From Data to Decision: An Implementation Model for the Use of Evidence-based Medicine, Data Analytics, and Education in Transfusion Medicine Practice

Healthcare in the United States is underperforming despite record increases in spending. The causes are as myriad and complex as the suggested solutions. It is increasingly important to carefully assess the appropriateness and cost-effectiveness of treatments especially the most resource-consuming clinical interventions. Healthcare reimbursement models are evolving from fee-for-service to outcome-based payment. The Patient Protection and Affordable Care Act has added new incentives to address some of the cost, quality, and access issues related to healthcare, making the use of healthcare data and evidence-based decision-making essential strategies. However, despite the great promise of these strategies, the transition to data-driven, evidence-based medical practice is complex and faces many challenges. This study aims to bridge the gaps that exist between data, knowledge, and practice in a healthcare setting through the use of a comprehensive framework to address the administrative, cultural, clinical, and technical issues that make the implementation and sustainability of an evidence-based program and utilization of healthcare data so challenging. The study focuses on promoting evidence-based medical practice by leveraging a performance management system, targeted education, and data analytics to improve outcomes and control costs. The framework was implemented and validated in transfusion medicine practice. Transfusion is one of the top ten coded hospital procedures in the United States. Unfortunately, the costs of transfusion are underestimated and the benefits to patients are overestimated. The particular aim of this study was to reduce practice inconsistencies in red blood cell transfusion among hospitalists in a large urban hospital using evidence-based guidelines, a performance management system, recurrent reporting of practice-specific information, focused education, and data analytics in a continuous feedback mechanism to drive appropriate decision-making prior to the decision to transfuse and prior to issuing the blood component. The research in this dissertation provides the foundation for implementation of an integrated framework that proved to be effective in encouraging evidence-based best practices among hospitalists to improve quality and lower costs of care. What follows is a discussion of the essential components of the framework, the results that were achieved and observations relative to next steps a learning healthcare organization would consider

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

HR Selection Distortions: A theoretical framework for the Fiji Public Service

Despite being frequently perceived as a pertinent issue necessary to critically examine how incumbents are selected on merit, HR selection distortions is typically illdefined and poorly explained in much debate, hence, more precision in terms of contextualization of practice is needed. Through explaining and synthesizing the work of a number of scholars from different disciplines, the paper develops a theoretical framework for a meta- analysis, which begins with an exploration of the relationship between HR selection, networking and relational ties, employee’s justice perceptions, group heterogeneity and worker performance in Fiji’s public service institutions. The theoretical framework provides the leeway for the research questions to be answerable and the postulated hypotheses testable. However, more needs to be done to explain not only the nature and emergence of HR selection distortions but also the very real problems it faces in sustaining itself, let alone transforming the hiring processes in Fiji’s public service. The value of the paper lies in its theoretical innovation, drawing on a range of disciplines, and its attempt to situate HR selection distortions precisely, conceptually, theoretically, and practically

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

AAPOR Report on Big Data

In recent years we have seen an increase in the amount of statistics in society describing different phenomena based on so called Big Data. The term Big Data is used for a variety of data as explained in the report, many of them characterized not just by their large volume, but also by their variety and velocity, the organic way in which they are created, and the new types of processes needed to analyze them and make inference from them. The change in the nature of the new types of data, their availability, the way in which they are collected, and disseminated are fundamental. The change constitutes a paradigm shift for survey research.There is a great potential in Big Data but there are some fundamental challenges that have to be resolved before its full potential can be realized. In this report we give examples of different types of Big Data and their potential for survey research. We also describe the Big Data process and discuss its main challenges

Usability analysis of contending electronic health record systems

In this paper, we report measured usability of two leading EHR systems during procurement. A total of 18 users participated in paired-usability testing of three scenarios: ordering and managing medications by an outpatient physician, medicine administration by an inpatient nurse and scheduling of appointments by nursing staff. Data for audio, screen capture, satisfaction rating, task success and errors made was collected during testing. We found a clear difference between the systems for percentage of successfully completed tasks, two different satisfaction measures and perceived learnability when looking at the results over all scenarios. We conclude that usability should be evaluated during procurement and the difference in usability between systems could be revealed even with fewer measures than were used in our study. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe

An Epidemiology of Big Data

Federal legislation designed to transform the U.S. healthcare system and the emergence of mobile technology are among the common drivers that have contributed to a data explosion, with industry analysts and stakeholders proclaiming this decade the big data decade in healthcare (Horowitz, 2012). But a precise definition of big data is hazy (Dumbill, 2013). Instead, the healthcare industry mainly relies on metaphors, buzzwords, and slogans that fail to provide information about big data\u27s content, value, or purposes for existence (Burns, 2011). Bollier and Firestone (2010) even suggests big data does not really exist in healthcare (p. 29). While federal policymakers and other healthcare stakeholders struggle with the adoption of Meaningful Use Standards, International Classification of Diseases-10 (ICD-10), and electronic health record interoperability standards, big data in healthcare remains a widely misunderstood phenomenon. Borgman (2012) found by studying how data are created, handled, and managed in multi-disciplinary collaborations, we can inform science policy and practice (p. 12). Through the narratives of nine leaders representing three key stakeholder classes in the healthcare ecosystem: government, providers and consumers, this phenomenological research study explored a fundamental question: Within and across the narratives of three key healthcare stakeholder classes, what are the important categories of meaning or current themes about big data in healthcare? This research is significant because it: (1) produces new thematic insights about the meaning of big data in healthcare through narrative inquiry; (2) offers an agile framework of big data that can be deployed across all industries; and, (3) makes a unique contribution to scholarly qualitative literature about the phenomena of big data in healthcare for future research on topics including the diffusion and spread of health information across networks, mixed methods studies about big data, standards development, and health policy

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Mapping the Path to a Health Data Marketplace in Norway: An Exploratory Case Study

This Master's thesis explores the complex dynamics of health data in the digital age, focusing on its secure and efficient management and ethical considerations. It investigates the potential of implementing a Health Data Marketplace (HDM) in the Norwegian e-health sector, aiming to construct a seamless health data exchange platform. This study proposes the integration of an existing health data gateway, the Egde Health Gateway (EHG), with the HDM. The research offers an in-depth analysis of existing limitations in health data exchange systems in Norway. It addresses current research gaps in Data Marketplace, Business Models, Gateways, and the Norwegian e-health context. Guided by two central research questions, this thesis delves into identifying essential components required to successfully implement an HDM in Norway and how this marketplace could be established using an existing data platform. Significantly, the thesis underscores the pivotal role of primary stakeholders in the HDM - Platform Operators, Platform Users, and Legal Authorities. The exploration reveals that Platform Operators are vital influencers, fostering collaboration and innovation within the ecosystem, while Platform Users and Legal Authorities ensure the marketplace's innovative and compliance aspects. Additionally, this study identifies essential components for successfully integrating an HDM into an existing health data platform, including Data Standardization, Interoperability, Integration, Security, Trust, and Legal Frameworks, among others. The thesis marks a significant step towards realizing an HDM in the Norwegian e-health sector. It invites future research to broaden stakeholder perspectives, examine economic aspects of the HDM, and delve into ethical considerations and technological innovations. The findings from this exploration serve as a catalyst for leveraging health data effectively, securely, and ethically, contributing to improved healthcare outcomes, research, and innovation in Norway and beyond