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Healthcare Foundations

This chapter is mainly aimed at developers who have limited experience of healthcare. By the end of it, you will be able to describe basic characteristics of healthcare delivery in the United States, you will be familiar with specific legislation in the US that is relevant to analytics, you will understand how data in healthcare is structured, organized, and coded, and you will be aware of frameworks for thinking about analytics in healthcare.

Key benefits

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

Description

In recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising 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 book 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. By the end of this book, you will understand 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.

Who is this book for?

Healthcare Analytics Made Simple is for you if you are a developer who has a working knowledge of Python or a related programming language, although you are new to healthcare or predictive modeling with healthcare data. Clinicians interested in analytics and healthcare computing will also benefit from this book. This book can also serve as a textbook for students enrolled in an introductory course on machine learning for healthcare.

What you will learn

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

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Machine Learning for Healthcare Analytics Projects

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J. Weeks May 19, 2020

Great overview of several broad topics. The book was exceptionally well written in an easy to read and referencable manner. I would highly recommend the book to anyone new to the healthcare industry or interested in learning advanced analytics with Python.

Amazon Verified review

M. D. Reader Mar 17, 2020

What the book does well: I thought the most useful part of the book was an overview of the United States Healthcare system and how healthcare has changed after the Affordable Care Act. This includes Meaningful Use for electronic health records and accountable care organizations. The book is also good for some terminology related to medical claims and payer data such as International Statistical Classification of Diseases (ICD) ICD10 diagnosis codes, and Current Procedural Terminology (CPT) for medical procedures.What could have been better: There is very little Python code in a book advertised as being about machine learning and Python programming. The few chapters that do contain Python code have some pretty trivial examples, like calculating the means and counts by group of a dataset using Pandas and NumPy, and training a very basic linear regression classifier with scikit-learn on a tiny dataset. The only real-world data given as an example is the Hospital Ambulatory Medical Care Survey (NHAMCS) from the CDC’s website. Chances are if you’re doing any kind of healthcare data analytics you will be working with medical claims data and clinical data from an electronic health record and I wish the book had given more examples using these types of data. Also, healthcare data gets messy pretty quickly, some examples and strategies on how to assure data quality would have been appreciated.It’s an okay book about healthcare data and the United States healthcare industry. You might be better served by a book specifically about machine learning and applying it to healthcare data.

SeattleAndrew Mar 01, 2020

This book is long on talking about healthcare analytics which it should t need, vs what it promoted itself as an example for code that was healthcare based.I feel like many of chapters Are refreshers on basics vs in depth work on algorithms and founds on probability how much specifically applies to healthcare issues.I feel like of the 100 page book only about 5 to 10 pages are of value, not even that much value.I just dropped the review down another star as I was really looking for ward to a complex “made simple” analytics read about application of healthcare.Instead of the basics refresher course on course versus advanced healthcare.Which is the name of the book, “blah blah blah made simple” which is obvious to every industry, a very tiny portion is specific to only healthcare.This book should be republished 2nd edition as 10 page pamphlet on healthcare analytics, the rest could be found on a five to ten minute search of LinkedIn for paper saving sake.

Aalap Shah, MD Jan 17, 2020

As an anesthesiologist, I have found that our specialty has shifted from traditional experience and bench/lab research to data-drive approaches in optimizing our patients' outcomes. As more patients enter the US hospital systems, more and more data points are being combined to help establish trends and extrapolate future results.While I personally have limited experience with data mining at different institutions (e.g. SQL), this primer helps even the least computer-savvy person read between the lines of the data-heavy EHR. One can understand how data points can start to be manipulated for research or quality/process improvement purposes. The primer does a great job of address important and trendy topics in healthcare quality improvement by empowering the reader with the tools to create predictive models for outcomes, code and analyze diagnoses and sub-groups, and plot data in a way that C-Suite and physicians alike can appreciate and understand.Overall, a very thorough primer that flows well between the tenets of knowledge germane to medical specialties and helps to create a dialog between non-programming physicians and analysts seeking to improve the healthcare system.

DI Jul 07, 2019

Excellent book to learn and to apply ML in healthcare.It s very well writen, so everybody can understand. I used this book as guideline for my project.

Healthcare Analytics Made Simple: Techniques in healthcare computing using machine learning and Python

What do you get with Print?

Healthcare Analytics Made Simple

Healthcare Foundations

Healthcare delivery in the US

Patient data – the journey from patient to computer

Standardized clinical codesets

Breaking down healthcare analytics

Summary

References and further reading

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Healthcare Analytics Made Simple: Techniques in healthcare computing using machine learning and Python

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