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Preface

Chapter 1: Introduction to Healthcard Analytics
What is healthcare analytics?
Foundations of healthcard analytics
History of healthcare analytics
Examples of healthcard analytics
Exploring the softward
Summary
Reference

Chapter 2: Healthcare Foundations
Healthcard delivery in the US
Patient data- the journey from patient to computer
Standardized clinical codesets
Breaking down healthcare analytics
Summary
References and further reading

Chapter 3: Machine Learing Foundations
Model frameworks for medical decision making
Machine learing pipeline
Summary
References and further reading

Chapter 4: Computing Foundations - Databases
Introduciton to databases
Data engineering with SQL - an example case
Case details - predicting mortality for a cardiology pracitce
Starting an SQLite session
Data engineering, one table at a time with SQL
Summary
References and further reading

Chapter 5: 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
Summary

Chapter 6: 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
Summary
References

Chapter 7: Making Predictive Models in Healthcare
Introduction to predictive analytics in healthcard
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
Summary'
References and further reading

Chapter 8: 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
Summary
References and further reading

Chapter 9: The Future - Healthcare and Emerging Technologies
Healthcare analytics and the internet
Healthcare and deep learing
Obstacles, ethical issues, and limitations
Conclustion of this book
References and further reading

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