The Power BI report titled “Hospital Management Report” provides insights into the hospital’s key metrics, including revenue, expenses, patient demographics, admissions, discharges, and department-level performance. This report aims to help hospital administrators and stakeholders make data-driven decisions to enhance operational efficiency, patient satisfaction, and financial performance.

Functional Requirements

1. Dashboard Elements

The report contains the following key dashboard components, which must be implemented and optimized:

a) Revenue and Expenses by Department

  • Bar chart comparing revenue and expenses across different hospital departments (e.g., ICU, Orthopedics, Neurology).
  • Objective: Highlight departments generating profit versus those incurring losses.

b) Total Admissions and Discharges by Month

  • Line chart showing monthly trends in patient admissions and discharges.
  • Objective: Identify seasonal trends or periods of high/low patient flow.

c) Payments Method Distribution

  • Pie chart illustrating the distribution of payment methods, including:
    • Insurance
    • Self-Pay
    • Government
  • Objective: Understand patient payment behavior and evaluate financial coverage.

d) Total Patients by Age Group

  • Bar chart displaying patient count distribution by age brackets (e.g., 0-18, 19-35, 36-50, etc.).
  • Objective: Analyze which age groups are most represented in the hospital’s patient base.

e) Net Profit by Department

  • Bar chart showing net profit for each department, calculated as the difference between revenue and expenses.
  • Objective: Highlight the financial health of individual departments.

2. Data Table

  • Detailed patient-level data table with the following fields:
    • Patient ID
    • Gender
    • Age
    • Department
    • Revenue, Expense, Net Profit
    • Payment Method
    • Satisfaction Score
  • Objective: Allow users to drill down into specific patient records for deeper analysis.

3. Filters and Interactivity

  • Implement slicers or filters to enable users to filter data by:
    • Department
    • Payment Method
    • Age Group
    • Month (for admission/discharge trends)
  • Objective: Provide flexibility in data exploration.

Technical Requirements

  • Data Source: Ensure the report is connected to a live database or a regularly refreshed dataset to keep metrics updated.
  • Calculations: Create DAX measures to calculate net profit, monthly admission/discharge trends, and payment method percentages.
  • Performance: Optimize the report for fast loading and smooth interactivity, especially when filtering large datasets.
  • Security: Implement row-level security (RLS) if the report will be shared with different user roles (e.g., department heads).

User Experience Requirements

  • Layout and Design: Ensure the dashboard follows best practices for visual hierarchy, with intuitive navigation and minimal clutter.
  • Color Coding: Use appropriate color schemes (e.g., red for negative values like losses) to enhance data interpretation.
  • Tooltips: Add tooltips to charts and data points to provide additional context.

Future Enhancements (Optional)

  • Add predictive analytics features, such as forecasting future admissions or financial trends.
  • Include patient satisfaction analysis using sentiment scoring.

Stakeholders

  • Hospital Administrators
  • Department Heads (e.g., ICU, Orthopedics)
  • Financial Analysts
  • IT/Data Management Team

This document outlines the key requirements for the Power BI report to ensure that it delivers actionable insights and supports strategic decision-making for the hospital management team.

Hospital Management Report th1(original)Download

Project Completion Method

This section outlines the project completion method, detailing the steps and tools required, including SQL queries, Power Query, and DAX formulas, to implement and optimize the Power BI Hospital Management Report.

1. SQL Queries for Data Preparation

Data preparation starts with fetching and transforming raw data from the hospital database. SQL queries will be used to extract relevant tables and fields for the report. Below are sample SQL queries for each key dataset:

a) Revenue and Expenses by Department

Query to calculate revenue, expenses, and profit per department:

sqlCopyEditSELECT  
    Department,  
    SUM(Revenue) AS Total_Revenue,  
    SUM(Expenses) AS Total_Expenses,  
    SUM(Revenue - Expenses) AS Net_Profit  
FROM  
    Hospital_Financials  
GROUP BY  
    Department;

b) Admissions and Discharges by Month

Query to count admissions and discharges per month:

sqlCopyEditSELECT  
    FORMAT(Admission_Date, 'MMMM') AS Month,  
    COUNT(CASE WHEN Admission_Date IS NOT NULL THEN 1 END) AS Total_Admissions,  
    COUNT(CASE WHEN Discharge_Date IS NOT NULL THEN 1 END) AS Total_Discharges  
FROM  
    Hospital_Patient_Data  
GROUP BY  
    FORMAT(Admission_Date, 'MMMM')  
ORDER BY  
    MIN(Admission_Date);

c) Payment Method Distribution

Query to calculate the distribution of patients by payment method:

sqlCopyEditSELECT  
    Payment_Method,  
    COUNT(Patient_ID) AS Patient_Count,  
    ROUND((COUNT(Patient_ID) * 100.0) / (SELECT COUNT(*) FROM Hospital_Patient_Data), 2) AS Percentage  
FROM  
    Hospital_Patient_Data  
GROUP BY  
    Payment_Method;

d) Patient Age Group Distribution

Query to categorize patients by age groups:

sqlCopyEditSELECT  
    CASE  
        WHEN Age BETWEEN 0 AND 18 THEN '0-18'  
        WHEN Age BETWEEN 19 AND 35 THEN '19-35'  
        WHEN Age BETWEEN 36 AND 50 THEN '36-50'  
        WHEN Age BETWEEN 51 AND 65 THEN '51-65'  
        ELSE '66+'  
    END AS Age_Group,  
    COUNT(Patient_ID) AS Patient_Count  
FROM  
    Hospital_Patient_Data  
GROUP BY  
    CASE  
        WHEN Age BETWEEN 0 AND 18 THEN '0-18'  
        WHEN Age BETWEEN 19 AND 35 THEN '19-35'  
        WHEN Age BETWEEN 36 AND 50 THEN '36-50'  
        WHEN Age BETWEEN 51 AND 65 THEN '51-65'  
        ELSE '66+'  
    END;

2. Power Query for Data Transformation

Power Query will be used in Power BI to clean and transform the extracted data. Key transformations include:

a) Removing Unnecessary Columns

Remove columns that are not required for reporting to enhance performance.
Step: Use the “Remove Columns” feature in Power Query.

b) Data Type Conversion

Ensure that fields like dates, numbers, and text are assigned appropriate data types (e.g., converting age and revenue fields to numerical types).

c) Creating a Date Table

A date table is essential for time-based analyses.

MCopyEditlet  
    StartDate = #date(2020, 1, 1),  
    EndDate = #date(2025, 12, 31),  
    DateTable = List.Dates(StartDate, Duration.Days(EndDate - StartDate) + 1, #duration(1, 0, 0, 0)),  
    DateDimension = Table.FromList(DateTable, Splitter.SplitByNothing(), {"Date"}),  
    AddYear = Table.AddColumn(DateDimension, "Year", each Date.Year([Date])),  
    AddMonth = Table.AddColumn(AddYear, "Month", each Date.Month([Date])),  
    AddMonthName = Table.AddColumn(AddMonth, "Month Name", each Text.Start(Date.ToText([Date], "MMMM"), 3)),  
    AddDay = Table.AddColumn(AddMonthName, "Day", each Date.Day([Date]))  
in  
    AddDay

d) Merging Queries

Join datasets using keys like Department, Patient_ID, and Date to create a unified dataset for Power BI visualization.

3. DAX Measures and Calculations

DAX will be used to create measures that enhance data analysis and visualization in Power BI.

a) Net Profit Calculation

DAXCopyEditNet Profit = SUM(Hospital_Financials[Revenue]) - SUM(Hospital_Financials[Expenses])

b) Monthly Admissions and Discharges

DAXCopyEditTotal Admissions = COUNT(Hospital_Patient_Data[Admission_Date])  
Total Discharges = COUNT(Hospital_Patient_Data[Discharge_Date])

c) Patient Satisfaction Score Average

DAXCopyEditAverage Satisfaction = AVERAGE(Hospital_Patient_Data[Satisfaction_Score])

d) Payment Method Distribution Percentage

DAXCopyEditPayment Percentage =  
DIVIDE(COUNT(Hospital_Patient_Data[Patient_ID]), CALCULATE(COUNT(Hospital_Patient_Data[Patient_ID]), ALL())) * 100

e) Dynamic Age Grouping

DAXCopyEditAge Group =  
SWITCH(  
    TRUE(),  
    Hospital_Patient_Data[Age] <= 18, "0-18",  
    Hospital_Patient_Data[Age] <= 35, "19-35",  
    Hospital_Patient_Data[Age] <= 50, "36-50",  
    Hospital_Patient_Data[Age] <= 65, "51-65",  
    "66+"  
)

f) Conditional Formatting for Profit Visualization

Apply conditional formatting in Power BI visual settings to highlight negative profits in red and positive profits in green.

4. Testing and Validation

After completing the data modeling and report creation, perform the following tests:

  • Data Validation: Cross-check data in Power BI with the source SQL tables to ensure accuracy.
  • Performance Testing: Optimize DAX measures and Power Query steps to reduce report refresh time.
  • User Testing: Gather feedback from hospital administrators and end-users to ensure the report meets business requirements.

5. Report Publishing and Maintenance

  • Publishing: Deploy the final report to the Power BI Service and configure scheduled data refresh.
  • Access Control: Implement row-level security (RLS) to restrict data access based on user roles.
  • Maintenance: Regularly review and update SQL queries, Power Query transformations, and DAX measures to accommodate changes in data or reporting requirements.

sample data file for the Hospital Management Report, which includes the following sheets:

  1. Patient Data: Contains information about patients, including PatientID, Gender, Age, Department, Revenue, Expense, Net Profit, Payment Method, and Satisfaction Score.
  2. Admissions & Discharges: Monthly summary of total admissions and discharges.
Hospital_Sample_DataDownload

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