W3SKILLSET

Requirement Document for Power BI Healthcare Report

1. Introduction

This document outlines the requirements for the Power BI Healthcare Report. The report is designed to provide insights into hospital admissions, patient demographics, medical conditions, medications, and financial data across various dimensions.

2. Purpose

The purpose of this Power BI report is to enable healthcare providers and administrators to analyze key metrics related to patient admissions, diagnoses, and financial performance. It allows decision-makers to track trends, optimize resources, and improve patient care.

3. Key Features & Functionalities

3.1 Filters & Slicers

  • Hospital Selection: Users can filter data based on specific hospitals.
  • Admission Type: Filters for Emergency, Urgent, and Elective admissions.
  • Test Results: Allows selection of specific test results.
  • Medical Condition: Enables filtering by disease or condition.

3.2 Key Performance Indicators (KPIs)

  • Year-to-Date (YTD) Metrics: Comparison of the current and previous years’ total patients and revenue.
  • Quarter-to-Date (QTD) & Month-to-Date (MTD) Metrics: Patient count and revenue trends over quarters and months.

3.3 Demographics Analysis

  • Total Patients by Age Group: Breakdown of patient count across different age groups (<25, 26-40, 41-60, 60+).
  • Total Patients by Gender: Male vs. Female distribution.
  • Total Patients by Blood Type: Insights into patient blood types and their distribution.

3.4 Insurance & Financial Analysis

  • Total Patients by Insurance Provider: Analysis by insurance type (e.g., Cigna, Medicare, Blue Cross, UnitedHealthcare, Aetna).
  • Revenue by Insurance Provider: Breakdown of financial contribution from different insurance providers.
  • Total Amount by Age Group & Gender: Financial performance across demographics.
  • Total Amount by Medication: Analysis of revenue generated from medications.

3.5 Medical Conditions & Medications

  • Total Patients by Medical Condition: Breakdown of patients diagnosed with conditions like Arthritis, Asthma, Cancer, Diabetes, Hypertension, and Obesity.
  • Total Patients by Medication & Gender: Distribution of prescribed medications across genders.

3.6 Admissions & Trends Analysis

  • Total Patients by Date of Admission: Trends in hospital admissions over the years.
  • Financial Trends by Date of Admission: Revenue analysis based on admission dates.

4. Data Sources

The Power BI report is expected to pull data from:

  • Hospital EHR systems (Electronic Health Records).
  • Insurance providers and claim data.
  • Pharmacy and medication records.

5. User Roles & Access

  • Hospital Administrators: Full access to all metrics and financial reports.
  • Doctors & Medical Staff: Limited access to patient demographics and medical conditions.
  • Financial Analysts: Access to revenue and insurance-related insights.

6. Report Visualization & Interactivity

  • Bar Charts: Used for age group, gender, and insurance provider analysis.
  • Line Charts: Trends in admissions and revenue over time.
  • Pie Charts: Gender and insurance provider distribution.
  • Tables & Matrices: Detailed breakdowns for patient count and financial performance.

7. Performance Considerations

  • The report should handle large datasets efficiently.
  • Filters and slicers should have minimal impact on performance.
  • Data should refresh automatically based on predefined schedules.

8. Security & Compliance

  • Adherence to HIPAA regulations for patient data privacy.
  • Role-based access control to ensure data confidentiality.
  • Data encryption for secure storage and transmission.

9. Future Enhancements

  • Integration with real-time hospital admission data.
  • Predictive analytics for patient admission trends.
  • Enhanced drill-down functionalities for deeper analysis.
Helthcare ProjectDownload

1. Data Extraction (SQL Queries)

Data is extracted from multiple sources, including hospital databases, insurance providers, and pharmacy records. The extraction process ensures that the necessary tables are retrieved for analysis.

1.1 SQL Queries for Data Retrieval

Patient Admissions Data

sqlCopyEditSELECT 
    PatientID, HospitalID, AdmissionDate, DischargeDate, AdmissionType, 
    Gender, Age, BloodType, InsuranceProvider, MedicalCondition
FROM 
    HospitalAdmissions
WHERE 
    AdmissionDate >= DATEADD(YEAR, -5, GETDATE()); -- Last 5 years of data

Financial Data by Insurance Provider

sqlCopyEditSELECT 
    InsuranceProvider, SUM(TotalAmount) AS TotalRevenue, COUNT(PatientID) AS TotalPatients
FROM 
    BillingRecords
GROUP BY 
    InsuranceProvider;

Medication Prescription Data

sqlCopyEditSELECT 
    MedicationName, COUNT(PatientID) AS TotalPrescriptions, Gender
FROM 
    PharmacyRecords
GROUP BY 
    MedicationName, Gender;

2. Data Transformation (Power Query in Power BI)

Power Query is used to clean, transform, and shape the extracted data before loading it into Power BI.

2.1 Key Data Cleaning Steps in Power Query

  • Handling Missing Data: Fill null values, remove duplicates, and filter irrelevant data.
  • Data Type Formatting: Ensure consistency (e.g., Date columns are in proper date format, numerical values are set as decimals).
  • Merging Queries:
    • Join Admissions Data with Billing Records using PatientID.
    • Merge Pharmacy Records with Patient Data for medication analysis.
  • Creating Calculated Columns:
    • Extract Age Groups from patient data using Power Query functions.
    • Create Custom Admission Type Labels for better readability.

Example Power Query Transformations

Creating an Age Group column in Power Query:

MCopyEditAgeGroup = 
if [Age] < 25 then "Less than 25" 
else if [Age] >= 25 and [Age] <= 40 then "26 to 40" 
else if [Age] > 40 and [Age] <= 60 then "41 to 60" 
else "60+"

Merging Admissions Data with Insurance Data:

MCopyEditMergedTable = Table.NestedJoin(Admissions, "PatientID", Insurance, "PatientID", "InsuranceData", JoinKind.LeftOuter)

3. Data Modeling & Calculations (DAX in Power BI)

DAX (Data Analysis Expressions) is used for creating calculated measures and columns in Power BI for deeper insights.

3.1 Key DAX Calculations

Total Patients Calculation (YTD, QTD, MTD)

DAXCopyEditTotalPatientsYTD = CALCULATE(COUNT(HospitalAdmissions[PatientID]), DATESYTD(Calendar[Date]))
TotalPatientsQTD = CALCULATE(COUNT(HospitalAdmissions[PatientID]), DATESQTD(Calendar[Date]))
TotalPatientsMTD = CALCULATE(COUNT(HospitalAdmissions[PatientID]), DATESMTD(Calendar[Date]))

Total Revenue by Insurance Provider

DAXCopyEditTotalRevenue = SUM(BillingRecords[TotalAmount])

Percentage of Patients by Gender

DAXCopyEditPctFemale = DIVIDE(COUNTROWS(FILTER(PatientData, PatientData[Gender] = "Female")), COUNTROWS(PatientData), 0)
PctMale = DIVIDE(COUNTROWS(FILTER(PatientData, PatientData[Gender] = "Male")), COUNTROWS(PatientData), 0)

Rolling 12-Month Admissions

DAXCopyEditRolling12MonthAdmissions = 
CALCULATE(
    COUNT(HospitalAdmissions[PatientID]),
    DATESINPERIOD(Calendar[Date], MAX(Calendar[Date]), -12, MONTH)
)

4. Visualization & Report Development in Power BI

Once the data is cleaned and transformed, Power BI visualizations are created. The following elements are included:

4.1 Report Pages

  • Admissions Overview: Yearly, quarterly, and monthly trends.
  • Demographics Analysis: Gender, age group, and blood type distribution.
  • Financial Performance: Revenue trends by insurance provider.
  • Medical Condition & Medication Insights: Condition-wise patient count and prescribed medications.

4.2 Key Visualizations

  • Bar Charts: Patient count by age group, gender, and blood type.
  • Line Charts: Trends in admissions and revenue.
  • Pie Charts: Distribution of insurance providers.
  • Tables & Matrices: Financial data and admission details.

5. Performance Optimization

To ensure efficient report performance:

  • Data Compression: Reducing dataset size by filtering unnecessary columns.
  • Aggregations: Pre-aggregating data at the SQL level instead of performing heavy DAX calculations.
  • Indexing: Optimizing SQL queries using indexing for faster data retrieval.
  • Incremental Data Refresh: Loading only new data to improve report load time.

6. Security & Compliance

  • Role-Based Access Control (RBAC): Ensure different users have access to specific data.
  • Data Encryption: Sensitive patient data is encrypted before storing.
  • Compliance with HIPAA: The report ensures patient privacy regulations are followed.

7. Deployment & Maintenance

  • Publish Report to Power BI Service: Share reports with relevant stakeholders.
  • Scheduled Data Refresh: Automate daily or weekly updates.
  • Error Handling & Logging: Implement logs to track data refresh failures.

Conclusion

By following the above methodology, the Power BI Healthcare Report ensures accurate, efficient, and interactive reporting that supports healthcare decision-making. Let me know if you need further refinements.

sample data files:

  1. Patient Admissions Data – Includes patient details, admission/discharge dates, diagnoses, and insurance providers.
  2. Billing Records – Contains billing details by insurance provider, including amounts and payment status.
  3. Pharmacy Records – Lists prescribed medications, dosages, frequencies, and prescribing doctors.
patient_admissionsDownload
billing_recordsDownload
pharmacy_recordsDownload

Leave a comment

Your email address will not be published. Required fields are marked *