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Clinical Informatics: Big Data Analysis

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Abstract— The growth of the idea of business intelligence and analysis has emphasize the significance of the set, combination, processing of data and reporting of fundamental knowledge and how this knowledge can assist to make more suitable business decisions, obtain a better understanding of marketplace behaviors and trends. Great growth of the data has enabled us to uncover the hidden knowledge from data. We can use the Big Data analysis for effective decision making in healthcare domain using the existing machine learning algorithms with some modification to it This paper summarizes the role of Big Data analysis in healthcare and various shortcomings of traditional machine learning algorithms.
Keywords— Big Data, knowledge, of Health Informatics, …show more content…

Clinical questions are the large amount of important question level in Health Informatics as it works frankly with the patient. This is where a misunderstanding can arise with the term “clinical” when found in research, as all Health Informatics research is performed with the ultimate goal of predicting “clinical” events (directly or indirectly). This uncertainty is the reason for defining Clinical Informatics as only research which straight uses patient data. With this, data used by Clinical Informatics research has Big …show more content…

Analyzing Big Data of this scale has only been possible extremely freshly, due to the growing capability of both computational assets and the algorithms which take benefit of these assets. Research on using these tools and techniques for Health Informatics is significant, because this field requires a huge deal of testing and confirmation before new techniques can be practical for making real world decisions across all levels. The way promote for Health Informatics is absolutely exploiting the Big Data fashioned throughout all the a mixture of levels of medical data and discovery ways to finest investigate, extract and reply as several medical questions as

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We must filter and customize that downloaded data for the health conditions that we primarily try to improve. Once data is customized and filtered properly, it gives us “care gaps”. Those care gaps can be easily closed out by accessing patient’s EMR or by referral. This updated data then gets uploaded back to the healthcare insurance company data set for reporting purpose. Data analytics helps health profession close the care gaps and improv care coordination between

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NUR-342 written assignment Need for Change meets the criteria of Outcome 2, because the interventions developed focused on how to meet the patient’s treatment goals. As well as strategies to incorporate multiple healthcare disciplines in the development of a treatment plan. This assignment addressed the need to consider the long-term impact of treatment interventions. NUR 443 PLEET form meets the outcome for knowledge of human diversity because it is a summary of my experiences in the practice-learning environment (PLE). The PLE highlighted the creation and implementation of education strategies for a diverse population.

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Week 9 Overcoming Factors That Impact Informatics Initiatives DB Main Post Informatics impacts the healthcare setting, through the implementation of EHRs. A nurse informaticist not only manages the implementation of technology but follows guidelines set by ANA. Growth in nursing is moving forward as technology is erupting on the scene. The purpose of this paper does nurse impact leadership change for nurses moving into nursing informatics. Can implementation of technological transformation the care of patients, and components of ANCC Magnet health care set?

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Competencies gained from this course will be found in foundations of nursing informatics with benchmarking, outcome probabilities, and expert decision support. Healthcare Project Management (N527PE) Healthcare project management shows methods of managing healthcare projects, aiding the strategic goals of the institution. This course will add to competencies in each of the areas listed under systems life cycle in my previous paper. The skills necessary for performing needs assessments, gap analysis, and feasibility analysis will be acquired as well as creating clinical and data work flow diagrams.

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Therefore, continued success rests heavily upon nurse educator 's to collectively instruct and integrate informatics in the classroom and clinical setting. The key concepts of informatics such as the basic ideas that underlie modern computers, networks, and information be introduced in the curriculum (Camilli,

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Abstract The use of medical AI in the healthcare industry has been a topic of much discussion and excitement in recent years. However, there is a growing concern that the hasty adoption of these technologies may be premature, and that the potential risks and drawbacks outweigh the potential benefits. This paper argues that at this stage, it is advisable to avoid using medical AI in healthcare.

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I am excited to be a part of Nursing Informatics. I was not sure what to expect, as this is my first online class. Since starting, I was suprised to see how Nursing Informatics applies to me at work. As I was taking the assessment, I discovered how much I enjoy working in the Electronic Medical Record. The Cerner program I use at the hospital gives me quicker access to patient information, improves efficiency, and reduces the potential for drug errors.

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With common standards, clinical and patient safety systems can share an integrated information infrastructure whereby data are collected and reused for multiple purposes to meet more efficiently the broad scope of data collection and reporting requirements. Common data standards also support effective assimilation of new knowledge into decision support tools, such as an alert of a new drug contraindication, and refinements to the care

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I will summarize each outcome for the Nursing Informatics specialty. For the intent of this paper I will use outcome and competency interchangeably. The first outcome means the ability to gather healthcare information across the continuum of care; combine and utilize the information gathered to develop a process. Finally execution of that process to evaluate its ability to improve the quality of the healthcare environment. Healthcare managers are constantly assessing patients and collecting information.

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What is a Health Information Specialist? Health information specialist is a blanket term that is applied to a variety of technical positions. Almost all of these jobs involve medical data, information technology, electronic health records and health information management systems. The BLS states that the job outlook for health information technicians is expected to continue growing at 15 percent, which is much faster than average.

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Healthcare data mining is restricted by the availability of data, because necessary inputs for data mining exist in different locations and systems such as laboratory, data banks of hospitals, clinics etc. Further, as large volume of data is involved there is a need to make a data secure and safe from being getting corrupted or inconsistent across the platforms. Moreover, doctors, physicians, healthcare executives are to be convinced about the usefulness of data mining as this is an emerging field and acceptance might take time among health care professionals. Thus, to make the data mining successful cooperation and collaboration of all stakeholders is needed. REFERENCES 1.

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Social media platforms continue to change the healthcare landscape by allowing for users to see a variety of information and points of view. Social media can be used for information and education, marketing, recruiting, and news updates. Healthcare organizations and consumers can utilize social media to improve health and knowledge. Social media can also be used for research, disease tracking, and to monitor trends. This paper discusses the controversial public health topic of abortion and what people are saying regarding it on Twitter.

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Big Data refers to the massive amounts of structured and unstructured data that is collected over time from various internal as well as external sources. Enterprises are facing challenges in integrating these new and different types of data and also turning this data into meaningful information. The data is growing at a tremendous rate due to increase in connectedness of machines and people. Analyzing this data to extract sensible and meaningful insights is a big challenging task; integrating and optimizing this data, storing, organizing and analyzing is a challenge. The Big Data must be captured, stored, organized and analyzed to influence the decision making in any enterprise or business

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As big data things continue to grow in this modern era, today we can learn how to predict or assume anything that will happen in the future with data from the past. This studies known as Predictive Analytics. Predictive analytics combine methods from machine learning, data mining and statistics to find meaning or pattern from a huge volume of data. Tom H Davenport, a senior advisor at Deloitte Analytics has broken down three primer models on doing predictive analytics: the data, statistics, and assumptions.

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