MS

thesisDelivery of high quality health care requires access to complete and accurate patient information. Variation in data context and content across disparate clinical systems adversely affects the integration of information needed for effective patient care and outcomes research. This study detects the extent and nature of data variation across three disparate clinical systems used along different points of the perinatal care continuum at Intermountain Health Care (IHC). Three analytical methods were used to examine data variation: data structure analysis; clinician perception of missing data elements; and patient record review of key data values. Knowledge acquisition techniques and consensus among clinical domain experts were used to select sample data elements for the data structure analysis. Findings revealed only 17% of the sample data elements had ompatible structure and meaning across the prenatal, labor and delivery (L&D), and newborn intensive care (NICU) clinical data systems. Impact on clinician efficiency from missing and contradicting information in nonintegrated perinatal systems was captured and analyzed using a Critical Incident Technique-based clinician survey. In a 1-month period, 75% of responding clinicians reported missing data and 34% reported contradicting data. The time taken to resolve problems from 1 month's missing data was estimated to be 231 hours for 23 clinicians. Data values from patient records for eight laboratory results were compared across the three perinatal systems. The best match across any two systems was 88% (blood type) and the worst was 0% (antibody screen, chlamydia). The highest incidence of contradicting data was 2.5% for blood type. Comparing agreement of the three methods, triangulation,"" gave additional insight into IHC's data variation problem. The data model study and the patient record review study showed missing data element problems beyond what clinicians perceived. In all, the consistency of data capture in the three perinatal systems at IHC is worse than expected. The data necessary to computationally execute the logic of the perinatal care process models is intermittent and unreliable. Rework of the perinatal applications based on a uniform data model and standard terminologies will provide an infrastructure to achieve IHC's vision of interdisciplinary care."

SecConNet:Smart and secure container networks for trusted big data sharing

There are many organizations interested in sharing data with others. However, they can do this only if a secure platform is available. Digital Data Marketplaces (DDMs) are emerging as a framework for organizations to share their data. To increase trust among participating organizations multiple agreements should be established to determine policies about who has access to what. Translating these high-level sharing policies to actionable code and setting up an infrastructure that implements and enforces the policies is still a big challenge.In SecConNet, we research novel container network architectures, which utilize programmable infrastructures and virtualization technologies across multiple administrative domains whilst maintaining the security and quality requirements of requesting parties for both private sector and scientific use cases. Containers are lightweight alternatives to full-fledged virtual machines. A container can operate as a secure, isolated, and individual entity that on behalf of its owner manages and processes the data it is given. However, for multi-organization (chain) applications groups of containers need access to the same data and/or need to exchange data among them. Technologies to connect containers are developed with primary attention to their performance, but the greatest challenge is the creation of secure and reliable multi-domain container networks. We first investigate different technologies to evaluate their capabilities to support the network infrastructure requirements in secure data sharing. We then proposed a P4-based network to be able to build a multi-domain DDM. Finally, we use the capabilities of the P4-based network to monitor the transactions in the DDM

SecConNet:Smart and secure container networks for trusted big data sharing

There are many organizations interested in sharing data with others. However, they can do this only if a secure platform is available. Digital Data Marketplaces (DDMs) are emerging as a framework for organizations to share their data. To increase trust among participating organizations multiple agreements should be established to determine policies about who has access to what. Translating these high-level sharing policies to actionable code and setting up an infrastructure that implements and enforces the policies is still a big challenge.In SecConNet, we research novel container network architectures, which utilize programmable infrastructures and virtualization technologies across multiple administrative domains whilst maintaining the security and quality requirements of requesting parties for both private sector and scientific use cases. Containers are lightweight alternatives to full-fledged virtual machines. A container can operate as a secure, isolated, and individual entity that on behalf of its owner manages and processes the data it is given. However, for multi-organization (chain) applications groups of containers need access to the same data and/or need to exchange data among them. Technologies to connect containers are developed with primary attention to their performance, but the greatest challenge is the creation of secure and reliable multi-domain container networks. We first investigate different technologies to evaluate their capabilities to support the network infrastructure requirements in secure data sharing. We then proposed a P4-based network to be able to build a multi-domain DDM. Finally, we use the capabilities of the P4-based network to monitor the transactions in the DDM

Designing Data Spaces

This open access book provides a comprehensive view on data ecosystems and platform economics from methodical and technological foundations up to reports from practical implementations and applications in various industries. To this end, the book is structured in four parts: Part I “Foundations and Contexts” provides a general overview about building, running, and governing data spaces and an introduction to the IDS and GAIA-X projects. Part II “Data Space Technologies” subsequently details various implementation aspects of IDS and GAIA-X, including eg data usage control, the usage of blockchain technologies, or semantic data integration and interoperability. Next, Part III describes various “Use Cases and Data Ecosystems” from various application areas such as agriculture, healthcare, industry, energy, and mobility. Part IV eventually offers an overview of several “Solutions and Applications”, eg including products and experiences from companies like Google, SAP, Huawei, T-Systems, Innopay and many more. Overall, the book provides professionals in industry with an encompassing overview of the technological and economic aspects of data spaces, based on the International Data Spaces and Gaia-X initiatives. It presents implementations and business cases and gives an outlook to future developments. In doing so, it aims at proliferating the vision of a social data market economy based on data spaces which embrace trust and data sovereignty

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe