Task 1: Background Information
SAS visual analytics help in health data analysis which is very essential for every organization that focuses on health issues with a specific focus. These analytic data visualizations to the health sector play an important role in order enhance general productivity of the organization. As a result, increases efficiency and effectivity of the organization. The advantage of the SAS analytic data visualizations is that it produces valuable and deep health insights that boosted the organization’s reputation. Moreover, other organizations or business firms dealing with matters of healthcare by use of the SAS analytic data visualizations in one way or the other remain competitive and this enhances its overall performance.
For the purposes of accomplishing the task, the “READMIT-HISTORICAL” dataset from teradatauniversitynetwork.com has been considered. Recently, the SAS visualization analytics has gained preferences in many organizations including health departments due to its ability to display data visualization that makes decision-making process easy. Furthermore, these analytics show the trend and discover hidden patterns within a dataset. In US, it is important to analyze health data in general because of its expensive in nature. Again, the development and updating of health strategic plans addressing health issues depend on the timely analysis and availability of data.
For to note, Big Data analysis while utilizing huge data of numerous hospitals, (June 2011 – July 2012) is one of the tools that helps in evaluating quality of service provided to the population by any the health industry. Additionally, visualization of health conditions via SAS Visual Analytics helps to predict the future of U.S Health Services. Findings from these analytical work from SAS help the US health departments to make effective decisions given the fact that they are dealing with the lives of people. Hence, this information will also help to ensure that general health operations are continuously improved.
There are several benefits of using SAS data visualization such as having a glance to detect any changes within the healthcare provision by just interrogating results. However, these results must be availed within a specific timeline, (Yang, Li, & Zhang, 2018). Just to mention, dashboards have been developed as one of the data visualizations measures that can be manipulated to uncover other findings especially hidden factors affecting the general performance of health organizations. Through these dashboards, the management team of health departments can come up with specific recommendations to improve general health services being offered to the public. Generally, data visualizations have been viewed to be better than static charts given their ability to demonstrate additional interactions, unlike static charts that do not have real-time interactions of the dashboards. In most cases, statisticians and those who do not have any analytical backgrounds have continued to like data visualizations due to its interactive measures that change the general perception of many analytics, (Hepworth, & C Canon, 2018).
Task 4: Justification
For effective data visualizations to be used, majority of the statisticians have preferred data visualization charts and maps that can easily be understood by anyone dealing with the lives of people like US health departments. These make the interpretations of the findings easy and quick thus the development of recommendations for better healthcare improvement possible.
On this note, some of the data visualizations used include; a horizontal graph, line graphs, frequency polygons, and a bar graph. These graphs play an important role since the graphs can easily categorize different variables to indicate the general distribution of frequencies of the results. These frequencies distribution can either be numbers or percentages or both but can easily be understood by the management.
Moreover, the horizontal graphs could ensure that the numerical numbers of different categories are shown within the bars. These graphs are also relevant since it gives summaries that can be easily interpreted by just having a visual form of the dataset without requiring the viewers to have statistical backgrounds to understand the results being displayed, (Dobbs, 2018).
While comparing the results of the horizontal graphs, line graphs, frequency polygons, and a bar graph to the results normally indicated in the tabular format, these graphics normally give better data visualization hence it is easy to give estimations of very essential values at a glance. In addition, the graphs can be modified depending on the analyst property preference by highlighting different numbers that probably requires attention. Furthermore, labeling and drawing of the bars can be done using different colors which can easily be seen at a glance without struggling to understand the presentations.
Additionally, the accuracy level of a horizontal graph, line graphs, frequency polygons, and a bar graph are at par. This is because it is easy to check by visualizing the results of the calculations are right or not now that results are not crowded as in the table formatting so any form or errors can easily be detected even by the people who are not professionals in SAS data analytics. Moreover, the horizontal graphs and bar charts can easily be arranged in ascending or descending order hence leading proportional results of the variable of interests within the bars can be seen at a glance and this enhances levels of accuracy now wrong findings while searching through the bars are likely not to be presented.
Finally, these horizontal graphs, line graphs, frequency polygons, and a bar graph have been used in several organizations especially those that are dealing with big datasets like in the case of business intelligence, computational statistics, and machine learning to display various findings by use of summaries in dashboards which can be easily interpreted by the management levels without necessarily making the management to acquire specific or statistical knowledge to understand and interpret the results. As a result, has made the processes of developing effective recommendations that improve general healthcare possible.
Task 5: Discussions
The results show that COPD is the diagnosis group which is the least popular disease whereas Congestive Heart Failure (CHF) is the most popular disease. Therefore, majority of the US populations suffer from Congestive Heart Failure (CHF) than any other condition. As a result, there is need to train more medical specialists in Congestive Heart Failure (CHF).
From the findings, it is clear that the Acute Myocardial Infarction (AMI) and COPD have the HIGHEST ICU DAYS for male’s region 11 while Congestive Heart Failure (CHF) is HIGHLY DIAGNOSED in region 10 and 11. This implies that the are some kind of geographical settings in region 10 and 11 that may be exposing the populations to Acute Myocardial Infarction (AMI) and Congestive Heart Failure (CHF).
Furthermore, health education and promotion programs should be periodically done to target more males than females since they have Congestive Heart Failure (CHF) and COPD conditions in higher proportions than their female counterparts.
Other conditions diagnosed to be affecting the citizens of the US are the Pneumonia Organism Unspecified and Chronic Bronchitis. On this note, there is need to sensitize the populations against these conditions and raise awareness on the best prevention strategies.
Given the fact that the Heart department has the highest number of patients show that the government needs to employ more heart specialists in these departments now that they tend to see many patients compared to other departments.
Furthermore, the highest admission of patient’s number is in March 2012 while October – January 2012 recorded low admission means that there are some hidden factors that lead to these high number of admission cases in these months.
Again, now that the highest Patient numbers come from the DELRAY BEACH compared to other cities need specialize attention so that the population can be sensitized on best prevention and management strategies to address various causes of admission in the hospital.
From the map, it seems that data collection has been based in only 10 states: Florida, Alabama, Georgia, Texas, Virginia, Illinois, Mississippi, Arkansas, Missouri, and Tennessee. This means that the findings cannot be generalized into other settings of US due to smaller sampled sizes depicted.
Task 6: Executive summary
Visualization of huge datasets using SAS Visual Analytics can be used by the U.S. Department of Health and Human Services to control hazards via predictive forecasting and helps in generating real-time responses for immediate action. Congestive Heart Failure (CHF) HIGHLY DIAGNOSED in regions 10 and 11. The least popular disease is COPD while the Congestive Heart Failure (CHF) is the most popular disease. Heart Failure; 85, 138 cases are the most popular diseases while disorders of disease electrolyte; 94 remains the least popular disease. In addition, Pneumonia Organism Unspecified; 31, 245 is the most popular disease under Acute Myocardial Infarction (AMI) while Bronchopneumonia; 94 is the least popular disease. In COPD, Chronic Bronchitis; 75, 538 has been identified as the most popular disease whereas Others; 48 is seen to be the least popular disease. The highest admission of patient’s number is in March 2012 while October – January 2012 recorded low admission. Highest Patient numbers: DELRAY BEACH compared to other cities. Data collection has been based in 10 states: Florida, Alabama, Georgia, Texas, Virginia, Illinois, Mississippi, Arkansas, Missouri, and Tennessee. The highest count of patient numbers by hospital is from Florida. The lowest count is from Tennessee.
Include training of Congestive Heart Failure (CHF) in most learning institutions so that more doctors can be trained to tackle the disease due to its popularity. Train more doctors to specialize in Pneumonia Organism Unspecified and Chronic Bronchitis since they are some of the most popular diseases affecting the US general population. Increase the number of staff being deployed in the city of Delray Beach.
Conclusion
Acute Myocardial Infarction (AMI) and Congestive Heart Failure (CHF) were highly diagnosed in regions 10 and 11. Congestive Heart Failure (CHF) is the most popular disease. In addition, Heart Failure has been identified as the most popular diseases while disorders of disease electrolytes have been identified as the least popular disease. In addition, Pneumonia Organism Unspecified is the most popular disease under Acute Myocardial Infarction (AMI) while Bronchopneumonia is the least popular disease. Furthermore, Pneumonia Organism Unspecified and Chronic Bronchitis are the most popular diseases affecting the US general populations. The highest admission of patient’s number is in March 2012 while October – January 2012 recorded low admission. DELRAY BEACH had highest Patient numbers compared to other cities. Data collection has been based in 10 states: Florida, Alabama, Georgia, Texas, Virginia, Illinois, Mississippi, Arkansas, Missouri, and Tennessee. The highest count of patient numbers by the hospital is from Florida while the lowest count is from Tennessee.
Anderson P. The beverage alcohol industry’s social aspects organizations: a public health warning. Addiction 2004; 99: 1376– 7.
Babor T., Caetano R., Casswell S., Edwards G., Giesbrecht N., Graham K., et al. Alcohol: No Ordinary Commodity—Research and Public Policy. Oxford: Oxford University Press; 2003.
Dobbs, D, “Rational functions whose graphs intersect their horizontal asymptotes.”. in Far East Journal of Mathematical Education, 17, 2018, 133-162.
Green, Jesse, et al. “The Importance of Severity of Illness in Assessing Hospital Mortality.” Journal of the American Medical Association, January 12, 1990, pp.241-46.
Halpin, K, E Paprocki, & R McDonough, “Utilizing health information technology to improve the recognition and management of the life-threatening adrenal crisis in the pediatric emergency department: medical alert identification in the 21st century.”. in Journal of Pediatric Endocrinology and Metabolism, 32, 2019, 513-518.
Hepworth, K, & C Canon, “Improving Science Students’ Data Visualizations: A STEAM-Based Approach.”. in Dialectic, 2, 2018.
Jendrol’, S, & M Kle????, “On graphs whose line graphs have crossing number one.”. in Journal of Graph Theory, 37, 2001, 181-188.
O’Dowd, A, “Social care can reduce demand on hospitals for end of life care.”. in BMJ, 345, 2012, e6951-e6951.
Pearce N. Traditional epidemiology, modern epidemiology, and public health. Am J Public Health 1996; 86: 678– 83.
Siegel M., Doner L. Marketing Public Health. Strategies to Promote Social Change. Sudbury, MA: Jones & Bartlett; 2004.
The American Journal of Clinical Nutrition, Volume 36, Issue 4, October 1982, Pages 700–708, https://doi.org/10.1093/ajcn/36.4.700
Yang, Y, N Li, & Y Zhang, “Networked Data Mining Based on Social Network Visualizations.”. in Journal of Software, 19, 2008, 1980-1994.
