Welcome

M1: Python Foundation

M2: Healthcare Data Manipulation with Pandas & NumPy

M3: Healthcare Data Visualization & Reporting

M4: Automation & APIs in Healthcare

M5: Introduction to AI/ML in Healthcare

Module 4: Automation & APIs in Healthcare

Modern healthcare runs on systems—EHRs, lab portals, registries, and regulatory dashboards—all producing and requiring constant streams of data. In this module, you'll learn how to automate repetitive workflows and connect with external data systems using Python. From sending daily reports and parsing physician notes to pulling drug interaction data from APIs like OpenFDA or querying clinical resources via FHIR, you'll gain hands-on experience in making systems talk to each other. You'll also explore tools like regular expressions for extracting structured insights from unstructured clinical text, and Selenium for automating interactions with web-based tools. The goal: free up time, reduce errors, and let data move as fast as your care demands.

Module 4: Automation and APIs in Healthcare with Python

Module Overview

This module introduces learners to automating repetitive tasks and integrating external data using APIs—two critical capabilities in modern healthcare analytics. By automating workflows and connecting to real-time data sources, analysts can streamline reporting, extract insights more quickly, and support operational efficiency.

Learning Objectives

By the end of this module, learners will be able to:

Automate repetitive tasks such as report generation and file handling
Schedule Python scripts using built-in libraries
Use Python to interact with web-based APIs (including healthcare APIs)
Parse and structure API responses (e.g., JSON data)
Apply automation and API skills to healthcare data pipelines

4.1 Introduction to Automation in Healthcare Analytics

Use Cases:

Automatically generating daily or weekly reports
Cleaning and archiving EHR exports
Sending alerts based on threshold breaches (e.g., critical lab values)

Key Libraries:

os, glob – file management
datetime – time-based logic
schedule, time – task scheduling

Practice:

Create a script that checks for new CSV files in a folder and renames them based on today’s date
Write a loop that moves processed files to an archive folder

4.2 Scheduling Tasks and Batch Processing

Use Cases:

Running reports at specific times (e.g., every morning)
Cleaning and transforming data files overnight

Example:

python

CopyEdit

import schedule

import time

def job():

print("Running daily patient summary...")

schedule.every().day.at("08:00").do(job)

while True:

schedule.run_pending()

time.sleep(60)

Practice:

Schedule a task to summarize patient admissions by 8:00 AM daily
Automate a script that combines all Excel reports in a folder into a master workbook

Module 4: Automation and APIs in Healthcare with Python

4.3 Introduction to APIs and Requests

Key Concepts:

What is an API? (Application Programming Interface)
HTTP methods: GET, POST, PUT, DELETE
The structure of JSON responses

Library:

requests

Example:

python

CopyEdit

import requests

response = requests.get("https://api.fda.gov/drug/event.json?limit=5")

data = response.json()

print(data["results"])

Practice:

Query the openFDA API for recent adverse event reports
Extract and display top 3 reported medications and reaction types

4.4 Parsing and Using API Data

Key Concepts:

Navigating nested JSON structures
Converting JSON to a Pandas DataFrame
Handling errors and response codes

Example:

python

CopyEdit

import pandas as pd

results = data['results']

df = pd.json_normalize(results)

print(df[['patient.reaction.reactionmeddrapt', 'receivedate']])

Practice:

Convert a JSON API response to a clean DataFrame
Filter events that involve a specific drug or symptom
Save the final output to a CSV file for further analysis

4.5 Case Study: Automating a Healthcare Dashboard Pipeline

Scenario:

You are asked to automate a reporting pipeline that:

Downloads patient vitals from an API daily
Cleans and formats the data
Saves a summary to a shared folder
Sends a notification if abnormal readings are detected

Steps:

Call a mock API endpoint or use a static JSON
Filter for critical BP or glucose values
Generate summary stats and save to Excel
Send a notification (via print/log, or smtplib for email in advanced setups)

4.6 Summary and Assessment

Summary:

In this module, you have learned to:

Automate repetitive file and reporting tasks
Schedule and manage timed data workflows
Use Python to access and parse data from public APIs
Integrate automation and APIs into healthcare analytics projects

Assessment Tasks:

Write a script that reads patient files every morning, filters high-risk cases, and saves a summary
Use the openFDA or another public API to extract drug-related data and generate a trend report
Automate the entire workflow using schedule and simulate it with print/logs

Automation & APIs Quiz

Disclaimer: The videos included have been thoughtfully selected to support and enrich the learning experience. While not essential to the completion of the course, they offer valuable insights that may deepen your understanding of the module content. Dr Data Insights does not claim authorship or involvement in the creation of these tutorials.