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Learn how Q-Pulse, a quality management solution, can help organisations within the life science industry to maintain data integrity. This is particularly critical for pharmaceutical companies.
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The third webcast in this series focuses on ways to meet your health system’s specific needs and achieve a 360-degree view of your patients, processes, physicians, and costs without purchasing multiple, disparate solutions, and creating information silos. Our speakers discuss their collective experience in working with organizations to create tailored platforms that provide convenient access to data collected by, and stored in, disparate clinical information systems and enabling that data to be securely used by users throughout the broader healthcare community. Actionable data – available to all users when they need it – serves as a foundation for analysis and decision-making aimed at improving how care is delivered. You can find it online at http://www.informationbuilders.com/webevents/online/24637#sthash.RnwoH27x.dpuf
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Analytics is a key enabler for life sciences and healthcare organizations to create better outcomes for patients, customers and other stakeholders across the entire healthcare ecosystem. While almost two-thirds of organizations across the healthcare ecosystem have analytics strategies in place, our research shows that only a fifth are driving analytics adoption across the enterprise. The key barriers are a lack of data management capabilities and skilled analysts, as well as poor organizational change management. To develop and translate insights into actions that enhance outcomes, organizations will need to collaborate across an expanding ecosystem.
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Cloud computing promises to fundamentally transform the global healthcare industry. But most healthcare providers have only just started to understand the power of cloud to not only drive efficiency, but also to redefine collaboration, partnering, and business models. The IBM Institute for Business Value point-of-view explores the opportunities and implications of cloud computing to help global healthcare companies meet new competitive pressures and ever-expanding consumer expectations.
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Learn how Q-Pulse, a quality management solution, can help organisations within the life science industry to maintain data integrity. This is particularly critical for pharmaceutical companies.
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The third webcast in this series focuses on ways to meet your health system’s specific needs and achieve a 360-degree view of your patients, processes, physicians, and costs without purchasing multiple, disparate solutions, and creating information silos. Our speakers discuss their collective experience in working with organizations to create tailored platforms that provide convenient access to data collected by, and stored in, disparate clinical information systems and enabling that data to be securely used by users throughout the broader healthcare community. Actionable data – available to all users when they need it – serves as a foundation for analysis and decision-making aimed at improving how care is delivered. You can find it online at http://www.informationbuilders.com/webevents/online/24637#sthash.RnwoH27x.dpuf
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Analytics is a key enabler for life sciences and healthcare organizations to create better outcomes for patients, customers and other stakeholders across the entire healthcare ecosystem. While almost two-thirds of organizations across the healthcare ecosystem have analytics strategies in place, our research shows that only a fifth are driving analytics adoption across the enterprise. The key barriers are a lack of data management capabilities and skilled analysts, as well as poor organizational change management. To develop and translate insights into actions that enhance outcomes, organizations will need to collaborate across an expanding ecosystem.
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Cloud computing promises to fundamentally transform the global life sciences industry. But most life sciences organizations have only just started to understand the power of cloud to not only drive efficiency, but also to redefine collaboration, partnering, and business models. Life sciences organizations are hungry for the capabilities that cloud can deliver, to meet new competitive pressures and ever-expanding consumer expectations. This new IBM Institute for Business Value (IBV) Cloud point-of-view (POV) for the life sciences industry explores the opportunities and implications of cloud computing for global life sciences companies. It provides a roadmap to formulate and execute cloud strategies.
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Technology is constantly transforming healthcare for the better, but getting technology right is an understated challenge for the industry. This webinar addresses three of healthcare's top challenges in tapping technology's full potential: cost, privacy and adoption. Experts and providers share tips, strategies and stories to help overcome these challenges to truly harness the power of transformative healthcare technology.
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Poor data governance impacts reputation risk by data breach, by privacy violations and by acting on poor quality data. Furthermore, there are some important differences in what data governance means for big data compared to data governance for operational data. That poor data governance impacts reputation risk means it has considerable implications for the Board of Directors, for whom reputation risk is the number one risk according to Deloitte (2013). This presentation targeting the Board of Directors and the C-Suite and presented at the National Data Governance and Privacy Congress in Calgary, Canada presented some reasons why data governance is critical, from the perspective of both the C-Suite and the Board of Directors. (Also on YouTube at http://youtu.be/QR4KO3Yx0n4)
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MITS Advanced Research Techniques Research Proposal Student’s Name Higher Education Department Victorian Institute of Technology Proposed Title: Data Integrity Threats to Organizations Abstract Data integrity, an integral aspect of cyber security, is identified as the consistence and accuracy that is assured of data in its life cycle, and is an imperative aspect of implementation, design, and utilization of systems which processes, stores, and retrieves data (Graham, 2017). It is estimated that almost 90 percent of the world’s data was generated in the last two year, and this goes to show the rate at which data is being availed. There are various threats associated with data integrity, for example, security, human, and transfer errors, cyber-attacks and malware just to name a few. The purpose of examination of data integrity in the context of organizations and business is due to the impact that it has on the latter’s operations and eventual success. Data integrity is important when it comes to the productivity and operations of an organization, because management make decisions based on real-time data that is offered to them. If the data presented to management is inaccurate due to lack of proper data integrity, then the decisions that they make might have an adverse effect on an organization. For example, if data related to last year’s projections and profits in the finance department is altered in any way, then the decision of making plans in relation to an organization’s financial position might be lead to further losses. Organizations ought to prioritize security measures through there various Information Systems departments or by seeking third party cyber security specialties to protect and mitigate against the threats related to data integrity. Outline of the Proposed Research What are the threats associated with data integrity and the impact they have on organizational productivity and operations? Background Data plays an integral role in today’s business environment especially when most organizations are harnessing the benefits of data to facilitate their decision-making processes. It is through understanding why and how data is important in business that one may also comprehend the importance of ensuring the integrity of this same data is upheld. Most individual think that data security and integrity are one and the same thing, which is not true, as security refers to leaking of information such as intellectual property and healthcare documents, whereas data integrity refers to the process of ensuring whether data is trustworthy to facilitate the decision-making process. Due to the lack of proper systems and structures to ensure that data integrity is at the helm of an organization’s priorities, management has found it difficult to solely rely on data and analytics to facilitate their decision-making process. What this means is that a significant number of businesses are missing out on the advantages accorded through aspects such ...
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It’s been three years since the General Data Protection Regulation shook up how organizations manage data security and privacy, ushering in a new focus on Data Governance. But what is the state of Data Governance today? How has it evolved? What’s its role now? Building on prior research, erwin by Quest and ESG have partnered on a new study about what’s driving the practice of Data Governance, program maturity and current challenges. It also examines the connections to data operations and data protection, which is interesting given the fact that improving data security is now the No. 1 driver of Data Governance, according to this year’s survey respondents. So please join us for this webinar to learn about the: Other primary drivers for enterprise Data Governance programs Most common bottlenecks to program maturity and sustainability Advantages of aligning Data Governance with the other data disciplines In a post-COVID world, data has the power to be even more transformative, and 84% of business and technology professionals say it represents the best opportunity to develop a competitive advantage during the next 12 to 24 months. Let’s make sure your organization has the intelligence it needs about both data and data systems to empower stakeholders in the front and back office to do what they need to do.
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Chapter 1 The Transforming World of Health Information Management The Case for an Enterprise Solution The St. Rita’s Case Who appointed the EIM team? CIO Joan Morton Why was EIM the team appointed? Sort out the root causes of data integrity issues at St. Rita’s Determine the feasibility of developing an enterprise division to assure data integrity The St. Rita’s Case The initial members of the EIM team Denise – HIM professional Bob – Business analyst Linda – Decision support analyst Val – Project manager Shirlee – Clinical analyst Monte – Systems analyst The St. Rita’s Case The EIM team’s charge Study St. Rita’s data problems Recommend new ideas and improvements Assess feasibility of an EIM division Develop an action plan to implement improvements Enterprise Information Management Enterprise information management (EIM) A set of functions used by organization to plan, organize, and coordinate people, processes, technology, and content for managing information as a corporate asset that ensures data quality, safety, and ease of use. Data Governance Data governance (DG) A specific EIM function that supports coordination among all other EIM functions by establishing data standards, policies, procedures, and accountabilities that span the entire organization. Definition of health information Definition of Health Information Definition dependent upon the context Information about health science and health-related issues published by an authoritative source For example, journals, magazines, web sources Secondary patient-related data For example, aggregated data in registries Primary patient-related data For example, specific patient data used for healthcare delivery Electronic Health Records: Differing Definitions EMR – Electronic medical record Electronic record Health-related information about an individual Is created, gathered, managed, and consulted by authorized clinicians and staff within one healthcare organization National Alliance for Health Information Technology Electronic Health Records: Differing Definitions EHR – Electronic health record Electronic record Health-related information about an individual Conforms to nationally recognized interoperability standards Is created, gathered, managed, and consulted by authorized clinicians and staff across more than one healthcare organization National Alliance for Health Information Technology Electronic Health Records: Differing Definitions PHR – Personal health record Electronic record Health-related information about an individual Conforms to nationally recognized interoperability standards Is drawn from multiple sources Managed, shared, and controlled by the individual National Alliance for Health Information Technology Complexity of data flow and retrieval Clinic Appointment and Visit Example Healthcare Data Flow Across Users Healthcare Information Exchanges (HIEs) Federal-state collaboration Enable and expand secure electronic movement and use of ...
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