SBS Healthcare Data Analytics in Health Insurance Assignment

Assignment

Brief

You are taking on the role of a healthcare data analyst for a GP based in Bahrain. The dataset provides a snapshot of electronic healthcare records. The head data analyst from the GP practice has nominated you to use the dataset provided in order to analyse the data and extract relevant insights that should help management in decision making.

First, you will have to clean the dataset, identify all errors and discuss how to remedy each one of these. You should discuss generic issues that can be encountered with datasets and how to remedy these followed by the specific issues in the dataset.

Next, you should start carrying out the following types of analysis on the dataset given:

• Carry out descriptive statistics analysis on the key factors and discuss their distribution.
• Use pivot tables and/ or charts to show insights in the dataset on: - patient age, gender and diagnosis;
  • diagnosis and medication prescribed 
  • diagnosis and billing amount 
  • billing amount and insurance status 
  • age and doctors’ notes 
• Analyse Insurance Status and High Billing Amounts to check if uninsured patients have higher costs. 
• Carry out regression analysis on:
  • impact of diagnosis on billing amount 
  • impact of age on billing amount

Based on the insights extracted from the analysis above, provide recommendations for the GP practice on how to improve its operations with the use of data analytics. Discuss new systems or updated functions of systems that could be used for the GP practice. Discuss healthcare data ethics, secure handling of data principles and how these should be applied to the GP practice.

Summary of Assessment Requirements

The assignment requires the student acting as a healthcare data analyst for a GP practice in Bahrain to analyze a dataset of electronic healthcare records and derive insights that support managerial decision-making.

The assessment includes the following core tasks:

Data Cleaning

• Identify generic data quality issues (missing values, duplicates, inconsistencies, outliers).
• Identify specific errors in the provided dataset.
• Explain how each issue can be corrected or prevented through data cleaning techniques.

Data Analysis

The student must perform multiple analytical tasks such as:

1. Descriptive Statistics
   • Summary statistics on key variables
   • Interpretation of distributions
2. Exploratory Insights Using Pivot Tables/Charts
   Analysis must cover relationships between:
   • Patient age, gender, diagnosis
   • Diagnosis vs. medication prescribed
   • Diagnosis vs. billing amount
   • Billing amount vs. insurance status
   • Age vs. doctors’ notes
3. Insurance Status & High Billing Analysis
   • Determine whether uninsured patients incur higher costs.

4. Regression Analysis

   • Impact of diagnosis on billing amount
   • Impact of age on billing amount

Recommendations & Ethical Considerations

• Provide data-driven recommendations to improve GP operations.
• Suggest new systems or enhancements in current health information systems.
• Discuss healthcare data ethics, security principles, and safe data handling practices.

How the Academic Mentor Guided the Student 

Step 1: Understanding the Brief and Structuring the Report

The mentor helped the student break the assignment into clear sections:

1. Introduction
2. Data Cleaning
3. Descriptive Statistics
4. Pivot Table & Chart Insights
5. Insurance & High Billing Analysis
6. Regression Analysis
7. Recommendations
8. Data Ethics & Secure Handling
9. Conclusion

This structure ensured clarity and logical flow.

Step 2: Preparing the Introduction

The mentor advised the student to:

• Introduce the role of a healthcare data analyst.
• Briefly describe the purpose of analyzing electronic health record data.
• Outline what each section of the report will address.

This created a strong foundation for the overall analysis.

Step 3: Data Cleaning Guidance

The mentor explained:

• The difference between generic dataset issues (missing data, outliers, typos, inconsistent formats) and dataset-specific issues.
• How to document each problem clearly and propose solutions such as:
  • Imputation
  • Removing duplicates
  • Standardizing formats
  • Using validation rules

The student was encouraged to present the cleaning process step-by-step to show analytical reasoning.

Step 4: Conducting Descriptive Statistics

The mentor guided the student to:

• Calculate mean, median, mode, ranges, standard deviation for key variables.
• Interpret what these statistics reveal about patient demographics and billing behavior.
• Present findings clearly in tables.

Step 5: Creating Pivot Tables and Visual Insights

The mentor instructed the student to generate pivot tables and charts exploring:

• Age & gender patterns across diagnoses
• How medications differ by diagnosis
• Variation in billing across medical conditions
• Billing patterns for insured vs. uninsured patients
• Relationship between age and doctors’ notes

The focus was on visual clarity and linking insights back to practical implications for the GP practice.

Step 6: Insurance Status vs. High Billing

The mentor asked the student to:

• Compare average billing for insured and uninsured patients
• Identify whether uninsured patients drive higher costs
• Present findings in charts for easy interpretation

This helped connect data with financial decision-making.

Step 7: Regression Analysis

The mentor guided the student to:

• Set billing amount as the dependent variable
• Use diagnosis and age as independent variables
• Interpret coefficients, significance, and model fit
• Explain what the regression implies for cost drivers

This demonstrated the student’s ability to apply statistical modelling.

Step 8: Recommendations & System Improvements

The mentor helped the student translate insights into actionable recommendations, such as:

• Streamlining billing processes
• Enhancing diagnostic coding accuracy
• Implementing automated dashboards
• Using EHR analytics for resource planning

The mentor emphasized that recommendations must be directly linked to earlier findings.

Step 9: Healthcare Data Ethics & Security

The mentor instructed the student to discuss:

• Confidentiality
• Data minimization
• Patient consent principles
• Secure storage and access control
• Ethical use of patient data for decision-making

This ensured the assignment aligned with healthcare regulatory standards.

Step 10: Writing the Conclusion

The mentor guided the student to summarize:

• Key analytical findings
• How data insights support better operational decisions
• The importance of ethical data practices in healthcare settings

This provided a strong closing section.

Final Outcome and Learning Objectives Achieved

Outcome

The student produced a structured, clear, and well-supported analytical report that successfully:

• Cleaned and prepared the dataset
• Conducted descriptive, exploratory, and regression analyses
• Derived meaningful insights
• Proposed actionable recommendations
• Addressed ethical and security considerations in healthcare data use

Learning Objectives Covered

• Understanding common data quality challenges
• Applying descriptive and inferential analysis techniques
• Using pivot tables, charts, and regression modelling
• Interpreting healthcare datasets to support clinical operations
• Demonstrating problem-solving skills through data insights
• Understanding ethical and secure data handling practices
• Communicating findings in a professional healthcare context

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