top of page
hand-businesswoman-touching-hand-artificial-intelligence-meaning-technology-connection-go-

Healthcare Data Analysis and Visualization using Tableau

Introduction

In the healthcare industry, data-driven decision-making is becoming increasingly important. With huge amount of data available in Healthcare Industry, it is crucial to have tools to explore, analyze and communicate insights from the available data. One of such tool is Tableau which is a powerful data visualization and analysis software that plays a crucial role in healthcare data exploration and decision-making. By using Tableau, healthcare organizations can turn their data into actionable insights that can be used to improve patient care, early detection of diseases, reduce costs, and make more informed decisions. There are three main types of data used in this industry which are clinical data, financial data, and administrative data. In this blog I would like to share how Tableau can be used to analyze the clinical Data and its applications and benefits for the target industry.


What is Clinical Data?

Clinical Data is collective data obtained through ongoing patients care as part of diagnosis and treatment. This may include patient information such as demographics, vital signs and laboratory results. Clinical data can be used to improve patient outcomes, early detection of diseases, enhancing quality of care and predicting patient risk.


Clinical Data Analysis with Tableau

Tableau offers a wide variety of visualization types like bar charts, pie charts, line charts, maps and tree maps. By using these visualization types healthcare providers can easily communicate insights and identify trends and patterns in the data. Below I will be showing some basic visualization charts using the Sepsis Dataset.


  1. Bar Chart



Source: Author


The above bar chart shows the count of onset sepsis patients by ICU Length of Stay. Here, each bar represents the volume of patients in an interval of 16 hours of ICU stay. In this chart we get the insights that at the end of 16 hours there were 591 onset patients and the patient count decreasing gradually. So we can easily predict that there was maximum number of onset sepsis patients between 16-32 hours of ICU Length of Stay.


2. Donut Chart


Source: Author


This donut chart shows the patient count distribution of Sepsis Patients with different phosphate levels(Lab Results). The inner circle shows the total number of sepsis patients. Here we get the insights that Hypophosphatemia is more commonly observed in Sepsis Patients as compared to Hyperphosphatemia.


3. Tree Map Chart



Source: Author


This tree map chart shows the patient count of Sepsis Patients with age range of 10 years. Here we get the insights that there are more numbers of sepsis patients between the age group of 61-80 years.


By studying the patterns and trends from the above charts, the healthcare professionals can make more informed decisions and improve patient care.


Applications of Clinical Data Analysis using Tableau

1.Improving patient outcomes: By analyzing data on patient treatments and outcomes, healthcare providers can identify best practices and make informed decisions to enhance patient care.


2.Predicting patient risk: Advanced analytics can identify patients at high risk of developing certain conditions, enabling early intervention and preventive care.


3.Reducing costs: Data analysis can help healthcare providers optimize resource allocation, reduce waste, and lower costs.


Benefits of Clinical Data Analysis Using Tableau

1.Real-time data analysis: Tableau allows for real-time data analysis, enabling healthcare providers to make decisions based on up-to-date and relevant information. This is particularly useful in emergency situations where quick decisions are required based on accurate information.

2.Interactive visualizations: Tableau's interactive visualizations help healthcare providers gain deeper insights into complex datasets. The tool enables users to easily identify trends, patterns, and outliers in data, making it easier to identify opportunities for improvement in patient care.

3.Enhanced patient care: With Tableau, healthcare providers can make more informed decisions, leading to improved patient outcomes and supporting evidence-based healthcare practices. For instance, healthcare providers can analyze patient outcomes data to identify best practices and areas that require improvement.



Conclusion:

Tableau is a powerful tool for healthcare analysis, providing actionable insights to improve patient outcomes and operational efficiency. Its real-time analytics empower healthcare professional to make informed decisions and enhance patient care. Its ability to connect to a wide variety of data sources and its powerful data preparation and cleaning capabilities make it an ideal tool for exploring and visualizing healthcare data.


Resources:


Thank You for reading.



104 views0 comments

Recent Posts

See All

Commentaires

Noté 0 étoile sur 5.
Pas encore de note

Ajouter une note
bottom of page