📊 Datix EDA Project 🏥 Welcome to the Datix EDA Project! This project is all about exploring and analysing healthcare incident reporting data (Datix). Our aim is to uncover trends, visualise insights, and apply Natural Language Processing (NLP) to better understand the narrative behind incident reports.
🎯 Project Overview This project focuses on using Exploratory Data Analysis (EDA) techniques to make sense of Datix incident data. Our objectives include: • Analysing the distribution of incident types, such as falls, medication errors, and patient absconsion. • Investigating the severity of harm caused by incidents and identifying any patterns. • Exploring time-based trends to understand when incidents occur most frequently. • Applying Natural Language Processing (NLP) to examine text descriptions of incidents, actions taken, and lessons learned. We also aim to make the analysis interactive and accessible by developing a Streamlit app, which will allow users to explore the data and visualisations in real-time.
🛠️ Setting Up the Project To get started with the project, there are a few important steps:
- Clone the repository: Make sure you have access to all the project files and data.
- Install necessary dependencies: Ensure that your working environment is equipped with the required libraries and packages.
- Set up the environment configuration: This project uses an .env file for specifying paths to the dataset and any necessary configuration details, such as NLP model settings or additional resources. Once the environment is configured, you’ll be able to load the Datix data and begin your exploration.
📊 Data Exploration Goals This project provides a thorough analysis of the following aspects of Datix data: • Incident Categories: Understanding the most frequent types of incidents, such as those related to patient records, falls, and absconsions. • Harm Levels: Examining the severity of incidents and how often they result in minimal or significant harm. • Trends Over Time: Analysing how incident frequencies change over different months, days of the week, or times of day. • Textual Analysis: Applying NLP to dig deeper into the incident descriptions, helping us uncover recurring themes and common issues in reported incidents.
🔍 NLP Features One of the most exciting parts of this project is the use of Natural Language Processing (NLP). By applying NLP, we aim to: • Categorise incident descriptions based on common phrases and terminology. • Identify frequent keywords or patterns in the "What Happened?" and "Lessons Learned" sections of reports. • Analyse sentiment or tone within incident reports to understand the emotional context of the events. This analysis will help provide a richer understanding of the incidents beyond just numbers and categories.
🚀 Future Enhancements The current project lays the groundwork for more advanced analytics. Some potential future developments include: • Advanced NLP Models: Leveraging deeper NLP techniques like BERT or GPT to extract more complex insights from the incident reports. • Predictive Modelling: Using historical data to predict the likelihood of future incidents based on past trends. • Interactive Dashboards: Enhancing the interactivity of the project with more customisable visualisations and filtering options to give users even more control over the analysis.
🌟 Why This Project Matters By understanding the patterns and causes behind healthcare incidents, we can contribute to improving patient safety and overall healthcare quality. The insights gained from this analysis can help healthcare professionals anticipate and prevent future incidents, as well as develop better policies and procedures.
🤝 Contributing to the Project This project welcomes contributions from anyone interested in data analysis, healthcare, or machine learning. Whether you're an expert in EDA, NLP, or Streamlit, or just looking to learn, there are plenty of ways to get involved. Feel free to share your ideas, report issues, or contribute improvements.