustainability. While concepts of sustainability have been applied to many other areas, IS is yet to demonstrate a balanced economic, ecological and social focus. This paper discusses the issues of IS sustainability and the implications for strategic IS management. It analyses the major stakeholder businesses that the Information and Communications (ICT) industry deal with and identifies requirements for successful sustainable IS management. It also discusses current emerging technologies in the field of ICT and their potential for supporting sustainable IS management. Bringing these ideas together will extend the ongoing discussion of ICT's environmental impact by providing Chief Information Officers, IS and environmental managers and researchers alike with a conceptual foundation towards future sustainable information systems management.

Background: Efficient data management and escalation processes are crucial for facilitating continuous improvement initiatives. Stroke care necessitates quick identification of opportunities for improvement (OFIs) related to core measures and performance improvement initiatives (PI). This abstract presents an overview of a tailored data management and escalation process designed to enhance stroke team performance through the utilization of SharePoint™. Methods: SharePoint™ list was configured to simultaneously capture OFIs identified by the stroke team. An automated email notification system triggers a prompt alert to the assigned parties, including unit department administrators and stroke champions, regarding the new entry. The assigned parties receive the email notification along with an audit link, granting them direct access to the corresponding audit form. Stakeholders then analyze the OFIs, develop action plans and assess trends in the data. This process supports continuous learning and adaptation, enabling the stroke team to refine strategies and optimizes compliance with The Joint Commission stroke standards. Results: From October 2021 through July 2024, 2,292 escalations were issued. The mean number of escalations per unit was 134.82. Most departments (8 out of 17) have escalations between 0 and 50. The highest number of escalations were in the ICU 639 (28%), followed by Interventional Radiology 542 (24%), and PACU 226 (10%). There were a total of 22,668 site visits to the SharePoint™ site, with 210 unique viewers. Figure 1 shows the SharePoint™ entry form that is utilized for escalation and analysis. Figure 2 displays PowerBI™ visualization depicting the unit summary of escalations by month, unit, and OFI type. Figure 3 demonstrates the site usage traffic. Conclusion: The data management and escalation process outlined in this abstract represents a paradigm shift in stroke team performance improvement strategies. By harnessing the capabilities of SharePoint™ and leveraging automated notifications, real-time data analytics, and collaborative action planning, healthcare organizations can effectively navigate the complexities of performance improvement in a rapidly evolving landscape. This process not only enhances operational efficiency but also empowers stakeholders to drive meaningful change, ultimately leading to improved patient outcomes and enhanced quality of care driven by sustainable PI.

Despite treatment improvement and performance management imperatives, little research describes the data management capacity of substance abuse treatment programs, and useful metrics are not available to gauge capacity. This exploratory study evaluates clinical and administrative data management at eight substance abuse treatment programs in four US states to identify factors for developing an appropriate metric. Findings indicate that programs tend to manage data inefficiently and have few protocols guiding information management. Barriers to better data management included lack of integrated information technology (IT) systems; limited funding, time, and staff for developing and implementing IT-related changes; and divergent staff skills in and attitudes toward IT. This snapshot of substance abuse treatment programs' data management capabilities suggests a need for a metric to examine data management capability in these settings. Infusion of expertise, training, and funding are needed to improve substance abuse treatment programs' IT-related systems and data management processes.

This tutorial presents the definition, the models and the techniques of location privacy from the data privacy perspective. By reviewing and revising the state of art research in data privacy area, the presenter describes the essential concepts, the alternative models, and the suite of techniques for providing location privacy in mobile and ubiquitous data management systems. The tutorial consists of two main components. First, we will introduce location privacy threats and give an overview of the state of art research in data privacy and analyze the applicability of the existing data privacy techniques to location privacy problems. Second, we will present the various location privacy models and techniques effective in either the privacy policy based framework or the location anonymization based framework. The discussion will address a number of important issues in both data privacy and location privacy research, including the location utility and location privacy trade-offs, the need for a careful combination of policy- based location privacy mechanisms and location anonymization based privacy schemes, as well as the set of safeguards for secure transmission, use and storage of location information, reducing the risks of unauthorized disclosure of location information. The tutorial is designed to be self-contained, and gives the essential background for anyone interested in learning about the concept and models of location privacy, and the principles and techniques for design and development of a secure and customizable architecture for privacy-preserving mobile data management in mobile and pervasive information systems. This tutorial is accessible to data management administrators, mobile location based service developers, and graduate students and researchers who are interested in data management in mobile information systems, pervasive computing, and data privacy.

Fleet management refers to the management of ships while at sea also. In terms of maritime fleet management, there is important function the software must possess - data management. This also means transfer and replicate data between the office and the individual ships of the fleet. This ensures that the respective databases on shore and on board remain synchronized and that all involved personnel are working with the same information. Cloud-based maritime fleet management software performs exceptionally well on these and more requirements. The aim of this paper is to underline some important topics in the area of data management in cloud-based maritime fleet management software. Above all are explain: role of software as a service (SaaS) which is considered be part of the nomenclature of cloud computing (CC) in data quality, and role of Key Performance Indicators (KPI) in data management process.

The Norwegian Health Archives Registry (NHAR) is a national initiative dedicated to digitising, centralising, and providing access to historical full-text patient health records (PHRs) for research purposes. Established in 2019, NHAR includes PHRs from the deceased population in Norway's specialist healthcare services, offering a unique long-term data source for future research. NHAR has now digitised 1.7 million paper-based PHRs, covering medical history dating back to 1875. The registry is now expanding to include digital-born PHRs. This article describes NHAR's innovation potential as a health registry, its data management processes, and the integration of artificial intelligence (AI) tools to facilitate data management and research in compliance with strict health data regulations. NHAR's data value chain includes structured metadata acquisition, large-scale digitisation and secure data delivery for research. The workflow includes a custom optical character recognition (OCR) tool tailored to Norwegian medical terminology, concept-based search tools for unstructured clinical full text and robust strategies for long-term data management. A novel AI-based de-identification system automatically detects and masks personal identifiers in digitised PHRs. Despite these innovations, challenges persist in processing handwritten and historical PHRs due to OCR limitations and language-specific complexities. Key challenges include improving data quality, enhancing OCR accuracy and refining AI tools for information retrieval, data extraction and de-identification. NHAR offers significant potential for interdisciplinary research across various medical fields.Implications for health information management practice:NHAR establishes a foundation for secure access to historical health data and introduces advanced data management strategies to facilitate future research.

This paper presents a collaborative data management framework which is capable of supporting information sharing and team data management in concurrent team-oriented product and process development by providing functions for project configuration, personal product and process item management, and team library management. Establishing this framework involves: (i) identification of functional requirements for computer-aided engineering data management through the investigation of concurrent product delivery process, with an emphasis on product and process development, (ii) use of system engineering and object-oriented modeling techniques for development of the proposed framework.

IntroductionInteroperability of collected dataset has been distress for researchers. Introduction of database management software made some improvements, however, still we need many of craftworks to convert and clean up original...

Data management in always-on enterprise information systems is an important function that must be governed, that is, planned, supervised, and controlled. According to Data Management Association, data management is the development, execution, and supervision of plans, policies, programs, and practices that control, protect, deliver, and enhance the value of data and information assets. The challenges of successful data management are numerous and vary from technological to conceptual and managerial. The purpose of this chapter is to consider some of the most challenging aspects of data management, whether they are classified as data continuity aspects (e.g., data availability, data protection, data integrity, data security), data improvement aspects (e.g., coping with data overload and data degradation, data integration, data quality, data ownership/stewardship, data privacy, data visualization) or data management aspect (e.g., data governance), and to consider the means of taking care of them.

The insights that firms gain from big data analytics (BDA) in real time is used to direct, automate and optimize the decision making to successfully achieve their organizational goals. Data management (DM) and advance analytics (AA) tools and techniques are some of the key contributors to making BDA possible. This paper aims to investigate the characteristics of BD, processes of data management, AA techniques, applications across sectors and issues that are related to their effective implementation and management within broader context of BDA. A range of recently published literature on the characteristics of BD, DM processes, AA techniques are reviewed to explore their current state, applications, issues and challenges learned from their practice. The finding discusses different characteristics of BD, a framework for BDA using data management processes and AA techniques. It also discusses the opportunities/applications and challenges managers dealing with these technologies face for gaining competitive advantages in businesses. The study findings are intended to assist academicians and managers in effectively quantifying the data available in an organization into BD by understanding its properties, understanding the emerging technologies, applications and issues behind BDA implementation.

This research focuses on the development and implementation of a web-based citizen data management system in Keboan Anom Village, Sidoarjo, Indonesia. The aim of the study is to enhance the efficiency and effectiveness of citizen data recording and management processes. The System Development Life Cycle (SDLC) Waterfall model is employed as the methodology for system development, ensuring an organized and systematic approach. Data collection involves semi-structured interviews and unstructured observations, targeting the village's RT 01 (neighborhood association) officials. The application features two key functions: citizen registration, including permanent and non-permanent residents, and a comprehensive data summary. Access to these functions is limited to the RT Secretary and the RT Chairman. The implementation of the system provides improved data management and financial tracking capabilities, enabling simplified reporting. The web-based platform ensures accessibility for RT officials and residents with internet connectivity. Black box testing confirms the functionality of the website and its compliance with expected outputs. The results demonstrate that the application supports the transformation of Keboan Anom Village into a more efficient and technologically connected Digital Village. Future research is suggested to examine community participation levels in using the citizen data management application and its impact on decision-making and overall village governance, incorporating participatory approaches to understand the perspectives and active involvement of Keboan Anom Village residents.
 Highlight:
 
 Efficient citizen data management: The development of a web-based application for citizen data management in Desa Keboan Anom enables efficient recording and documentation of resident information, improving data accuracy and accessibility.
 Technological transformation: The implementation of the web-based system contributes to the realization of a Digital Village concept, facilitating efficient and technology-connected administration in Desa Keboan Anom, Sidoarjo.
 Participatory approach: Future research should focus on evaluating community participation levels and the impact of the citizen data management application on decision-making and overall village governance, utilizing a participatory approach to gain insights and foster active involvement of Keboan Anom Village residents.
 
 Keyword:
 Keboan Anom Village, Citizen Data Management, Web-Based Application, Efficiency, Participatory Approach

This poster introduces the efficient set of research support services available in University of Jyväskylä’s (JYU) Open Science Centre (OSC). The set includes several essential services for researchers to help them execute an open and responsible research process. The continuous improvement of research support services has led to an interlinked, integrated and harmonious set of technical solutions, processes and workflows which meet the needs of researchers and help them implement open science and FAIR policies in practice. They also save time and enable better working processes for the researchers but also for the experts conducting these services. The poster describes two main services which are interlinked and which both aim to make processes and workflows effortless. One of the main services is when metadata from research information system Converis is utilized using API in the self-archiving process of research articles to the institutional repository. OSC’s experts record the publication metadata on behalf of researchers and the same metadata of the publications is used in the self-archiving process. Also, data harvesting is used to get notification of new publications of researchers affiliated to JYU. Often the researchers don’t even have to inform OSC when their new publication is out. This effortless process combines two different systems (CRIS and institutional repository) and connects workflows regarding them. The poster also introduces a dynamic research data management tool which enables a fluent data management process. The goal of the development of this tool has been to free the researchers of having to learn the use of several different systems. The tool helps the researchers to track the handling process of their data and guides through all the necessary phases of the data management process. Again, the data once recorded is being utilized in other systems and in other phases of the whole process. Both the tool and the data management process it enables are integrated to other systems which are needed for example in storing and sharing the research data.

In a community-based birth cohort of 158 Australian infants followed to age 2 years, the incidence rate of human parainfluenza virus (HPIV) was 0.42 (95% CI = 0.33, 0.54) episodes per child-year with episodes occurring year-round, peaking in the spring season. HPIV-3 was the dominant subtype. Overall, 41% of detections were asymptomatic; only 32% of HPIV episodes led to healthcare contact with 1 hospitalization.