Advanced Computational Analysis of Healthcare Appointments Using Python

 

Project Type: Data Analytics

Tools Used: Python (Pandas, Matplotlib, Seaborn, NLP)

Carried out At: LSE Data Analytics Career Accelerator

OVERVIEW

This project analysed large-scale healthcare appointment data to uncover workload imbalances, scheduling inefficiencies, and gaps in stakeholder engagement.

Using Python (Pandas, NumPy, seaborn, and NLTK), the study combined statistical analysis, natural language processing, and data visualisation to extract insights for improving operational efficiency in healthcare delivery. The findings support data-informed decisions in resource planning and policy design for NHS systems.

TECHNICAL COMPETENCIES DEMONSTRATED

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TECHNICAL COMPETENCIES DEMONSTRATED 〰️

  • Implemented scalable techniques to clean and standardize over 500,000 records, ensuring data integrity and consistency.

  • Conducted statistical and visual analytics to detect temporal patterns, spatial disparities, and service utilization trends.

  • Developed multi-layered visual representations using Matplotlib and Seaborn to facilitate strategic decision-making.

  • Applied sentiment analysis and keyword extraction on Twitter data to gauge public discourse on healthcare services.

  • Engineered reusable Python workflows for automated data ingestion, transformation, and exploratory modeling.

NHS Appointment Scheduling Analysis

1. Data Preprocessing & Standardisation

Established a robust data cleaning pipeline to ensure analytical integrity of large-scale healthcare appointment datasets.

  • Deduplicated records and reformatted time-based fields for temporal analysis.

  • Detected and adjusted statistical anomalies in appointment durations.

  • Applied geospatial normalisation to support regional comparisons.

2. Temporal Trends in Appointment Scheduling

Analysed monthly and seasonal fluctuations to uncover patterns in patient demand and service utilisation.

  • Identified peak service periods and under-resourced time windows.

  • Generated visual timelines to support capacity planning and scheduling optimisation.

3. Sentiment Analysis & Stakeholder Engagement

Used Natural Language Processing (NLP) to analyse public feedback from Twitter on healthcare service quality and accessibility.

  • Extracted key themes and quantified sentiment polarity/intensity across regions.

  • Compared engagement levels to assess variation in public discourse and awareness.

4. Resource Allocation & Efficiency Assessment

Correlated staffing levels with appointment volumes using multi-variable analysis to reveal imbalances in resource distribution.

  • Highlighted regions with mismatches between supply and demand.

  • Provided evidence for optimising workforce deployment in line with patient needs.

Key Outcomes:

  • Identified bottlenecks in NHS scheduling patterns using time-series and geospatial analysis.

  • Applied text analytics to capture patient sentiment, adding a qualitative layer to operational data.

  • Delivered evidence-backed recommendations to support NHS policy and workload distribution planning.