Prior To Completing This Discussion Assignment Students Shou

Prior To Completing This Discussion Assignment Students Should Have R

Prior to completing this discussion assignment, students should have read and understand the concepts discussed in the textbook’s (Applying Quality Management) chapters 9-13. Students should follow these instructions: (30 points) Create an initial post by day 3. The initial post should be at least 200 words long (but it can be longer), and at minimum, it should these guidelines (but more can be discussed if the student desires): (30 of 30) Chapter 10 discusses data analytics. As students make progress through higher levels, data analytics is a topic that will most likely become almost unavoidable. The book describes descriptive analytics as answering these questions: What is happening? How often and where is it happening? What may be causing the results? When should action be taken? For this discussion, select a problem or topic (that is healthcare related) and describe how descriptive analytics could be used to resolve the topic in focus. See an abbreviated example below for assistance.

What is happening?, How often and where is it happening? “Many people today are using Fitbit fitness trackers. The tracker’s built-in goal is for the user to take 10,000 steps per day.” What may be causing the results?, When should action be taken? “How do we know 10,000 steps per day truly makes us healthier? I think we should study some users who take 10,000 steps per day and explore if those users truly are healthier than people who do not take 10,000 steps per day.” The easiest way to complete this portion of the assignment is to think of something that annoys you in today’s healthcare system and focus on how the issue can be resolved using data.

This is a springboard assignment for students who are considering obtaining a Master’s or doctoral degree. Not the problem does not have to be solved in this discussion, but a problem has to be presented along with a descriptive data could be used to solve it.

Paper For Above instruction

In the evolving landscape of healthcare, data analytics has become an essential tool for improving patient outcomes and optimizing healthcare delivery. Descriptive analytics, in particular, helps to understand what is currently happening within health systems by analyzing historical data to provide insights into patterns and trends. For this discussion, I will focus on hospital readmissions, a significant issue in healthcare that affects patient safety, quality of care, and costs.

Hospital readmissions occur when patients are discharged and then admitted again within a certain period, usually 30 days. These readmissions often indicate underlying problems such as inadequate discharge planning, poor follow-up care, or unmanaged chronic conditions. Descriptive analytics can assist healthcare providers by examining large datasets of patient records to identify the frequency and distribution of readmissions across different departments, patient demographics, and diagnoses. For example, analyzing data from electronic health records (EHRs) can reveal which patient populations are most prone to readmission, such as elderly patients with chronic conditions like heart failure or diabetes. It can also highlight temporal patterns, such as increased readmissions during certain months or seasons, and geographic concentrations within specific hospital regions or patient communities.

Understanding where and when readmissions are most prevalent is crucial for developing targeted interventions. Healthcare organizations can use descriptive analytics to evaluate the effectiveness of discharge processes and post-discharge support programs. By examining historical readmission data, hospitals can identify risk factors that contribute to unnecessary readmissions, such as medication non-adherence, lack of social support, or insufficient patient education. This, in turn, allows healthcare providers to implement more tailored care plans, improve communication with patients, and allocate resources effectively to reduce preventable readmissions.

Additionally, descriptive analytics enables continuous monitoring and benchmarking of hospital performance. Comparing readmission rates across different facilities or departments can highlight areas needing improvement and support quality improvement initiatives. For instance, if data analysis shows higher readmission rates among patients with heart failure in a particular hospital, targeted interventions like enhanced follow-up or telemonitoring can be introduced to address specific complexities associated with managing this condition.

In conclusion, employing descriptive analytics in healthcare provides a comprehensive understanding of hospital readmissions, helping providers to recognize patterns and reasons behind readmissions. By leveraging such insights, healthcare organizations can design more effective interventions, promote better patient outcomes, and reduce unnecessary costs, ultimately advancing the quality and efficiency of care delivery.

References

• American Hospital Association. (2020). Hospital Statistics. Chicago: American Hospital Association.
• Das, R., & Asari, V. (2019). Predictive and Prescriptive Analytics in Healthcare. Journal of Healthcare Management, 64(2), 117–131.
• Jencks, S. F., Williams, M. V., & Coleman, E. A. (2009). Rehospitalizations Among Patients in the Medicare Fee-for-Service Program. New England Journal of Medicine, 360(14), 1418–1428.
• Kruse, C. S., & Pichika, M. R. (2018). Healthcare Information Technology and Patient Safety. Journal of Medical Systems, 42(6), 118.
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