994 resultados para Information Accountability


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eHealth systems promise enviable benefits and capabilities for healthcare delivery. However, the technologies that make these capabilities possible introduce undesirable drawbacks such as information security related threats, which need to be appropriately addressed. Lurking in these threats are information privacy concerns. Addressing them has proven to be difficult because they often conflict with information access requirements of healthcare providers. Therefore, it is important to achieve an appropriate balance between these requirements. We contend that information accountability (IA) can achieve this balance. In this paper, we introduce accountable-eHealth (AeH) systems, which are eHealth systems that utilise IA as a measure of information privacy. We discuss how AeH system protocols can successfully achieve the aforementioned balance of requirements. As a means of implementation feasibility, we compare characteristics of AeH systems with Australia’s Personally Controlled Electronic Health Record (PCEHR) sys-tem and identify similarities and highlight the differences and the impact those differences would have to the eHealth domain.

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In this paper, we present the results of a survey conducted to measure the attitudes of the consumers of eHealth towards Accountable-eHealth systems which are designed for information privacy management. A research model is developed that can identify the factors contributing to system acceptance and is validated using quantitative data from 187 completed survey responses from university students studying non-health related courses at a university in Queensland, Australia. The research model is validated using structural equation modelling and can be used to identify how specific characteristics of Accountable-eHealth systems would affect their overall acceptance by future eHealth consumers.

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This tutorial primarily focuses on the technical challenges surrounding the design and implementation of Accountable-eHealth (AeH) systems. The potential benefits of shared eHealth records systems are promising for the future of improved healthcare; however, their uptake is hindered by concerns over the privacy and security of patient information. In the current eHealth environment, there are competing requirements between healthcare consumers' (i.e. patients) requirements and healthcare professionals' requirements. While consumers want control over their information, healthcare professionals want access to as much information as required in order to make well informed decisions. This conflict is evident in the review of Australia's PCEHR system. Accountable-eHealth systems aim to balance these concerns by implementing Information Accountability (IA) mechanisms. AeH systems create an eHealth environment where health information is available to the right person at the right time without rigid barriers whilst empowering the consumers with information control and transparency, thus, enabling the creation of shared eHealth records that can be useful to both patients and HCPs. In this half-day tutorial, we will discuss and describe the technical challenges surrounding the implementation of AeH systems and the solutions we have devised. A prototype AeH system will be used to demonstrate the functionality of AeH systems, and illustrate some of the proposed solutions. The topics that will be covered include: designing for usability in AeH systems, the privacy and security of audit mechanisms, providing for diversity of users, the scalability of AeH systems, and finally the challenges of enabling research and Big Data Analytics on shared eHealth Records while ensuring accountability and privacy are maintained.

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This tutorial primarily focuses on the social aspects of implementing a novel eHealth systems called Accountable-eHealth (AeH) systems. The main focus of AeH systems is mitigating information privacy concerns whilst facilitating appropriate access to information for users, and is based on the principles of information accountability (IA).

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This paper provides a first look at the acceptance of Accountable-eHealth systems, a new genre of eHealth systems, designed to manage information privacy concerns that hinder the proliferation of eHealth. The underlying concept of AeH systems is appropriate use of information through after-the-fact accountability for intentional misuse of information by healthcare professionals. An online questionnaire survey was utilised for data collection from three educational institutions in Queensland, Australia. A total of 23 hypothesis relating to 9 constructs were tested using a structural equation modelling technique. A total of 334 valid responses were received. The cohort consisted of medical, nursing and other health related students studying at various levels in both undergraduate and postgraduate courses. The hypothesis testing disproved 7 hypotheses. The empirical research model developed was capable of predicting 47.3% of healthcare professionals’ perceived intention to use AeH systems. A validation of the model with a wider survey cohort would be useful to confirm the current findings.

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Decision-making is such an integral aspect in health care routine that the ability to make the right decisions at crucial moments can lead to patient health improvements. Evidence-based practice, the paradigm used to make those informed decisions, relies on the use of current best evidence from systematic research such as randomized controlled trials. Limitations of the outcomes from randomized controlled trials (RCT), such as “quantity” and “quality” of evidence generated, has lowered healthcare professionals’ confidence in using EBP. An alternate paradigm of Practice-Based Evidence has evolved with the key being evidence drawn from practice settings. Through the use of health information technology, electronic health records (EHR) capture relevant clinical practice “evidence”. A data-driven approach is proposed to capitalize on the benefits of EHR. The issues of data privacy, security and integrity are diminished by an information accountability concept. Data warehouse architecture completes the data-driven approach by integrating health data from multi-source systems, unique within the healthcare environment.

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The concept of big data has already outperformed traditional data management efforts in almost all industries. Other instances it has succeeded in obtaining promising results that provide value from large-scale integration and analysis of heterogeneous data sources for example Genomic and proteomic information. Big data analytics have become increasingly important in describing the data sets and analytical techniques in software applications that are so large and complex due to its significant advantages including better business decisions, cost reduction and delivery of new product and services [1]. In a similar context, the health community has experienced not only more complex and large data content, but also information systems that contain a large number of data sources with interrelated and interconnected data attributes. That have resulted in challenging, and highly dynamic environments leading to creation of big data with its enumerate complexities, for instant sharing of information with the expected security requirements of stakeholders. When comparing big data analysis with other sectors, the health sector is still in its early stages. Key challenges include accommodating the volume, velocity and variety of healthcare data with the current deluge of exponential growth. Given the complexity of big data, it is understood that while data storage and accessibility are technically manageable, the implementation of Information Accountability measures to healthcare big data might be a practical solution in support of information security, privacy and traceability measures. Transparency is one important measure that can demonstrate integrity which is a vital factor in the healthcare service. Clarity about performance expectations is considered to be another Information Accountability measure which is necessary to avoid data ambiguity and controversy about interpretation and finally, liability [2]. According to current studies [3] Electronic Health Records (EHR) are key information resources for big data analysis and is also composed of varied co-created values [3]. Common healthcare information originates from and is used by different actors and groups that facilitate understanding of the relationship for other data sources. Consequently, healthcare services often serve as an integrated service bundle. Although a critical requirement in healthcare services and analytics, it is difficult to find a comprehensive set of guidelines to adopt EHR to fulfil the big data analysis requirements. Therefore as a remedy, this research work focus on a systematic approach containing comprehensive guidelines with the accurate data that must be provided to apply and evaluate big data analysis until the necessary decision making requirements are fulfilled to improve quality of healthcare services. Hence, we believe that this approach would subsequently improve quality of life.

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This paper provides a first look at the acceptance of Accountable-eHealth (AeH) systems–a new genre of eHealth systems designed to manage information privacy concerns that hinder the proliferation of eHealth. The underlying concept of AeH systems is appropriate use of information through after-the-fact accountability for intentional misuse of information by healthcare professionals. An online questionnaire survey was utilised for data collection from three educational institutions in Queensland, Australia. A total of 23 hypotheses relating to 9 constructs were tested using a structural equation modelling technique. The moderation effects on the hypotheses were also tested based on six moderation factors to understand their role on the designed research model. A total of 334 valid responses were received. The cohort consisted of medical, nursing and other health related students studying at various levels in both undergraduate and postgraduate courses. Hypothesis testing provided sufficient data to accept 7 hypotheses. The empirical research model developed was capable of predicting 47.3% of healthcare professionals’ perceived intention to use AeH systems. All six moderation factors showed significant influence on the research model. A validation of this model with a wider survey cohort is recommended as a future study.

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This work highlights two critical taboos in organizations: 1)taking for granted the quality of certain capabilities and attitudes of the end-user representatives (EUR) in information systems development projects (ISDP), and 2) the EUR´s inherent accountability for losses in IS investments. These issues are neither addressed by theory nor research when assessing success/ failure. A triangulation approach was applied to combine quantitative and qualitative methods, having convergent results and showing that in problematic cases, paradoxically, the origin of IS rejection by end users (EU) points towards the EUR themselves. It has been evaluated to what extent some EUR factors impacted a macro ISDP involving an enterprise resource planning (ERP) package, ranking the ‘knowledge of the EUR’ as the main latent variable. The results validate some issues found throughout decades of praxis, confirming that when not properly managed the EUR role by itself has a direct relationship with IS rejection and significant losses in IS investments.