[Recommended] Islam Et Al

[Recommended] Islam Et Al

each question half page and one reference

  

Q1

It is important to understand when considering the topic this week that big data, predictive analytics, and machine learning are not the same thing, though many people seem to use them interchangeably (Mester, 2020). Big data refers to a large number of data points, of which all are analyzed, as opposed to simply analyzing a sample population of the data. Predictive analytics then are the analysis of all of that data, and machine learning is the computer algorithm’s ability to learn from the predicted analysis versus the actual results of any data application.

Big data analytics has the potential to assist decision making in healthcare that can lead to decreased costs and increased patent safety (Islam et al., 2018). When looking at Sugar Land Hospital (SLH), one can see how data analytics have led to improvements in their organization. SLH (2016) argues that they “practice evidence-based medicine with a relentless focus on quality and Patient Safety, resulting in national awards and recognition” (Sugar Land Hospital, 2016, p. i). They believe that “without data, accurate, timely, valid and effective decisions cannot be made, and accountability cannot be executed” (Sugar Land Hospital, 2016, p. 18). However, simply because they use data to execute decisions does not necessarily mean that they use big data analytics. SLH does speak of large volumes of data though when they stated that “given the wealth of data information [they] receive from [their greater health systems] extensive IT capability, [SLH] has established defined processes for leadership […] to use data for tracking organizational performance, daily operations, decision-making, and transformational change through Innovation” (Sugar Land Hospital, 2016, p. 18). SLH’s use of data has helped them improve processes and decrease costs across a variety of areas in their organization (Sugar Land Hospital, 2016).

One additional way that SLH can use data analytics that has been proven to be helpful with other organizations includes ways to predict the number of patients visiting each department during specific times. This can help them better manage staffing resources and lead to reduces labor costs (Hajjar, 2021). This is a simple application of data analytics that could result in improved efficiencies for the organization and lead to lower costs.

Q2

he concepts of big data analytics (BDA) and other data analytics techniques all have a similar goal of running a situation and evaluating the best outcomes that will give a positive impact. “Big data describes data that are “generated from an increasing plurality of sources, including Internet clicks, mobile transactions, user-generated content and social media as well as purposefully generated content through sensor networks or business transactions such as sales queries and purchase transactions” (Lehrer, Wienek, Vom Brocke, Jung and Seidel, 2018, p. 428). Insurance companies have been able to use BDA with electronic records for customers’ vehicles. These devices track data on average speed, use of brakes and more statistics. This data and innovative process helps determine insurance rates for safe drivers. For Memorial Hermann Sugar Land Hospital, they have also used BDA for their innovative processes. For instance, they used the BDA technique, predictive analytics, to project growth in IP admission, surgeries, outpatient visits and EC visits from 2012 to 2018. Additionally, they used BDA to determine the root causes for incidents and injuries at their facilities. They developed an intervention to reduce injuries which resulted in a significant rate decrease in incident cases from 2011 to 2016. 

There are many data analytics techniques that can be used to improve an organization including: stream analytics, web analytics, predictive analytics and rule-based system (Lehrer, et al., 2018). A data analytic technique known as predictive analytics is effective and actually used innately by people on a daily basis. Mester (2016) provides a great example of this technique which includes an individual deciding which line to join when checking out at a grocery shop. The individual goes through a mental process when making this decision that analyzes which line will be the fastest based on past experiences and predictors. For Memorial Hermann Sugar Land Hospital, they can definitely use this, if they haven’t already to enhance performance. This can be applied to the data of readmissions. For Memorial Hermann, they can determine the reasons for readmissions, analyze how they can prevent those problems and choose solutions that will result in a positive impact, in this case, a decrease in readmissions. Memorial Hermann performs below the US Top 10% (Memorial Hermann Sugar Land Hospital – Application, 2016). Similarly, doctors and employees would use this technique every day in their decision to provide service and care to patients across the healthcare organization. 

Overall, BDA and the several technology techniques are very effective tools in improving innovation processes, performance and operations at any organization. Memorial Hermann has seen success with these techniques and any industry would benefit from it.

Q3

The case study by Hostetter (2019) discusses the organization, Charleston Area Medical Center (CAMC) Health System, their implementation processes, key measures and results on how they have improved in performance over the years. The medical center is made up of a General Hospital, Memorial Hospital and Women and Children’s Hospital with over 5,000 employees. The improvement projects that were implemented have a focus on targeting populations based on the Hospital Quality Alliance (HQA) reports (patients with acute myocardial infarction (AMI), heart failure (HF) and other conditions). There have been over 100 quality improvement projects that were initiated. The steps that Charleston Area took included in each of these projects was to use improvement tools to examine quality problems, collaborate in multidisciplinary teams, keep a focus on evidence-based processes of care, supporting improvement through “change agents” which were nurses who educate and coordinate the improvement processes and finally collecting performance data and evaluating. The results were phenomenal; from Q4 of 2003 to 2007, they improved composite scores in AMI, HF pneumonia and surgical infection. For AMI, the score went from 90 to 98. 

Some of the stakeholders that would be affected by this would be patients especially, clinical staff, the HQA organization, CAMC administration and medical staff officers, and competing hospitals in the region. Competitors would be forced to implement processes as well to stay competitive with Charleston Area. Patients would see higher levels of success and treatment for these conditions and Charleston would see significant increases in customer satisfaction, trust and coming back to their facilities for service. Lastly, the administration and medical staff officers would be recognized for leading these improvement initiatives. 

There are several types of data that were compared and analyzed to assess the competitiveness and success of CAMC. Some of these types of data include: quality benchmarking (safety), employee surveys, workforce climate, financial performance and market projections (Charleston Area Medical Center Health System – Application, 2015). The employee surveys can help determine how engaged and happy employees are. As we have learned in previous modules and courses, employee engagement is directly related to productivity for a healthcare organization. Employee engagement will directly impact the values of the bottom line of the organization (Kendall, 2017). 

Overall, there is significant evidence that CAMC has been able to improve their medical center operations. Some of the data analytics that were used includes monthly calculations of performance for each of the conditions and tracking how quickly patients were receiving care from time of arrival. A part of their success includes an extraordinary Enterprise Systems Model that is a process to help accomplish work through various systems as provided in Figure 6.1-1 (Charleston Area Medical Center Health System – Application, 2015). It is important to also recognize that CAMC allocates a good amount of time and focus on data and information quality. They ensure that there are integrity checks all across the organization, analysis of investments on new technology, upgrades and applications and finally, evaluating any gaps of error in the IT systems. Successful organizations will have high employee engagement, effective data analysis processes, and a focus on continuous improvement and innovation. CAMC has portrayed each of these and has been a very splendid health system and provider for patients across West Virginia.

Q4

Employee engagement has become an important topic to consider for those in management because organizations with engaged employees, especially those in healthcare, fare much better overall than those that do not have engaged employees. According to Staub, (2021) employee engagement is most evident in retention and turnover rates. It’s important for leaders of an organization to go through certain steps to ensure employees want to stay with the organization and make it possible for employees to become engaged. 

Leaders should be visible- When leaders are visible, it gives employees the opportunity to directly communicate with upper management.

Encourage open communication with employees about how they feel about their jobs

Act on employee feedback- lack of action will deter people from future participation

Leverage resources to assist employees with programs such as tuition reimbursements

HR should be viewed by employees as a helpful resource and not a bad place to go (Staub, 2021). 

In order to build employee engagement, an organization must first build trust with their employees. Employees begin to trust their organization and leaders within the organization when they believe their contribution is noticed and is valued. When employees feel that they are important to their organization, they are more likely to be engaged in their work. Employees that are engaged in their work care about what they’re doing and the outcome of their job. They often find ways to improve on the process and are innovative. Good leaders encourage feedback and innovation within the organization.

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