Dashboard Visualization
A HEALTH ANALYSIS DASHBOARD FOCUSING ON PATIENTS BILLING BASED ON INSURANCE COMPANIES, AND MEDICAL CONDITIONS.
Objectives
The analysis project aims to gather insights into the impacts of the billing process on health outcomes, as well as the preferences of patients regarding hospitals and insurance companies. The analysis is further driven by the need to discover patterns regarding how different genders pay for healthcare services, with a preference for certain selected hospitals.
Secondly, it is driven by the need to discover how hospitals charge different amounts based on patient admission model (elective admission, emergency admission and urgent admission).
Thirdly, the analysis is further compelled to discover how insurance companies cover for the different sets of ailments that patients may have, i.e. in this context, the conditions include arthritis, cancer, diabetes, hypertension, obesity and asthma.
The Analysis utilizes the usage of Microsoft Power BI:
Power Query for Data Cleaning
Data Analysis Expression (DAX) for Data Modelling
Data Visualization tools in the form of stacked BarCharts, Line graphs, and Card Visualizations.
Dataset columns
Methodology
Column data sets are cleaned with Power Query which involves the Trimming of empty rows, and conversion of data type in the right format.
DAX is used to create models for easier relationships between the categorical variables.
Generating relationship between medical condition between age, patient count and medical condition.
Relationship between gender, medical condition and patient count.
Relationship between admission type, medical condition and patient count. This model further highlights reveals the most preferred hospital and the most preferred doctor in the data set.
Relationship between blood type, medical condition and patient count.
Relationship between billing amount and the insurance providers.
Results
Potential Impact of The Analysis
The analysis provides an insight on some of the possible diseases (medical conditions) likely to be billed highly by insurance companies.
secondly, it highlights the preference of some of the notable insurance companies by selected customer base.
Thirdly, it provides an overview on which gender spends more on medical insurances.