NHS Missed Appointments Analysis 🏥 This project is a deep-dive analysis into the significant issue of 'Did Not Attend' (DNA) appointments within NHS England. Using official data, this analysis uncovers key patterns behind the 16 million+ appointments missed annually, which cost the NHS over £620 million a year.
The goal is to move beyond looking at just the total numbers and instead identify actionable insights that could help improve efficiency and patient care.
🚀 Key Insights My analysis revealed three major trends hiding within the data:
-
The Postcode Lottery is Real 📍 There are stark regional disparities in DNA rates across England. A patient's location significantly impacts the likelihood of them missing an appointment. For instance, London's DNA rate (5.6%) is 75% higher than the rate in the East of England (3.2%).
-
The Saturday Spike 📅 Patient attendance patterns change dramatically over the weekend. The analysis shows that the DNA rate on a Saturday (8.2%) is nearly double the average weekday rate (~4.1%). This highlights a specific operational challenge for weekend services.
-
A Problem of Scale 📈 Missed appointments are a chronic and systemic issue, not a one-off problem. The data shows that the NHS consistently loses over 1.2 million appointments every single month, leading to a massive and continuous drain on financial and clinical resources. The total for the 12-month period analysed was 16.0 million wasted appointments.
🛠️ Tools Used This analysis was conducted using the following tools:
Python 💻: For data cleaning, manipulation, and analysis.
Pandas 📊: For efficient data handling and transformation.
Plotly 📈: For creating interactive and insightful data visualizations.
📂 Data Source The data used in this project is official 'Did Not Attend' (DNA) appointment data published by NHS England.
💡 Conclusion This project demonstrates how data analytics can be used to identify specific, actionable inefficiencies within public services. By understanding where and when appointments are most likely to be missed, targeted strategies can be developed to reduce no-shows, save public money, and ultimately improve patient care.