Healthcare Workshop

Overview

This repository contains a collection of Jupyter Notebooks designed to demonstrate data generation, validation, and MongoDB-based operations for a healthcare system. The main goal is to generate patient and appointment data while handling complex MongoDB schema transformations and relationships.

Prerequisites

Ensure that you have the following installed:

• Python 3.x
• Conda package manager (or miniconda, anaconda)
• MongoDB Atlas account or local MongoDB setup.

MongoDB Credentials

The connection to MongoDB requires a URI string that is provided via an environment variable. You should set your MongoDB connection string in your environment:

export MONGO_URI='your-mongo-uri'

Alternatively, for ease of use in Jupyter Notebooks, you can set the variable directly within the notebooks.

Conda Environment Setup

1. Create a new conda environment:

conda create –name healthcare_env python=3.11

2. Activate the environment:

conda activate healthcare_env

3. Install required packages:

pip install pymongo Faker jupyter

4. Launch Jupyter Notebook:

jupyter notebook

Notebooks Overview

S1 - Data Generation (Patients)

File: S1 - data-generation-patients.ipynb

This notebook generates random patient data using the Faker library and inserts it into MongoDB. It demonstrates how to generate fields such as patient name, address, contact details, and emergency contact. The main MongoDB collection for storing this data is patients.

• Features:
• Use of Faker to generate realistic patient data.
• Randomized timestamps for record creation and updates.

S2 - Insert Validation Error

File: S2 - insert-validate-error.ipynb

This notebook demonstrates MongoDB insert validation, showcasing how to manage and handle validation errors when attempting to insert data that doesn’t meet the required schema or constraints. An example is provided where incorrect province data leads to an error.

• Features:
• Error handling for incorrect schema values.
• Validation of address data.

S3 - Change Schema

File: S3 - change-schema.ipynb

This notebook illustrates how to change an existing schema in MongoDB. Specifically, it migrates the address field to a new addresses array format, allowing patients to have multiple addresses.

• Features:
• Schema transformation: from a single address to multiple addresses.
• Use of MongoDB updateMany() function to modify existing records.

S4 - Generate Appointments

File: S4 - generate-appointments.ipynb

This notebook generates appointments for patients in the system. It demonstrates creating various appointment types (e.g., consultation, follow-up) and includes additional details such as doctor information, services provided, and location using MongoDB’s document model capabilities.