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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>NHS UK : Appointments Data Analysis</title>
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</head>
<body>
<div class="container-lg p-responsive mt-6">
<article class="markdown-body">
<h1>NHS UK : Appointments Data Analysis</h1>
<h3>📋 Project Brief</h3>
<p>
I conducted an in-depth analysis of NHS UK appointment data (2020-2022) to identify service utilization patterns, assess resource capacity, and investigate missed appointments, which incur significant costs.
</p>
<h3>✅ Objectives</h3>
<p>
The analysis supported key business objectives:
</p>
<ul>
<li>Understanding service utilization trends and patterns</li>
<li>Assessing staff and capacity adequacy across regions</li>
<li>Reducing missed appointments through targeted interventions</li>
<li>Evaluating the value of external data sources for decision-making</li>
</ul>
<h3>🎯 Conclusion </h3>
<p>
The analysis revealed significant opportunities to improve NHS appointment utilization and reduce missed appointments. Weekdays show consistent over-utilization (73% of days exceeding capacity), while weekends are under-utilized.
</p>
<ul>
<b>Key Recommendations:</b>
<li>Increase same-day and next-day appointments to reduce missed rates</li>
<li>Expand telephone and video consultations to optimize resources</li>
<li>Improve weekend utilization to relieve weekday overuse</li>
<li>Implement region-specific interventions for high-miss areas</li>
<li>Use automated reminders for appointments with longer booking windows</li>
</ul>
<hr>
<h3>Project Overview:</h3>
<div>
<table>
<tr>
<th>⏱️ Duration</th>
<th>🏆 Grade</th>
<th>🛠️ Tools</th>
<th>📊 Datasets</th>
</tr>
<tr>
<td><b>6 Weeks</b></td>
<td><b>93% (High Distinction)</b></td>
<td>
<img src="https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=python&logoColor=white" alt="Python">
<img src="https://img.shields.io/badge/Pandas-150458?style=for-the-badge&logo=pandas&logoColor=white" alt="Pandas">
<img src="https://img.shields.io/badge/Seaborn-4EABE5?style=for-the-badge&logo=python&logoColor=white" alt="Seaborn">
</td>
<td>
<b>30 months</b> of appointment data<br>
<b>742.8M</b> appointments analyzed
</td>
</tr>
</table>
</div>
<hr>
<h3>Analytical Approach:</h3>
<h4>1. Data Preparation & Cleaning</h4>
<table>
<tr>
<th>Process</th>
<th>Highlights</th>
</tr>
<tr>
<td><b>Data Import</b></td>
<td>Loaded multiple NHS datasets with different time ranges<br>Consolidated appointment data across regional, national, and duration-specific sources</td>
</tr>
<tr>
<td><b>Data Cleaning</b></td>
<td>
<ul>
<li>Verified data consistency across the datasets</li>
<li>Validated appointment count alignments</li>
<li>Handled duplicate records</li>
<li>Standardized datetime fields for analysis</li>
</ul>
</td>
</tr>
<tr>
<td><b>Data Enhancement</b></td>
<td>
<ul>
<li>Mapped location codes to NHS organizational structure</li>
<li>Added geographical context by linking regions, ICBs, and sub-ICBs</li>
<li>Created derived fields for time and service settings</li>
</ul>
</td>
</tr>
</table>
<h4>2. Exploratory Data Analysis</h4>
<table>
<tr>
<th>Process</th>
<th>Highlights</th>
</tr>
<tr>
<td><b>Location Analysis</b></td>
<td>Analyzed 7 NHS regions with 42 ICBs and 106 sub-ICBs<br>Identified regional disparities in appointment volumes and missed rates</td>
</tr>
<tr>
<td><b>Categorical Analysis</b></td>
<td>
<ul>
<li>Created custom functions to analyze multiple appointment categories</li>
<li>Examined service settings, context types, national categories, and appointment modes</li>
</ul>
</td>
</tr>
<tr>
<td><b>Temporal Analysis</b></td>
<td>Assessed monthly, seasonal, and COVID-related trends<br>Identified impact of lockdown periods on appointment volumes</td>
</tr>
</table>
<h4>3. Advanced Analysis</h4>
<table>
<tr>
<th>Component</th>
<th>Highlights</th>
</tr>
<tr>
<td><b>Missed Appointments</b></td>
<td>Analyzed 30.9M missed appointments (4.16% of total)<br>Identified key correlations with appointment modes and booking intervals<br>Quantified business impact of missed appointments</td>
</tr>
<tr>
<td><b>Capacity Utilization</b></td>
<td>Evaluated daily capacity against the 1.2M appointment benchmark<br>Identified weekday vs. weekend utilization disparities<br>Assessed post-COVID capacity patterns</td>
</tr>
<tr>
<td><b>Multivariate Analysis</b></td>
<td>Combined mode, timing, and regional factors to identify missed appointment patterns<br>Created region-specific insights on appointment behaviors</td>
</tr>
</table>
<hr>
<h3>🗝️ Key Insights:</h3>
<h4>Appointment Distribution</h4>
<ul>
<li>Identified General Practice as the dominant service setting (91% of appointments)</li>
<li>Discovered Face-to-Face (60%) and Telephone (28%) as the primary appointment modes</li>
<li>Face-to-Face appointments decreased while Telephone consultations doubled during COVID</li>
</ul>
<blockquote>
<p><b>Value Discovery:</b> 87.7% of all appointments have booking windows of 0-14 days, with same-day appointments showing the lowest missed rates across all modes</p>
</blockquote>
<div align="center">
<img src="https://github.com/user-attachments/assets/bc91e35a-dba6-4859-9377-6436386a4fa0" alt="NHS Appointment Distribution">
<p><em>Distribution showing significant variations in appointment volumes and types</em></p>
</div>
<h4>Capacity Utilization</h4>
<ul>
<li>Quantified weekday over-utilization with 73% of weekdays exceeding capacity</li>
<li>Identified Tuesday (107.3%) and Monday (103.6%) as peak utilization days</li>
<li>Measured weekend under-utilization with Saturday (9.9%) and Sunday (1.1%) showing minimal use</li>
</ul>
<blockquote>
<p><b>Value Discovery:</b> Weekend appointment expansion could significantly reduce weekday pressure, particularly following post-lockdown peak periods</p>
</blockquote>
<div align="center">
<img src="https://github.com/user-attachments/assets/52ae05a0-7217-406a-97b2-aa41ae8f2646" alt="NHS Capacity Utilization">
<p><em>Utilization trends showing weekday overuse and weekend underutilization</em></p>
</div>
<h4>Missed Appointments</h4>
<ul>
<li>Quantified Missed appointments as 4.16% of all appointments over 30 months of analysis.</li>
<li>Identified Face-to-Face appointments accounting for 78% of all missed appointments</li>
<li>Discovered strong correlation between booking interval and missed rates</li>
<li>Longer intervals showed significantly higher missed rates (9.8% for 28+ days vs. 2.0% for same-day)</li>
</ul>
<blockquote>
<p><b>Value Discovery:</b> Telephone appointments consistently show lower missed rates (1.4-3.3%) across all booking intervals</p>
</blockquote>
<div align="center">
<img src="https://github.com/user-attachments/assets/992b2c80-df7b-49b1-b0ae-d9a37303f43a" alt="Missed Appointments Analysis">
<p><em>Analysis of missed appointments by mode, booking interval, and region</em></p>
</div>
<hr>
<h3>📊 Recommendations to NHS:</h3>
<p>
Based on the comprehensive data analysis, the following strategic recommendations were presented to enhance NHS appointment management:
</p>
<h4>Operational Recommendations</h4>
<ul>
<li>Shorten booking intervals by increasing same-day and next-day appointments to reduce missed rates</li>
<li>Expand remote consultations through telephone and video to capitalize on their lower missed rates</li>
<li>Implement weekend utilization improvements to balance weekday over-utilization</li>
<li>Deploy automated appointment reminders for bookings with intervals greater than 8 days</li>
<li>Develop region-specific interventions targeting the unique patterns in high-miss areas</li>
</ul>
<h4>Strategic Recommendations</h4>
<ul>
<li>Redistribute staff resources to match peak demand periods and underutilized capacity windows</li>
<li>Balance face-to-face and telephone appointment availability based on regional attendance patterns</li>
<li>Consider short-term staff augmentation to address post-COVID demand surges</li>
<li>Improve data collection methods to enable more granular analysis in the future</li>
</ul>
<h4>Technical Recommendations</h4>
<ul>
<li>Create a unified data model linking appointment-level data across all systems</li>
<li>Incorporate population metrics and healthcare provider data for more comprehensive analysis</li>
<li>Extend data collection timeframes to better identify seasonal patterns</li>
<li>Develop advanced predictive models once post-COVID normalization periods provide more stable data</li>
</ul>
</article>
</div>
</body>
</html>