Securely sharing dynamic medical information in e-health

This thesis has introduced an infrastructure to share dynamic medical data between mixed health care providers in a secure way, which could benefit the health care system as a whole. The study results of the universally data sharing into a varied patient information system prototypes

Guidelines for the user interface design of electronic medical records in optometry

With the prevalence of digitalisation in the medical industry, e-health systems have largely replaced the traditional paper-based recording methods. At the centre of these e-health systems are Electronic Health Records (EHRs) and Electronic Medical Records (EMRs), whose benefits significantly improve physician workflows. However, provision for user interface designs (UIDs) of these systems have been so poor that they have severely hindered physician usability, disrupted their workflows and risked patient safety. UID and usability guidelines have been provided, but have been very high level and general, mostly suitable for EHRs (which are used in general practices and hospitals). These guidelines have thus been ineffective in applicability for EMRs, which are typically used in niche medical environments. Within the niche field of Optometry, physicians experience disrupted workflows as a result of poor EMR UID and usability, of which EMR guidelines to improve these challenges are scarce. Hence, the need for this research arose, aiming to create UID guidelines for EMRs in Optometry, which will help improve the usability of the optometrists’ EMR. The main research question was successfully answered to produce the set of UID Guidelines for EMRs in Optometry, which includes guidelines built upon from literature and made contextually relevant, as well as some new additions, which are more patient focused. Design Science Research (DSR) was chosen as a suitable approach, and the phased Design Science Research Process Model (DSRPM) was used to guide this research. A literature review was conducted, including EHR and EMR, usability, UIDs, Optometry, related fields, and studies previously conducted to provide guidelines, frameworks and models. The review also included studying usability problems reported on the systems and the methods to overcome them. Task Analysis (TA) was used to observe and understand the optometrists’ workflows and their interactions with their EMRs during patient appointments, also identifying EMR problem areas. To address these problems, Focus Groups (FGs) were used to brainstorm solutions in the form of EMR UID features that optometrists’ required to improve their usability. From the literature review, TAs and FGs, proposed guidelines were created. The created guidelines informed the UID of an EMR prototype, which was successfully demonstrated to optometrists during Usability Testing sessions for the evaluation. Surveys were also used for the evaluation. The results proved the guidelines were successful, and were usable, effective, efficient and of good quality. A revised, final set of guidelines was then presented. Future researchers and designers may benefit from the contributions made from this research, which are both theoretical and practical

The Knowledge Grid: A Platform to Increase the Interoperability of Computable Knowledge and Produce Advice for Health

Here we demonstrate how more highly interoperable computable knowledge enables systems to generate large quantities of evidence-based advice for health. We first provide a thorough analysis of advice. Then, because advice derives from knowledge, we turn our focus to computable, i.e., machine-interpretable, forms for knowledge. We consider how computable knowledge plays dual roles as a resource conveying content and as an advice enabler. In this latter role, computable knowledge is combined with data about a decision situation to generate advice targeted at the pending decision. We distinguish between two types of automated services. When a computer system provides computable knowledge, we say that it provides a knowledge service. When computer system combines computable knowledge with instance data to provide advice that is specific to an unmade decision we say that it provides an advice-giving service. The work here aims to increase the interoperability of computable knowledge to bring about better knowledge services and advice-giving services for health. The primary motivation for this research is the problem of missing or inadequate advice about health topics. The global demand for well-informed health advice far exceeds the global supply. In part to overcome this scarcity, the design and development of Learning Health Systems is being pursued at various levels of scale: local, regional, state, national, and international. Learning Health Systems fuse capabilities to generate new computable biomedical knowledge with other capabilities to rapidly and widely use computable biomedical knowledge to inform health practices and behaviors with advice. To support Learning Health Systems, we believe that knowledge services and advice-giving services have to be more highly interoperable. I use examples of knowledge services and advice-giving services which exclusively support medication use. This is because I am a pharmacist and pharmacy is the biomedical domain that I know. The examples here address the serious problems of medication adherence and prescribing safety. Two empirical studies are shared that demonstrate the potential to address these problems and make improvements by using advice. But primarily we use these examples to demonstrate general and critical differences between stand-alone, unique approaches to handling computable biomedical knowledge, which make it useful for one system, and common, more highly interoperable approaches, which can make it useful for many heterogeneous systems. Three aspects of computable knowledge interoperability are addressed: modularity, identity, and updateability. We demonstrate that instances of computable knowledge, and related instances of knowledge services and advice-giving services, can be modularized. We also demonstrate the utility of uniquely identifying modular instances of computable knowledge. Finally, we build on the computing concept of pipelining to demonstrate how computable knowledge modules can automatically be updated and rapidly deployed. Our work is supported by a fledgling technical knowledge infrastructure platform called the Knowledge Grid. It includes formally specified compound digital objects called Knowledge Objects, a conventional digital Library that serves as a Knowledge Object repository, and an Activator that provides an application programming interface (API) for computable knowledge. The Library component provides knowledge services. The Activator component provides both knowledge services and advice-giving services. In conclusion, by increasing the interoperability of computable biomedical knowledge using the Knowledge Grid, we demonstrate new capabilities to generate well-informed health advice at a scale. These new capabilities may ultimately support Learning Health Systems and boost health for large populations of people who would otherwise not receive well-informed health advice.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146073/1/ajflynn_1.pd

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

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

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

The Development and Evaluation of a Learning Electronic Medical Record System

Electronic medical record (EMR) systems are capturing increasing amounts of data per patient. For clinicians to efficiently and accurately understand a patient’s clinical state, better ways are needed to determine when and how to display patient data. The American Medical Association envisions EMR systems that manage information flow and adjust for context, environment, and user preferences. We developed, implemented, and evaluated a prototype Learning EMR (LEMR) system with the aim of helping make this vision a reality. A LEMR system, as we employ the term, observes clinician information seeking behavior and applies it to direct the future display of patient data. The development of this system was divided into five phases. First, we developed a prototype LEMR interface that served as a testing bed for LEMR experimentation. The LEMR interface was evaluated in two studies: a think aloud study and a usability study. The results from these studies were used to iteratively improve the interface. Second, we tested the accuracy of an inexpensive eye-tracking device and developed an automatic method for mapping eye gaze to patient data displayed in the LEMR interface. In the two studies we showed that an inexpensive eye-tracking device can perform as well as a costlier device intended for research and that the automatic mapping method accurately captures the patient information a user is viewing. Third, we collected observations of clinician information seeking behavior in the LEMR system. In three studies we evaluated different observation methods and applied those methods to collect training data. Fourth, we used machine learning on the training data to model clinician information seeking behavior. The models predict information that clinicians will seek in a given clinical context. Fifth, we applied the models to direct the display of patient data in a prospective evaluation of the LEMR system. The evaluation found that the system reduced the amount of time it takes for clinicians to prepare for morning rounds and highlighted about half of the patient data that clinicians seek