BUSINESS INTELLIGENCE SYSTEMS (7156) ASSIGNMENT 2: FINAL PROJECT REPORT SUBMITTED TO: DR. DALE MACKRELL) GURPREET SINGH SAINI U3095940 5-9-2016   Contents Executive Summary 2 Introduction 2 Development and Implementation 2 User Manual 3 Conclusion 3 Executive Summary The prototype designed is a simple yet resourceful tool to analyse and assess key information in the sector of public health, taking into consideration various determinants affecting health as can be seen nowadays. The main aim of this design is to spread awareness regarding the main determinants- smoking and over- eating, in relation to their impacts in the future health setting. It informs us of key elements of health such as Blood pressure, BMI, Smoking status- onset, Deceased age, cholesterol levels. This data can help predict and give an estimate to help professionals regarding the death rate due to a possible cause which can further help in advising the patients. Introduction The Public health sector is one of the most important and ever growing sectors which also makes use of the latest technology. Not only does this sector focuses on patient-centred care but also requires new information ( such as statistics) to perform informed decision making such as guiding the patients about the future health consequences in regards to their current life style. With the option of this prototype, analysed data is processed into information to guide the health professionals of the current death rate associated to various factors and help them resolve the underlying issues in terms of life style changes. Development and Implementation The healthcare industry does not only consists of doctors and nurses, but also makes hold of research in order to assess, evaluate, plan and implement the processes of care giving and treatment. In order to meet this criteria, a certain level of research is required- similar to one that is generated by Australian Bureau of Statistics (ABS) but on a smaller level accounted by the area the investigation is performed in. Statistics, nowadays, are highly performed in a study to investigate any ongoing changes in the society that are having some impact. Similarly, this prototype is designed to account for any patients that have been assessed by critically analysing the information on their health and lifestyle to help preview an overall public status in regards to health. This not only helps to guide the patient, but also to the policy makers in the health sector by providing them with extensive information that is filtered for their ease. It also allows for strategic planning to be performed by any government organisations including changes in laws by assessing the health patterns that are damaging to the society. The data itself is branched and contains an enormous amount of information, which is crucial, yet needs to be refined further. The data contains information on a person’s health history, their BMI, mortality and several other questions which when seen for a range of people, is difficult to interpret without having a set pattern. This can however, be managed via the use of this prototype which allows critical data to be inserted in the appropriate fields and generate an outcome accordingly. The prototype takes into account the two confounding factors of a persons’ smoke status and their eating patterns which later in their life had detrimental impacts on their health and wellbeing. These two were the root of most of the problems such as cancer along with a high BMI indicating obesity and an increased cholesterol levels. The study divides the results into males and females to analyse and perform a risk assessment by the organisations. The prototype designed for the collection and interpretation of this data is simple yet resourceful containing tables and graphs to assist and provides a client with ease to access the records and find the variance in the changing patterns of health over a community. Set in Microsoft Excel – a basic and work appropriate model has been set to ensure the client gains the information for which they tend to perform the analysis for. User Manual This prototype is set in a way that provides easy access to all the information. Home screen is linked with dashboard which provides the overall information on the data of public sector. Dashboard is then linked with other labels which provides the information on five different types of data that shows what has affected the public sector. These labels are include information about different types of data and labels are named as: Smokers, smoker’s status, cholesterol status cause of death, diseases. 1. The first label is named as “Smokers”, by clicking on this label, it will show graph and data on how many females and males smoke and the grand total of people who smokes. Furthermore, the graph includes slicers which can be seen in the dashboard page. The slicers helps to shorten the information which shows the accurate result of each field. For example: If you click on the “Female” button it will only show the female smokers and if you click on the “Male” button, it will only show how many male smokers are there. 2. The second label is named as “Smokers status”, by clicking on this label, it will show the graph and data on how many people are “Dead” and “alive” after smoking and number of people that are Males and females in both categories. By clicking back on dashboard page, slicers shows more accurate information on the data, one slicer contains information on how many people are dead and alive and the other slicer contains information on different gender. For example: By clicking on “Alive” option in the “Status” slicer, it only shows the status of number of alive people and if you click the female button in the “sex” slicer, it will show the graph with number of female smokers alive. 3. The third label is named as “Cholesterol”, by clicking on this button, it will show information on amount of males and females are dead/ alive due to cholesterol levels. By going back to dashboard page, slicers provide more accurate information, In the status option, It shows that the number of females that are dead due to cholesterol are 859 and number of males are 1063. It also shows that the number of females that are alive due to cholesterol are 1915 and number of males are 1220 and the grand total of number of people both dead and alive that are affected by cholesterol is 5057. 4. The fourth label is named as “causes of death”, by clicking on this label, it will show information on different diseases that caused deaths to both Male and female. By clicking back on dashboard, it shows two different slicers, in one slicer all different diseases are provided and the other slicer contains number of females and males which will show the number of males and females affected by different diseases, which could be causes of the deaths. 5. The fifth label is named as “Diseases”, by clicking on this label, it shows the overall data information which includes count of bp status, count of cholesterol and count of smoking. By clicking back to the dashboard page, it shows information of each values on individual slicers. For example: Smoking, cholesterol and Bps taus and the graph shows all this information on both Males and Females. Conclusion The prototype designed covers a lot of content in a simple space highlighting the important variables that are affecting the overall health patterns in today’s society, making it almost detrimental and significant to emphasize that a change in the healthcare setting is required. It highlights the effect of smoking and over- binge eating and their impacts on a persons’ BMI, cholesterol levels, cardiovascular health and many others. Not only, signifying the confounding factors, has it also given an idea to the government organisations to guide their patients of the risks along with advising limits to their routines. The prototype allows this to be done by making use of and assessing information presented in the tables and charts throughout the various sections. To summarise, the prototype designed may not fully be an accurate model, but gives an estimate for the overall health patterns which can further help in other research models.