Actionable Intelligence in Healthcare
This book shows healthcare professionals how to turn data points into meaningful knowledge upon which they can take effective action. Actionable intelligence can take many forms, from informing health policymakers on effective strategies for the population to providing direct and predictive insights on patients to healthcare providers so they can achieve positive outcomes. It can assist those performing clinical research where relevant statistical methods are applied to both identify the efficacy of treatments and improve clinical trial design. It also benefits healthcare data standards groups through which pertinent data governance policies are implemented to ensure quality data are obtained, measured, and evaluated for the benefit of all involved.

Although the obvious constant thread among all of these important healthcare use cases of actionable intelligence is the data at hand, such data in and of itself merely represents one element of the full structure of healthcare data analytics. This book examines the structure for turning data into actionable knowledge and discusses:







  • The importance of establishing research questions


  • Data collection policies and data governance


  • Principle-centered data analytics to transform data into information


  • Understanding the "why" of classified causes and effects


  • Narratives and visualizations to inform all interested parties


Actionable Intelligence in Healthcare is an important examination of how proper healthcare-related questions should be formulated, how relevant data must be transformed to associated information, and how the processing of information relates to knowledge. It indicates to clinicians and researchers why this relative knowledge is meaningful and how best to apply such newfound understanding for the betterment of all.

1126173482
Actionable Intelligence in Healthcare
This book shows healthcare professionals how to turn data points into meaningful knowledge upon which they can take effective action. Actionable intelligence can take many forms, from informing health policymakers on effective strategies for the population to providing direct and predictive insights on patients to healthcare providers so they can achieve positive outcomes. It can assist those performing clinical research where relevant statistical methods are applied to both identify the efficacy of treatments and improve clinical trial design. It also benefits healthcare data standards groups through which pertinent data governance policies are implemented to ensure quality data are obtained, measured, and evaluated for the benefit of all involved.

Although the obvious constant thread among all of these important healthcare use cases of actionable intelligence is the data at hand, such data in and of itself merely represents one element of the full structure of healthcare data analytics. This book examines the structure for turning data into actionable knowledge and discusses:







  • The importance of establishing research questions


  • Data collection policies and data governance


  • Principle-centered data analytics to transform data into information


  • Understanding the "why" of classified causes and effects


  • Narratives and visualizations to inform all interested parties


Actionable Intelligence in Healthcare is an important examination of how proper healthcare-related questions should be formulated, how relevant data must be transformed to associated information, and how the processing of information relates to knowledge. It indicates to clinicians and researchers why this relative knowledge is meaningful and how best to apply such newfound understanding for the betterment of all.

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Actionable Intelligence in Healthcare

Actionable Intelligence in Healthcare

Actionable Intelligence in Healthcare

Actionable Intelligence in Healthcare

Overview

This book shows healthcare professionals how to turn data points into meaningful knowledge upon which they can take effective action. Actionable intelligence can take many forms, from informing health policymakers on effective strategies for the population to providing direct and predictive insights on patients to healthcare providers so they can achieve positive outcomes. It can assist those performing clinical research where relevant statistical methods are applied to both identify the efficacy of treatments and improve clinical trial design. It also benefits healthcare data standards groups through which pertinent data governance policies are implemented to ensure quality data are obtained, measured, and evaluated for the benefit of all involved.

Although the obvious constant thread among all of these important healthcare use cases of actionable intelligence is the data at hand, such data in and of itself merely represents one element of the full structure of healthcare data analytics. This book examines the structure for turning data into actionable knowledge and discusses:







  • The importance of establishing research questions


  • Data collection policies and data governance


  • Principle-centered data analytics to transform data into information


  • Understanding the "why" of classified causes and effects


  • Narratives and visualizations to inform all interested parties


Actionable Intelligence in Healthcare is an important examination of how proper healthcare-related questions should be formulated, how relevant data must be transformed to associated information, and how the processing of information relates to knowledge. It indicates to clinicians and researchers why this relative knowledge is meaningful and how best to apply such newfound understanding for the betterment of all.

Product Details

ISBN-13: 9781032476865
Publisher: CRC Press
Publication date: 01/21/2023
Series: Data Analytics Applications
Pages: 294
Product dimensions: 6.12(w) x 9.19(h) x (d)

About the Author

Dr. Jay Liebowitz is the DiSanto Visiting Chair in Applied Business and Finance at Harrisburg University of Science and Technology. He previously served as the Orkand Endowed Chair of Management and Technology in the Graduate School at the University of Maryland University College (UMUC). Prior to UMUC, Dr. Liebowitz was a full professor in the Carey Business School at Johns Hopkins University. He was ranked one of the top 10 knowledge management researchers/practitioners out of 11,000 worldwide. Dr. Liebowitz is the founding editor-in-chief of Expert Systems with Applications: An International Journal, which is ranked as a top-tier journal worldwide.

Amanda Dawson holds a Ph.D. in Experimental Psychology and is the Director of Research at Select Medical where she oversees clinical research and quality improvement initiatives at more than 100 nation-wide long-term acute care hospitals. Prior to joining Select Medical, she was a research fellow in biomedicine at Albert Einstein Hospital’s Moss Rehabilitation Research Institute and completed her post-doctoral training in Physical Medicine & Rehabilitation from the University of Pennsylvania Medical School. Over the past decade, she has published numerous articles on identifying patient subpopulations and characterizing clinical practice patterns and patient outcomes over time.

Table of Contents

Empowering Clinician-Scientists in the Information Age of Omics and Data Science
Pamela A. Tamez and Mary B. Engler

Making Data Matter: Identifying Care Opportunities for US Healthcare Transformation
Mark A. Caron

Turning Data into Enhanced Value for Patients
Kyun Hee (Ken) Lee

Data Analytics for the Clinical Researcher
Minjae Kim

Intelligent Healthcare: The Case of the Emergency Department
Shivaram Poigai Arunachalam, Mustafa Sir, and Kalyan S. Pasupathy

Network Analytics to Enable Decisions in Healthcare Management
Uma Srinivasan, Arif Khan, and Shahadat Uddin

Modeling and Analysis of Behavioral Health Data Using Graph Analytics
Rose Yesha and Aryya Gangopadhyay

The Heart of the Digital Workplace: Intelligent Search Moves the Measure from Efficiency to Proficiency for a Fortune Healthcare Company
Jay Liebowitz and Diane Berry

The Promise of Big Data Analytics—Transcending Knowledge Discovery through Point-of-Care Applications
Lavi Oud

Predictive Analytics and Machine Learning in Medicine
L. Nelson Sanchez-Pinto and Matthew M. Churpek

High-Dimensional Models and Analytics in Large Database Applications
Michael Brimacombe

Learning to Extract Actionable Evidence from Medical Insurance Claims Data
Jieshi Chen and Artur Dubrawski

The Role of Unstructured Data in Healthcare Analytics
Amanda Dawson and Sergei Ananyan

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