An Introduction to Healthcare Informatics: Building Data-Driven Tools

Download An Introduction to Healthcare Informatics: Building Data-Driven Tools written by Peter McCaffrey in PDF format. This book is under the category Health and bearing the isbn/isbn13 number 0128149159; 0128149167/9780128149157/ 9780128149164. You may reffer the table below for additional details of the book.

$19.99

SKU: 404dcc91b2ae Category: Tags: ,

Specifications

book-author

Peter McCaffrey

publisher

Academic Press

file-type

PDF

pages

542 pages

language

English

asin

B08F3CWF7J

isbn10

0128149159; 0128149167

isbn13

9780128149157/ 9780128149164


Book Description

An Overview of Healthcare Informatics with a Focus on the Development of Data-Driven Tools (PDF) helps to bridge the gap between the existing environment of healthcare information technology (IT) and the most cutting-edge technologies in cloud infrastructure, data science, artificial intelligence, and application development. IT encompasses a number of fields that are undergoing rapid development; however, the field of healthcare suffers from a relatively antiquated technology landscape and a lack of curricula to effectively train its millions of practitioners in the skills they need to use data and related tools. Information technology (IT) encompasses a number of rapidly developing fields.

Data analysis, data access, the current environment of big data, and application design are just few of the subjects covered in this booklet. In addition to that, it includes a discussion on the potential future advances in this rapidly expanding sector. This ebook equips nurses, physicians, and health scientists with the knowledge and abilities essential to collaborate with IT professionals and analysts, as well as to perform analysis and application architecture on their own.

 

  • Contains illustrations for each example and technology, each of which describes how it operates on its own and how it fits into a wider reference architecture that is built upon throughout the chapter.
  • It provides a case-based learning approach that is pertinent to the healthcare industry, bringing each topic together with an example that is essential when describing the function of SQL, databases, basic models, and so on.
  • Describes healthcare-specific stakeholders as well as the administration of analytical initiatives within the healthcare industry; thereby enabling healthcare practitioners to successfully negotiate the political and regulatory hurdles that stand in the way of execution.
  • This document offers a road map for incorporating current technologies and design patterns in a healthcare setting. It assists the reader in understanding both the antiquated enterprise systems that frequently present in hospitals and emerging solutions, as well as how they might be utilized together.

 

 

PLEASE TAKE NOTE That the PDF version of An Introduction to Healthcare Informatics: Building Data-Driven Tools is the ONLY item included in this offer. There are no access codes provided.

Table of contents


Table of contents :
Cover
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
Back Cover

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