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Healthcare Analytics Made Simple

  • Course Code: Data Science - Healthcare Analytics Made Simple
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 2 Days Audience: This course is geared for Python experienced developers, analysts or others who are intending to add a touch of data analytics to your healthcare systems and get insightful outcomes

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Healthcare Analytics Made Simple for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who are intending to add a touch of data analytics to your healthcare systems and get insightful outcomes 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

In recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practicing doctors and data scientists. It equips the data scientists’ work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes. 

This course is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed. 

Working in a hands-on learning environment, led by our Healthcare expert instructor, students will learn about and explore: 

  • Perform healthcare analytics with Python and SQL 
  • Build predictive models on real healthcare data with pandas and scikit-learn 
  • Use analytics to improve healthcare performance 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

Gain valuable insight into healthcare incentives, finances, and legislation 

Discover the connection between machine learning and healthcare processes

Use SQL and Python to analyze data

Measure healthcare quality and provider performance

Identify features and attributes to build successful healthcare models 

Build predictive models using real-world healthcare data

Become an expert in predictive modeling with structured clinical data

See what lies ahead for healthcare analytics

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to know how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them. 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Introduction to Healthcare Analytics 
  • What is healthcare analytics? 
  • Foundations of healthcare analytics 
  • History of healthcare analytics 
  • Examples of healthcare analytics 
  • Exploring the software 
  1. Healthcare Foundations 
  • Healthcare delivery in the US 
  • Patient data – the journey from patient to computer 
  • Standardized clinical codesets 
  • Breaking down healthcare analytics 
  1. Machine Learning Foundations 
  • Model frameworks for medical decision making 
  • Machine learning pipeline 
  1. Computing Foundations – Databases 
  • Introduction to databases 
  • Data engineering with SQL – an example case 
  • Case details – predicting mortality for a cardiology practice 
  • Starting an SQLite session 
  • Data engineering, one table at a time with SQL 
  1. Computing Foundations – Introduction to Python 
  • Variables and types 
  • Data structures and containers 
  • Programming in Python – an illustrative example 
  • Introduction to pandas 
  • Introduction to scikit-learn 
  • Additional analytics libraries 
  1. Measuring Healthcare Quality 
  • Introduction to healthcare measures 
  • US Medicare value-based programs 
  • The Hospital Value-Based Purchasing (HVBP) program 
  • The Hospital Readmission Reduction (HRR) program 
  • The Hospital-Acquired Conditions (HAC) program 
  • The End-Stage Renal Disease (ESRD) quality incentive program 
  • The Skilled Nursing Facility Value-Based Program (SNFVBP) 
  • The Home Health Value-Based Program (HHVBP) 
  • The Merit-Based Incentive Payment System (MIPS) 
  • Other value-based programs 
  • Comparing dialysis facilities using Python 
  • Comparing hospitals 
  1. Making Predictive Models in Healthcare 
  • Introduction to predictive analytics in healthcare 
  • Our modeling task – predicting discharge statuses for ED patients 
  • Obtaining the dataset 
  • Starting a Jupyter session 
  • Importing the dataset 
  • Making the response variable 
  • Splitting the data into train and test sets 
  • Preprocessing the predictor variables 
  • Final preprocessing steps 
  • Building the models 
  • Using the models to make predictions 
  • Improving our models 
  1. Healthcare Predictive Models – A Review 
  • Predictive healthcare analytics – state of the art 
  • Overall cardiovascular risk 
  • Congestive heart failure 
  • Cancer 
  • Readmission prediction 
  • Other conditions and events 
  1. The Future – Healthcare and Emerging Technologies 
  • Healthcare analytics and the internet 
  • Healthcare and deep learning 
  • Obstacles, ethical issues, and limitations 
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