🔦 Disclaimer: I completed the following project as part of the LSE Data Analytics Career Accelearator Course (April 2024 - November 2024)
🔖 Grade 82%
❓ Can NHS GP services achieve their patient-focused and financial goals by better utilising existing resources?
For this project, I worked with real-world NHS data to explore how GP services could optimise resources while balancing patient care and financial sustainability. This was a challenging but rewarding analysis, as I dealt with both structured healthcare data and unstructured social media data.
I analysed three datasets on GP appointments and service performance, along with NHS-related tweets scraped from X (formerly Twitter). My goal was to identify seasonal trends in patient appointments, consultation methods, and wait times while also assessing public sentiment about NHS services.
1. Data Cleaning & Wrangling
- Processed structured NHS GP appointment data (2020–2022) using Python (Pandas, NumPy).
- Removed 21,604 duplicate records and aligned NHS regional codes for accurate geographic analysis.
2. Exploratory & Descriptive Analysis
- Conducted time-series analysis to identify seasonal patterns, wait times, and consultation trends.
- Used Matplotlib & Seaborn for visualising NHS appointment trends.
3. Consultation & Resource Analysis
- Examined face-to-face vs. telephone consultations to assess COVID-19’s impact on patient behaviour.
- Analysed the underutilisation of Primary Care Networks (PCNs) as a potential solution for reducing GP workloads.
4. Social Media Sentiment Analysis
- Scraped and analysed NHS-related tweets by engagement metrics (hashtags, retweets, and favourites) using Pandas to assess public sentiment and engagement.
- Found that NHS-related hashtags were underutilised, highlighting an opportunity to improve social media outreach.
- Identified seasonal appointment fluctuations, with higher demand in autumn and lower volumes in winter due to holiday closures.
- Found that Monday appointments were the highest, likely due to weekend backlog.
- Determined that same-day appointments remained high despite COVID-19, but next-day availability was very low, indicating resource strain.
- Discovered a shift from face-to-face to telephone consultations during COVID-19, with in-person visits slowly recovering by 2022.
- Twitter analysis revealed a lack of NHS-related hashtags in public discourse, suggesting a gap in patient engagement via social media.
- Gained hands-on experience working with large-scale healthcare data, ensuring data integrity and accuracy.
- Developed time-series analysis skills, identifying patterns in appointment volumes and consultation methods.
- Strengthened my ability to analyse public sentiment, using real-world social media data to complement structured datasets.
- Learned how to translate healthcare analytics into recommendations, focusing on improving patient access and resource distribution.