Data Mining

Data Mining

Data Mining 150 150 Peter

Data Mining

Write a 5 page paper using APA format answering the following questions:

Data mining helps in workflow design in hospitals and can uncover relationships that are hidden in the complexity of multiple processes.

Explain what relationships would be helpful to the nurse administrator in making quality improvement decisions.
Why is it important for the nurse executive to work closely with the advanced analytics professional?
What is data governance and how and who will monitor?

Sample Paper

Data Mining

Data Mining is among the most useful methods that have established warm reactions in the government, enterprises, healthcare, and private firms. It’s used mainly for understanding big data and analytics for smoothening workflow at the hospital management by assisting nurses and doctors in better serving their clients. Data mining is the process of cataloging via large sets of data to find patterns and trends and form correlations to help solve problems through data analysis (Yang et al., 2020). This process is significant to healthcare as it allows the staff to determine patterns that are then applied to predict the probability of future actions and trends.

Relationships That Would Be Useful To Nurse Administrator in Completing Quality

Improvement Decisions

Quality improvement is a framework used to improve care systematically. Expert decisions are required for quality improvement to appropriately standardize the processes and structure to minimize variation, attain probable results, improving results for patients, health care systems, and organizations. In healthcare, the key purpose of quality improvement decisions is to improve outcomes. Nurse administrators are required to make relationships, for instance, with patients, nurses, and physicians, to ensure quality improvement decisions are completed for excellent results. Any gaps in these particular relationships can adversely influence quality decisions.

One relationship that may be helpful to the nurse administrator in ensuring quality improvement decisions is relationship with patients. This may allow the nurse administrator and nurses to create trust that can help freely express opinions and concerns (Kloutsiniotis & Mihail, 2018). Usually, a trust-based relationship is developed in the direction of an individual. One or more individuals hold an individual’s property subject to particular duties to apply and defend it for the benefit of others. Another relationship is that with physicians. These individuals can develop effective communication relationship that may help allow for stimulating feedback questions that imparts clarification. This communication can raise cross-cultural understanding and collaboration that may help build harmony between administrators and physicians, making the organization’s transactions productive and timely appreciated. Communication teaches cooperation that generally expands to the firm’s other areas. When the nurse administrator and physicians collaborate, processes run more efficiently. Therefore communication relationship remains paramount in keeping steady profitable and solvent due to a calm and motivated team via quality improvement decisions. Besides, relationships with the nurses can be helpful to the nurse administrator in ensuring quality improvement decisions. A nurse administrator and nurses can develop respect to help create a healthy environment in which they feel cared for as individuals. Nurse teams are engaged, cooperative, and committed towards service via excellent decision-making. On the contrary, lack of respect between these individuals stifles teamwork, undermining individual performance.

Why Nurse Executive Requires To Work Closely With Advanced Analytics Professional

Involvement of the nurse executives with the advanced analytics professional can help in the overall system enhancement. A huge amount of data is collected in the healthcare system and then analyzed to develop valuable perceptions. Advanced analytics for one applies health care software systems to deal with the data attained from operational, clinical, and financial areas. The need to use analytical technologies thus help nurse executives simplify enhanced care at minimized cost with greater patient satisfaction. Secondly, the advanced analytic professional acquires healthcare data gathered and applies data management, analysis, and interpretation skills to offer actionable insights. This helps the executive nurse provide the necessary and accurate treatment to patients. Thirdly, by applying big data analytics, the advanced analytic professional helps the executive nurse to consider the newest medical research via databases and make administrative decisions.

Also, in cooperation with advanced analytic professionals, executive nurses can similarly understand how their patients respond to treatment programs and their current situation. Additionally, advanced analytic professionals collaborate information gathered from numerous sources to monitor the effectiveness of their processes (Dubey et al., 2019). As a result, the executive nurse can lead the nursing team in developing clinically necessary and effective diagnosis and treatment plans. The nurse executive can also identify those patients who are likely to have risk to their health. Again, they can come up with specific health programs that may be used to serve the interests of the patients to enjoy enhanced health. In some cases, a patient is readmitted due to relapse, where the advanced analytic professional can find the cause of relapse and make proposals on how to prevent it, which reduces the workforce of the executive nurse. Besides, via the effort of the advanced analytic professional, the executive nurse can identify ineffective processes and programs that do not produce desired outcomes so that they may be eliminated. This implies that only result-oriented programs are put in the health program.

Meaning of Data Governance

Data governance handles the availability, integrity, usability, and security of data in the enterprise system regarding the internal policies and standards that manage data usage (Yang et al., 2019). Data governance is also a method of handling data that allows an organization to collect and secure information and acquire value from that data. Efficient data governance makes sure that data is trustworthy and consistent and is not used in the wrong manner. As defined by the AHIMA (American Health Information Management Association), data governance in health care is the organization’s basis for managing health data from when a patient was admitted until discharged, including research, payment, treatment, government reporting, and outcome improvement. Data governance is directed towards ensuring that data can be trusted by its users, especially in making decisions for patient care.

How and Who Will Monitor Data Governance

According to Abraham, Schneider & Vom Brocke (2019), data governance is monitored by the information management professionals who take responsibility to oversee and develop data governance principles that improve reliability, consistency, and usability of data assets and enhance the EHR interfaces to do away with the unnecessary end-users and remove the challenging workarounds. The main aim is to enhance the efficiency of the staff, create a conducive environment and produce big data which can be used in health decision-making and improve the organization. An example of an information management professional is the Chief data officer, the senior executive whose role is to manage the governance program and is responsible for its success or failure. The chief data officer has other roles: staffing and funding the program, fortifying approval, leading the team in setting up the program, managing its progress, and functioning as an advocate for it within. If the organization lacks a chief data officer, another executive takes the lead and serves the same functions.



Abraham, R., Schneider, J., & Vom Brocke, J. (2019). Data governance: A conceptual framework, structured review, and research agenda. International Journal of Information Management49, 424-438.

Dubey, R., Gunasekaran, A., Childe, S. J., Roubaud, D., Wamba, S. F., Giannakis, M., & Foropon, C. (2019). Big data analytics and organizational culture complement swift trust and collaborative performance in the humanitarian supply chain. International Journal of Production Economics210, 120-136.

Kloutsiniotis, P. V., & Mihail, D. M. (2018). Trust’s mediating and moderating role is the link between perceived high-performance work practices, employee attitudes, and service quality. Employee Relations

Yang, J., Li, Y., Liu, Q., Li, L., Feng, A., Wang, T., & Lyu, J. (2020). Brief introduction of medical database and data mining technology in the significant data era. Journal of EvidenceBased Medicine13(1), 57-69.

Yang, L., Li, J., Elisa, N., Prickett, T., & Chao, F. (2019). Towards big data governance in cybersecurity. Data-Enabled Discovery and Applications3(1), 1-12.