SBS MBA Healthcare Data Analytics Assignment

General Instructions

• Submit all assignments by 17th April 2025 via LMS portal.

• Assignment extension requests must be sent to Azra Fatima (AVP Academics) at least 5 days before submission with valid reason and supporting documents.

• Follow APA 7th edition referencing guidelines.

• Work must be original; similarity with other students’ work is not allowed. Plagiarism is an academic offence.

• Revaluation requests must be made within 5 days of grade release.

• Late submissions within 1 week incur a 20% penalty.

• Keep a copy of your assignment in case the submitted version is lost.

Plagiarism Policy

Plagiarism is presenting work from uncredited sources as your own. Examples include:

• Copying another author’s work without acknowledgement

• Cut and paste without citation

• Paraphrasing without credit

• Using structure or presentation order of another’s work

• Submitting third-party work as your own

• Falsifying results

MBA plagiarism tolerance:8%.

• Exceeding tolerance allows a second chance to resubmit.

• Persistent plagiarism may result in a one-grade reduction.

Assignment Details

You are tasked to assume the role of a healthcare data analyst for a GP practice in Bahrain. Using the dataset provided, you are required to analyze electronic healthcare records and provide actionable insights to help management in decision-making.

Tasks Overview

1. Data Cleaning

• Identify generic data issues (e.g., missing values, duplicates, inconsistent formatting).

• Suggest remedies for each issue.

• Identify specific issues within the dataset and propose solutions.

2. Descriptive Analysis

• Perform descriptive statistics on key factors.

• Discuss the distribution of data variables.

3. Visual Insights

• Use pivot tables or charts to analyze relationships:

  • Patient age, gender, diagnosis

  • Diagnosis and medication prescribed

  • Diagnosis and billing amount

  • Billing amount and insurance status

  • Age and doctors’ notes

4. Insurance and Billing Analysis

• Investigate if uninsured patients have higher billing amounts.

5. Regression Analysis

• Examine the impact of diagnosis on billing amount.

• Examine the impact of age on billing amount.

6. Recommendations

• Provide actionable recommendations for improving operations using data analytics.

• Suggest new systems or enhanced functions for the GP practice.

7. Ethics and Data Security

• Discuss healthcare data ethics.

• Explain principles of secure data handling and how they apply to the GP practice.

Suggested Report Structure

1. Title Page

2. Executive Summary

3. Table of Contents

4. Introduction – Introduce the report and its objectives

5. Data Issues and Remedies

   • Generic issues

   • Dataset-specific issues

6. Data Analysis

   • Descriptive statistics

   • Visuals (tables, charts, graphs)

   • Key insights

7. Recommendations – Based on data insights

8. Ethical Principles – Data handling and security in healthcare

Summary of Assessment Requirements

The assessment tasked the student with analyzing a healthcare dataset for a GP practice in Bahrain. The primary objectives were to clean, explore, and analyze the dataset, extract actionable insights, and provide recommendations for improving operations.

Key pointers to be covered in the assessment included:

1. Data Cleaning – Identify both generic and dataset-specific errors, and propose remedies.

2. Descriptive Analysis – Conduct statistical analysis on key variables and discuss distributions.

3. Visual Insights – Use charts, graphs, and pivot tables to examine relationships:

   • Patient age, gender, and diagnosis

   • Diagnosis and prescribed medication

   • Diagnosis and billing amount

   • Billing amount and insurance status

   • Age and doctors’ notes

4. Insurance and Billing Analysis – Investigate whether uninsured patients incur higher costs.

5. Regression Analysis – Examine the effects of diagnosis and age on billing amounts.

6. Recommendations – Suggest operational improvements based on data insights, including new or enhanced systems.

7. Ethics and Data Security – Discuss secure handling of patient data and healthcare data ethics.

The report was to be structured professionally, including a title page, executive summary, introduction, analysis, insights, recommendations, ethical considerations, conclusion, references, and optional appendix.

Approach Taken by the Academic Mentor

The Academic mentor guided the student step by step to ensure clarity, comprehensiveness, and adherence to assessment requirements:

1. Understanding the Dataset and Objectives

   • The mentor explained the purpose of the dataset and the role of a healthcare data analyst.

   • Key performance indicators (billing, diagnosis, insurance status) and variables (age, gender, medication) were highlighted.

2. Data Cleaning

   • Mentored the student to identify generic issues such as missing values, duplicates, inconsistent formatting, and outliers.

   • Guided the student to propose dataset-specific remedies, like correcting incorrect entries, standardizing codes, and handling anomalies.

3. Descriptive Analysis

   • Student was instructed to calculate measures like mean, median, mode, standard deviation, and frequency distributions.

   • Mentor emphasized interpreting these statistics in the context of healthcare operations.

4. Visual Insights

   • The mentor demonstrated creating pivot tables and charts to explore relationships.

   • Visuals were linked to insights, for example, showing trends in diagnosis by age or billing by insurance status.

5. Insurance and Billing Analysis

   • Mentor guided the student to cross-analyze insurance status against billing amounts to identify patterns or disparities.

6. Regression Analysis

   • Step-by-step guidance was given on running regression models to examine the impact of diagnosis and age on billing.

   • Interpretation of regression outputs was explained to connect findings with operational decisions.

7. Recommendations

   • Mentor helped the student translate analytical insights into practical suggestions, such as optimizing billing procedures, enhancing patient tracking, or implementing predictive analytics tools.

8. Ethics and Data Security

   • Mentor emphasized healthcare data ethics, including confidentiality, secure handling, and compliance with data protection regulations.

Outcome Achieved

• The student produced a well-structured, professional report containing:

  • Cleaned and organized dataset

  • Descriptive statistics and visual analysis

  • Regression analysis results

  • Clear, actionable recommendations for GP practice operations

  • Discussion on ethical principles and data security

• Learning objectives covered:

  • Application of data cleaning techniques

  • Conducting descriptive and inferential statistical analysis

  • Data visualization and interpretation

  • Insight generation for decision-making in healthcare

  • Understanding healthcare data ethics and security principles

  • Professional report writing and APA referencing

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