An Introduction to Healthcare Informatics: Building Data-Driven Tools

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An Introduction to Healthcare
Informatics:
Building Data-Driven Tools
Copyright
Dedication
Author's biography
Foreword
Section 1: Storing and accessing data
The healthcare IT landscape
	How we got here and the growth of healthcare IT
	The role of informatics
	Common architectural aspects of healthcare IT
	Device and application levels
	Communication level
	Process level
	Common organizational aspects of healthcare IT
	Physician and nurse informaticists
	Regulatory aspects of healthcare IT
	Challenges and opportunities
	Conclusion
Relational databases
	A brief history of SQL and relational databases
	Overview of the relational model
	Differences between the relational model and SQL
	Primary and foreign keys
	ACID and transactions with data
	Normalization
	Conclusion
	References
SQL
	Getting started with SQL
	Structure of SQL databases
	Basic SQL: SELECT, FROM, WHERE, and ORDER BY statements
	Basic SQL: GROUP BY and general aggregate functions
	Intermediate SQL: Joins
	Advanced SQL: Window functions
	SQL concept: Indexes
	SQL concept: Schemas
	Advanced SQL: SubQueries
	Conclusion
Example project 1: Querying data with SQL
	Introduction and project background
	Viewing tables
	Querying tables
		Average number of visits per day per location
		Average patient age and patient sex per location
		Average number of patient visits per provider
		Counts of diagnosis codes and average age per clinic location
	Conclusion
Nonrelational databases
	Early nonrelational models
	The rise of modern nonrelational models
	Key-value stores
	Document stores
	Column stores
		Traditional column stores
		Wide column stores
	Graph databases
	Conclusion
	Reference
M/MUMPS
	A brief history and context
	The M language
	General concepts regarding arrays and MUMPS
	Arrays and MUMPS
	MUMPS, globals, and data infrastructure
	Conclusion
	References
Section 2: Understanding Healthcare Data
How to approach healthcare data questions
	Introduction
	Healthcare as a CAS
	Drivers of fallacy: Chance and bias
	Missingness
	Selecting tractable areas for intervention
	Data and trust
	Conclusion
Clinical and administrative workflows: Encounters, laboratory testing, clinical notes, and billing
	Introduction
	Encounters, patients, and episodes of care
	Laboratory testing, imaging, and medication administration
	Clinical notes and documentation
	Billing
	Conclusion
HL-7, clinical documentation architecture, and FHIR
	Introduction
	HL7 and HL7v2
	RIM, HL7v3, and clinical documentation architecture
	FHIR
	DICOM
	Vendor standards
	Cloud services
	Conclusion
	References
Ontologies, terminology mappings, and code sets
	Introduction
	Diagnostic ontologies: ICD and ICD-CM
	Procedure ontologies: ICD-PCS, CPT, and HCPCS
	General ontologies: SNOMED, SNOMED-CT
	Other specific ontologies: LOINC and NDC
	Summative ontologies: DRG
	Conclusion
	References
Section 3: Analyzing Data
A selective introduction to Python and key concepts
	Python: What and why
	A note on Python 2 and 3
	General structure of the language
	Type system
	Control flow
	Functions
	Objects
	Basic data structures
		Lists
		Sets
		Tuples
		Dictionaries
		List and dictionary comprehensions
	Conclusion
	Reference
Packages, interactive computing, and analytical documents
	Introduction
	Packages and package management
	Key packages
	Jupyter
	Analytical documents and interactive computing
	Conclusion
Assessing data quality, attributes, and structure
	Introduction
	Importing, cleaning, and assessing data
	Tidying data
	Handling missing values
	Conclusion
Introduction to machine learning: Regression, classification, and important concepts
	The aim of machine learning
	Regression
	Functions as hypotheses
	Error and cost
	Optimization
	Classification
	Additional considerations: Normalization, regularization, and generalizability
	Conclusion
Introduction to machine learning: Support vector machines, tree-based models, clustering, and explainability
	Introduction
	Support vector machines
	Decision trees
	Clustering
	Model explainability
	Conclusion
Computational phenotyping and clinical natural language processing
	Introduction
	Manual review and general review considerations
	Computational phenotyping: General considerations
	Supervised methods
	Unsupervised methods
	Natural language processing
	Conclusion
Example project 2: Assessing and modeling data
	Introduction and project background
	Data collection
	Data assessment and preparation
	Model creation
		Logistic regression
		Decision tree
		Support vector machine
	Exporting and persisting models
	Conclusion
Introduction to deep learning and artificial intelligence
	Introduction
	What exactly is deep learning
	Feed forward networks
	Training and backpropagation
	Local versus global minima
	Convolutional networks
	Adversarial examples and local minima
	Recurrent networks
	Autoencoders and generative adversarial networks
	Conclusion
	Reference
Section 4: Designing Data Applications
Analysis best practices
	Introduction
	Workflow
	Documentation
	Data governance
	Conclusion
Overview of big data tools: Hadoop, Spark, and Kafka
	Introduction
	Hadoop
	Spark
	Kafka
	Conclusion
Cloud technologies
	Introduction
	Data storage
	Compute
	Machine learning and analysis services
	Conclusion
Index
	A
	B
	C
	D
	E
	F
	G
	H
	I
	J
	K
	L
	M
	N
	O
	P
	Q
	R
	S
	T
	U
	V
	W
	X
	Y
	Z
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