GP Practice Segmentation Using Unsupervised Machine Learning

Project Overview

This project aims to apply unsupervised machine learning techniques, specifically K-Means clustering and Hierarchical clustering, to segment General Practitioner (GP) practices based on Quality and Outcomes Framework (QOF) performance measures and other demographic markers. By identifying meaningful clusters, this analysis will help uncover patterns in healthcare performance and inform targeted interventions.

Objectives

• Explore and preprocess QOF performance data and demographic indicators.
• Apply K-Means and Hierarchical clustering to group GP practices into meaningful segments.
• Evaluate the clustering results to identify patterns and insights.
• Visualize and interpret the clusters for actionable recommendations.

Project Status

🟢 Ongoing (Early Stages)

• Data collection: In progress
• Data cleaning and preprocessing: Not started
• Feature selection: Not started
• Clustering implementation: Not started
• Evaluation and visualization: Not started

Data Sources

The analysis will utilize publicly available data, including but not limited to:

• Quality and Outcomes Framework (QOF) – Performance measures of GP practices.
• Demographic Data – Population characteristics, socioeconomic factors, and regional variations.
• Additional Datasets – Any relevant healthcare indicators.

Methodology

1. Data Preparation

   • Collect and preprocess QOF and demographic data.
   • Handle missing values, outliers, and standardize features.

2. Feature Engineering

   • Select relevant performance and demographic metrics.
   • Apply dimensionality reduction (if needed) to improve clustering performance.

3. Clustering Techniques

   • Implement K-Means clustering to segment GP practices.
   • Use Hierarchical clustering for alternative segmentation and comparison.

4. Model Evaluation

   • Use Elbow Method and Silhouette Score to determine the optimal number of clusters.
   • Compare clustering results and interpret key patterns.

5. Visualization & Insights

   • Generate heatmaps, scatter plots, and geospatial maps to illustrate cluster characteristics.
   • Summarize findings to inform policy recommendations.

This repository is dual licensed under the Open Government v3 & MIT. All code can outputs are subject to Crown Copyright.