Data Modeling in Healthcare Analytics: From Conceptual Design to PostgreSQL Implementation

In healthcare analytics, particularly when analyzing cardiac failure data, the way you organize and model your data determines how quickly, reliably, and meaningfully your insights will be.

Working with datasets that include patient demographics, lab test results, medical history, and prescriptions can get messy without a structured approach.

That’s where data modeling comes in — a systematic process that transforms raw data into well-organized structures, making analysis and visualization easier, especially in tools like PostgreSQL and Tableau.

In this blog, we’ll explore the three core types of data models — Conceptual, Logical, Physical — and see how they apply to a cardiac failure dataset using PostgreSQL as the main database tool.

 1. Conceptual Data Model

 Purpose

• Provides a high-level view of the system

• Focus on what data is needed, not how it’s stored

• Defining entities, attributes, and relationships

 Example: Cardiac Failure Dataset

Let’s imagine a Cardiac Health Monitoring Platform integrating multiple healthcare data sources.

Entities

• Patient (ID, Name, Age, Gender)

• Lab Result (Test Type, Value, Date)

• Medical History (Condition, Diagnosis Date)

• Prescription (Medicine, Dosage, Frequency)

Relationships

• A Patient has many Lab Results

• A Patient has multiple Medical History entries

• A Patient can have multiple Prescriptions

Conceptual Representation:

Entities:

- Patient (Patient id, Name, Age, Gender)

- Lab Result (Test Type, Value, Date)

- Medical History (Condition, Diagnosis Date)

- Prescription (Medicine, Dosage, Frequency)

Relationships:

- Patient → Lab Result (1-to-many)

- Patient → Medical History (1-to-many)

- Patient → Prescription (1-to-many)

 This model helps teams — analysts, doctors, and developers — agree on the structure before creating any actual tables or writing SQL.

2. Logical Data Model

 Purpose

• Adds structure — tables, columns, and relationships

• Still DBMS-independent

• Defines how data is organized logically

Example: Logical Model for the Cardiac Dataset

Patients (patient_id, name, age, gender)

Lab Results (result_id, patient_id, test_type, value, test_date)

Medical History (history_id, patient_id, condition, diagnosis_date)

Prescriptions (prescription_id, patient_id, medicine, dosage, frequency)

Constraints:

patient_id in LabResults → foreign key to Patients

patient_id in MedicalHistory → foreign key to Patients

patient_id in Prescriptions → foreign key to Patients

The logical model ensures referential integrity and establishes clear relationships before we start building in PostgreSQL.

 3. Physical Data Model (PostgreSQL Implementation)

 Purpose

• Final implementation of the model in PostgreSQL

• Defines data types, indexes, constraints, and performance rules

Here’s how our logical model translates into real SQL:

CREATE TABLE patients (

  patient_id SERIAL PRIMARY KEY,

  name TEXT NOT NULL,

  gender TEXT,

  age INT

);

 

CREATE TABLE labresults (

  result_id SERIAL PRIMARY KEY,

  patient_id INT REFERENCES patients(patient_id),

  test_type TEXT,

  value DOUBLE PRECISION,

  test_date TIMESTAMP

);

 

CREATE TABLE medicalhistory (

  history_id SERIAL PRIMARY KEY,

  patient_id INT REFERENCES patients(patient_id),

  condition TEXT,

  diagnosis_date DATE

);

 

CREATE TABLE prescriptions (

  prescription_id SERIAL PRIMARY KEY,

  patient_id INT REFERENCES patients(patient_id),

  medicine TEXT,

  dosage TEXT,

  frequency TEXT

);

-- Performance Indexes

CREATE INDEX idx_labresults_patient_date ON labresults(patient_id, test_date);

CREATE INDEX idx_prescriptions_patient ON prescriptions(patient_id);

These indexes significantly improve query speed when analyzing patient data over time — especially in analytics dashboards or JOIN-heavy SQL queries.

Summary of Data Modeling Layers

Model Type	Focus	Example output
Conceptual	Defines business entities & relationships	Patient → LabResults, MedicalHistory, Prescriptions
Logical	Adds structure and relationships	Tables with foreign keys and constraints
Physical	Implement model with types, indexes	SQL schema optimized for performance

Conclusion

Designing an efficient data model is not just a technical step — it’s the foundation of reliable healthcare analytics.

By following the Conceptual → Logical → Physical modeling approach and leveraging the power of PostgreSQL, you can transform raw hospital data into structured, query-ready databases.

From fast insights in SQL to interactive dashboards in Tableau, strong data modeling ensures your analysis is both accurate and performant — saving time, computation, and effort.