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Module 6 Healthcare Informatics Applications Part I

Module 6 Healthcare Informatics Applications Part I

Data-Based Changes

Module 6 Healthcare Informatics Applications Part I

This paper will explain the basics of big data analytics and how businesses can utilize this resource. An explanation of how big mining methods are heavily used in today’s businesses will be discussed. Examples of how big data analytics are formed from large amounts of data mined by experts to assist in healthcare concepts and changes. How large corporations can gather and compare massive amounts of data patterns from data mining and formulate them into data driven 

systems to improve upon products and services. This paper will describe administrative concepts of business continuity planning structure and program education for the employees. Hebda (2019 p.50), agrees that good information management ensures access to the right information at the right time to the people who need it”. Detailed process of the business preparedness programming will be outlines along with its response to data breaches, cyber threats, and data recovery applications. An educational informatic article will be evaluated as beneficial or a disadvantage to healthcare. Healthcare informatic education technology as a practical tool in the medical field will be reviewed in this paper.

Module 6 Healthcare Informatics Applications Part I

Big Data and Data Mining

An interesting aspect of big data is that it can be versatile in its structure. A business can manual free format information into a data system or use a multi-dimensional information processing system. Healthcare businesses often use a technique called data mining to decipher what level of big data systems to invest into. For many decades organizations have tracked and studied public medical databases in many forms such as, disease prevention, electronic health record management, vaccinations, morbidity, and mortality indexes. The historical data configuration of the multivarious data systems resulted in scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized (Pei, et al., 2019 p.1).  Contemporary research on gathering big data and emerging data mining technology has assisted in developing excellent disease prediction replicas.  Large scale medical public data bases such as the Medicare provider analysis and review file, national practitioner data bank, and the national health care survey can have specific data extracted to determine health outcomes or anticipate specific determinants for doctors and patients. Big data analytics is the way of developing insights through the efficient use of data and application of quantitative and qualitative analysis (Saiful, et al., 2018 p. 2). Organizations who perform data mining can generate accurate details to manage, plan, measure and educate themselves on how efficiently their operation is functioning. Medical corporations can data mine information from large scale insurance companies to decrease errors with payments and reimbursement factors, predict return admission costs, disease prediction, and pharmacovigilance to improve their organization quality of service. According to Saiful, et al., (2018 p. 2) who estimates the application of data mining in medical organizations can save $450 billion each year from the U.S. healthcare system. There is value in big data and data mining throughout healthcare realms. When data mining is employed by businesses their patients receive more affordable medical treatments, prescriptions, and insurance coverages. With the newest streamlined data operating systems in use quicker reports and data collection is available. These large data collection systems can save operational costs with their efficiency and reduction of time elements for the business. There are many algorithms’ organizations can apply with the use of data mining such as clustering, prediction, and classification and decision tree available for solving the problems of big data (Kaur, et al., 2017 p. 1). But with this diverseness of medical information can become challenging to address the ethical, legal, social issues, privacy and security of data mining and inaccurate health information (Kumar, et al., 2017 p.182). Data mining can become a tedious task for organizations to sift through the tremendous volume of data. As data mining systems can retrieve huge amounts of data the organization must have an information technologist to logistically trudge through the missing values in data samples, redundant data, numeric errors, ineffective algorithms, and the update in software compatibilities.

Module 6 Healthcare Informatics Applications Part I

Business Continuity Planning

For all administrators working in the hospital setting, they should lead a team in developing a business continuity preparedness plan. Hospital continuity planning should be focused on internal and external factors that can become enormous in occurrence. However, as analytics and informatics projects often develop organically within medical organizations, the implementation of these projects often develops in response to community needs (Cervone. 2017 p. 78). Medical practices of disaster recovery planning along with principles of business continuity planning consistently evolves in the informatic technology circles. Hospital administration teams should create disaster recovery planning goals and outcomes for their specific needs. Disaster recovery planning from a medical perspective is a forethought on the resumption of informatic electronic organizational activities that maintain the hospitals infrastructure to provide medical cares to the community it serves. Hospital administrators and board directors can meet with its unit managers to create electronic disaster scenarios and organize plans for future events that may occur. The unit managers are encouraged to create detailed procedures that become part of the business continuity preparedness plan (Cervone, 2017. p.80). The concept for this operational continuity plan places substantial importance on technological threats like power or hardware failure. Recognizing and mapping out the medical organization’s resources will assist the planning and true to life electronic dereliction scenarios. Recovery is one part of the hospital risk management process biorhythms for example, the Cybersecurity Framework (CSF), defines five functions that should be included in continuity planning, identify, protect, detect, respond, and recover (Bartock, et al., 2016 p.6). These five functions of the continuity preparedness planning should be interwoven with fundamental risk management procedures and calculated into the drills and preplanned training illustrations. Multifaceted phases should be staged throughout the informatic continuity plan that initiates the problem, strives to improve upon these problems within the electronic system and continually updates the risk management guidebook for operational consistencies over time. For medical informational technologists there are three technology infrastructure areas that are focused on in continuity planning, the network infrastructure, data storage infrastructure, and application infrastructure (Cervone, 2017. P 80). Hospital unit manager teams should conduct bi-annual electronic system failure tests and drills. These testing drills can be appraised on needs and strengths of continuity of systems capabilities when electronic failure does occur. These system checks can also help to refine any data recovery needs that could potentially happen while the informatic structure is down.

Module 6 Healthcare Informatics Applications Part I

Educative Technology

In searching for a healthcare informatic education topic for this research paper I found an article of interest regarding utilizing electronic means for educating healthcare students. The article chosen evaluates healthcare students who learn via electronic online platforms and via traditional methods. With all the healthcare electronic advancements medical students must learn quickly and consistently to stay up to speed with the industry changes. Electronic competencies are ever expanding with the new age technological advancements and products. It is no wonder health care student education is moving towards the informatic direction too. With traditional medical teaching methods, it is required that teaching and learning take place at the same time and point (Pei, et al., 2019 p.1). Online learning platforms do not include the same time and space delivery systems as traditional classroom teaching offers. The article I researched mentions factors that influence healthcare student when using online educational learning technology. Some of the student hurdles with informatic learning can be academic skills, lack of face-to-face social interaction, time management for studies, and consistent internet access. The age of students affects their abilities to adapt to informatic technological learning. The young healthcare students are superior with the online platform learning opposed to older healthcare student who lean more to learning by face-to-face classroom instruction, lectures, and written study guides.  This learning gap steams from generational teaching differences in which the older generation is having to strive thrice as hard to develop electronic habits that their younger parts developed much earlier in their lives. Using online education platform technology is the way of the future with rapid growth in online learning with the potential of limitless students and lower administrative costs (Pei, et al., 2019 p.12). In this new era of online educational learning teachers must develop new skills in teaching healthcare students online. Online teachers creating the class format have a greater audience to reach with the same template to connect to every student. Online healthcare education can be a viable method of learning if the student is comfortable with using computer technology and its applications. 


Module 6 Healthcare Informatics Applications Part I

Today’s hospital administrators and board committees plan for big data and utilize data mining techniques to stay current in healthcare systems. When healthcare data methods are planned and implemented rigorously, they are a valued information processing system. These same administrators and board members can map out medical organizational continuity plans to maintain optimum operational capacity programs within their workforce. Continuing online platforms to train their medical students with fast easy access to health education materials can be a profitable and rewarding conversance. Providing informatic guidance by trained educators within the hospital can motivate healthcare staff to value internal data input, processing, and storage safety.


Bartock, M., Cichonski, J., Souppaya, M., Smith, M., Witte, G., Scarfone, K. (2016). Guide for cybersecurity event recovery. U.S. Department of Commerce. https://doi.org/10.6028/NIST.SP.800-184

Cervone, H. F. (2017). Disaster recovery planning and business continuity for informaticians. Digital Library Perspectives, Article 33(2), 78-81. https://www-proquest-com.aspenuniversity.idm.oclc.org/scholarly-journals/disaster-recovery-planning-business-continuity/docview/1942254847/se-2?accountid=34574

Hebda, T., Hunter, K, Czar, P. (2021). Handbook of Informatics for Nurses & Healthcare Professionals 6th edition.

Kumar, N., & Khatri, S. (2017). Significance of data mining in disease classification and prediction for mining clinical data: A review. International Journal of Advanced Research in Computer Science, Article 8(5), 181-186. http://dx.doi.org.aspenuniversity.idm.oclc.org/10.26483/ijarcs.v8i5.3269

Kaur, N., & Singh, G. (2017). A review paper on data mining and big data. International Journal of Advanced Research in Computer Science, Article 8(4). 

https://www-proquest-com.aspenuniversity.idm.oclc.org/scholarly-journals/review-paper on-data-mining-big/docview/1912629401/se-2?accountid=34574

Pei, L., & Wu, H. (2019). Does online learning work better than offline learning in undergraduate medical education? A systematic review and meta-analysis. Medical Education Online, Article 24(1). http://dx.doi.org.aspenuniversity.idm.oclc.org/10.1080/10872981.2019.1666538

Saiful, I., Hasan, M., Wang, X., Germack, H., Noor-E-Alam, M. (2018). A systematic review on healthcare analytics: Application and theoretical perspective of data Mining. Healthcare, Article 6(2), 54. http://dx.doi.org.aspenuniversity.idm.oclc.org/10.3390/healthcare6020054

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