The assignment brief is attached. Please find. What has to be done exactly is open the attached 1st draft which has an attempted answer to many of the questions. This has to be CORRECTED and BETTERED

Ioannis Mavrikis

Business analytics

An evaluation

BUS750 – Business Analytics

The first task of this report sets out to compute the frequencies of all the men in the data set using ‘age’ as the single variable. The descriptive statistics recorded show the age distribution values of the men that include the mean, range, maximum and minimum and variance and standard deviation.

The graphical representation below is in the form of a histogram, which clearly sets out the distribution of the different ages of the men, and includes the important normal distribution curve. The theoretical curve displays how often an experiment is likely to produce a particular result. As it can be observed, the curve is symmetrical and bell shaped, which means that results will fall near of within the average of the set, but could occasionally deviate from this pattern, in the case of large values for example.

The next stage of the report records the process that is carried out to find the proportion of men that belong to each label of the ‘own health’ self-assessment. The ‘own health in general’ field constitutes 4 main value labels that are recorded in the survey: excellent, good, fair and poor, with an alternative option for don’t know, although the latter constitutes as a missing variable and therefore is categorised as such.

Thanks to the statistics observed in the table which presents the ‘valid percentage’ that each label’s frequency represents compared to the total of 1199, we can deduce that the column ‘valid percent’ shows the proportion of men that belong to each category of ‘own health in general’. This proportional graphical representation is illustrated in the pie chart below.

A numerical summary of the height and weight variables of the men surveyed can be presented in the Descriptive Statistics table that is generated by the SPSS software, as observed below.

The table provides a summary of all the main values of averages, variances and other statistical relevance. Here the mean, range and standard deviation of the two variables can be easily compared numerically.

The next stage of the report involves carrying out a Linear Regression Analysis using SPSS, in order to investigate the relationship between ‘lung function’, quantified by the measure of ‘forced expiratory volume’ AND age of the men. This will provide a statistical response to whether a person’s lung functioning health is in fact related to age, and to what extent.

The computed values provided in the above tables assert two main findings with regards to how lung function is related to a person’s age. The R value of 0.619 in the model summary means that the two variables move very mildly in union. The correlation value of R always lies between -1 and 1, where 0 means there is no correlation at all and the two variables are not related. The 0.619 value for R still reflects that there is some relation between lung function and age, but not a significant one. The R Square value reflects how much of the model can be explained for the linear regression. The relatively small value of 0.383 is the coefficient of determination of the model and therefore shows the percentage variation in y which is explained by the x variables, which is 38% for this case, a relatively weak explanatory value.

The independent sample test is used in this question to find how different average lung functions are of the different social class categories. This is a statistical procedure that tests whether a sample of observations could have been generated by a process with a specific mean, and is therefore used, applying theoretical rules, to prove or disprove a devised hypothesis.

We compared means and chose social class as the grouping variable.

The two value labels for this field were 1. Non-manual and 2. Manual.

The test variable for the T-test was average lung functions (HYFEV).

T-Test

The following rules are to be applied to carry out the statistical analysis:

• Whenever the p-value for the F test is greater than our α value of 0.05, we can assume equal variances, and therefore results on the equal variances assumed row can be assumed.

• Whenever the p-value for the t test is greater than our α value of 0.05, we must ACCEPT the null hypothesis.

The table shows that our p-value for the F-test is 0.379 which is greater than α and therefore we can assume equal variance.

Having assumed equal variances on the table row, we notice how the p-value for the t-test is 0.000 which is less than α and therefore we REJECT the null hypothesis.

This thus concludes that in rejection of the null hypothesis, which stated that the two variables were equal, the average lung functions of the social class categories labelled are significantly different.

Lastly, we use a different data set this time to determine whether there are significant differences between fluoridation treatments in terms of the number of decayed, missing or filled teeth.

We carry out what is known as a non-parametric Kruksal-Wallis test, together with what delivers a one-way ANOVA. It is a non parametric test because it depends on independent samples.

The grouping variable is the type of treatment, represented by its number for each type: 1, 2, 3.

We once again apply the same rules for ANOVA testing:

The following rules are to be applied to carry out the statistical analysis:

• Whenever the p-value for the F test is greater than our α value of 0.05, we can assume equal variances, and therefore results on the equal variances assumed row can be assumed.

• Whenever the p-value for the t test is greater than our α value of 0.05, we must ACCEPT the null hypothesis.

Thus we conclude through the Kruksal Wallis test that values for before and after represents the ranks of the variables and are accounted for in the test statistics. The test of significance shows that there is a significant difference between fluoridation treatments in terms of the number of decayed, missing or filled teeth. The Kruksal Wallis served as a positive framework to elucidate and apply the rules of the test statistics.

This essay will discuss how business analytics can be used to further develop the healthcare system, with a specific focus on the American healthcare system. This essay aims to answer the question of how we are able to leverage the resources available to use within the realm of business analytics. This essay will evaluate the usefulness of business analytics in healthcare, and will highlight that although healthcare reform and development is very possible through business analytics, there are also a number of limitations involved, for example, as will be analysed further throughout this essay, managerial issues, data collection methods, quality of data and privacy concerns. This essays main focus will be in understanding how business analytic can be applied in the real world, in an attempt at healthcare reform and what limitations may arise in adopting these methods throughout the healthcare system. The American healthcare system has, for the longest time, lacked resources, and has been a late contender to adopting information systems in their practices. Both these factors have caused a deficit in the healthcare system, both financially, in terms of resource allocation, but also in the sense of efficiency and performance. The HITECH Act, which states that every hospital should be using an electronic health record, which will be referred to as an EHR, has allowed a 44% increase in the number of hospitals using a form of HER in the course of only four years (Witten, 2019). The next step following this advancement, is to further develop the methods by which data is collected, and analysed, in order to improve the efficiency and effectiveness of the healthcare system.

Since the essential information pieces are being instituted, Analytics can, and should, assume a vital job in the change of American human services into a proficient, esteem driven framework. By putting resources into the innovation of the healthcare system, and by moving the focus away from profit generation, the stage is set for the utilization of cutting edge Analytics. In this sense, patient wellbeing is the main aim, rather than financial profit.

While healthcare has taken longer than different businesses to consolidate the utilization of Analytics, such appropriation is drastically changing the conveyance of healthcare, in order to improve the efficiency and effectiveness of such services. This essay will discuss how human services is in a general sense changing in light of the use of investigation. We will likewise talk about how data is gathered, sorted out, and examined, just as the difficulties confronting the boundless appropriation of investigation in healthcare. We will likewise talk about administrative issues and how investigation can create an important yield for associations and people alike. Finally, we will close with explicit precedents representing the use of Analytics to human services conveyance. We will utilize precedents from the perception of information in quality improvement, hereditary qualities, relative viability, ceaseless illness databases, catastrophe arranging, and resource following to exhibit how the use of examination to social insurance is improving how medicinal services are conveyed and to show the one of a kind Analytical issues it raises.

Certain methods seem to present a particularly beneficial approach to healthcare reform, the following will be examples of such approaches. Dashboards and control charts help identify anomalies in data, which could indicate improvements in a process, a patient’s health, the standard of equipment used, or, similarly, areas that need to be improved (Ofori-Boateng, 2019). Categorisation through genetics has been a particularly beneficial step in applying business analytics to the healthcare field. Patients that are more at risk of certain ailments due to their genetics, are grouped off, which allows easier access to patient records and earlier detection of disease. This solution does provide a whole host of alternative concepts to consider, mainly the ethical. For example, how much control should a patient and medical practitioner have over the genetic information collected? (Ofori-Boateng, 2019)

Thirdly, the chronic disease database. This advancement shifts the healthcare system, and the American healthcare system specifically, from one of just detecting illness, to one that adapts, evolves and learns about illnesses and patients. The chronic disease database allows for a system to be to more user-friendly and interactive by creating personalised profiles that allows the patient to regain control over the expression of their symptoms, and proposed treatments. Again, this works to create a more patient centric healthcare system (Rtmd.org, 2019).
The fourth example of analytics playing an important role in healthcare reform is its impact on disaster response. Disaster response could refer to anything from natural disasters, to issues regarding the hospital building, and its available resources. Having live, real time data on what is occurring externally and internally regarding the hospital and its patients, would enable the hospital and healthcare system to provide the necessary resources. A system that is able to track these changes and match them with the demanded resources, is not only efficient, by can save lives. An interesting example of how business analytics is slowly becoming more mainstream, is social media and influenza outbreaks and pandemics (Ofori-Boateng, 2019). Individuals use social media to track trends and patterns, which they will then, naturally, respond to. Analytical tools can be used not only in the improvement of data collection and storage as aforementioned, but also how this data is used to forecast and predict future events, and the subsequent demanded resources (Rtmd.org, 2019).
Another element of healthcare that would benefit from utilising analytical models, is the patient flow. The flow of patients in and out of hospitals has been a concern for the healthcare industry for a very long time, and systems are looking to be improved in order to improve patient waiting times and experience. Emergency departments, since utilising business analytic, have used predictive models to prepare for excessive patient waiting times, crowding, or issues regarding insufficient numbers of staff. Healthcare is, by nature, and over time, becoming more time-dependent and analytics plays a vital role in ensuring the smooth running of the procedure, the patient flow and that patients with life threatening illnesses are treated as soon as possible to ensure lower mortality rates (Ofori-Boateng, 2019).

Analytics is now, more than ever, meandering its way into the healthcare system, but for good reason. As this essay has attempted to highlight, the opportunity to improve patient care, experience, disaster response, and the efficiency of the healthcare system as a whole, seem to be within the realm of possibilities available with utilising information technology and systems, with the healthcare system. Privacy concerns regarding who gets to access these systems and the data collected from them, the concern of collecting high quality data and communicating this effectively to the parties involved, are all prevalent, however, as technology and information systems continue to expand and evolve, it is likely these problems, will also find their solution, in business analytics.

References

Ofori-Boateng, C. (2019). Utilizing Business Analytics In The Healthcare Industry. [online] Go.christiansteven.com. Available at: https://go.christiansteven.com/bi-blog/utilizing-business-analytics-in-the-healthcare-industry [Accessed 26 Apr. 2019].

Rtmd.org. (2019). [online] Available at: http://rtmd.org/uploads/2014/09/JHM594_07_undergrad-essay-Wills.pdf [Accessed 26 Apr. 2019].

Witten, B. (2019). The HITECH Act and Electronic Health Records. [online] Health IS Technology Blog. Available at: https://health.usf.edu/is/blog/2018/02/13/the-hitech-act-and-electronic-health-records [Accessed 26 Apr. 2019].