Tools Used: Excel, MySQL, Tableau
Lown Hospital Dashboard - Tableau
The objective of this project is to perform a comprehensive analysis through MySQL of the Lown Hospital Index for Equity 2022 dataset and create a visually appealing and informative Tableau dashboard that showcase my findings. The analysis will focus on key metrics related to equity in healthcare, including Pay Equity, Community Benefit, and Inclusivity. By exploring these metrics, the project aims to gain insights into the extent of fairness, inclusiveness, and community engagement within hospitals.
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Downloaded the dataset and loaded it into Excel. Cleaned the dataset and when I felt it was ready, I converted the file into JSON to export it to MySQL
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Utilized SQL analysis techniques to extract and organize relevant data from the Lown Hospital Index for Equity 2022 dataset.
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Focused on the Equity measures, which consist of Pay Equity, Community Benefit, and Inclusivity indicators.
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Analyzed the Inclusivity aspect by examining income inclusivity, racial inclusivity, and education inclusivity within hospitals.
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Took a deep dive into the Tier 1 - Tier 4 rankings. Identified the top 10 and bottom 10 hospitals based on each ranking, as well as the number of hospitals corresponding to each grade.
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Designed and developed an interactive Tableau dashboard that presents the findings of the analysis in a visually engaging and user-friendly manner.
During the project, I encountered a challenge related to acquiring the complete dataset for analysis. Despite multiple attempts to resolve the issue, which included deleting and re-downloading the dataset, I noticed that I could only access about half of the data. Specifically, out of a total of 4000 rows, I was only able to obtain 1789 rows. This limitation had a notable impact on my analysis as it resulted in a reduced sample size, potentially leading to incomplete or skewed insights. The incomplete dataset hindered the ability to draw accurate conclusions and might have introduced bias in the analysis, thereby compromising the overall validity and reliability of the findings. This challenge highlights the importance of having full access to data sources during a project.
Through the SQL analysis of the Lown Hospital Index for Equity project, several noteworthy findings emerged. Firstly, the dataset contained hospitals of various sizes, with 442 hospitals classified as large, 441 as extra small, 433 as medium, 259 as extra large, and 214 as small. Secondly, California had the highest number of hospitals, totaling 122, followed by Illinois, Ohio, New York, and Texas. In terms of location, the analysis revealed that 1,352 hospitals were in urban areas, while 437 were in rural regions. Additionally, a significant majority of the hospitals were identified as non-profit institutions. Notably, Adventist Health Howard Memorial hospital achieved the top ranking in the Lown Composite category. When examining inclusivity, it was found that a greater number of hospitals received a grade of B. In terms of equity, more hospitals obtained a grade of B or C. These findings highlight the distribution of hospital sizes, geographic distribution, location types, and institutional nature within the dataset, along with the performance rankings for composite, inclusivity, and equity measures.