NEW

Theory and Practice of Business Intelligence in Healthcare, 1st Edition

  • Jiban Khuntia
  • Xue Ning
  • Mohan Tanniru
  • Published By:
  • ISBN-10: 1799823113
  • ISBN-13: 9781799823117
  • DDC: 610.285
  • 322 Pages | eBook
  • Original Copyright 2020 | Published/Released March 2020
  • This publication's content originally published in print form: 2020

  • Price:  Sign in for price

About

Overview

Business intelligence helps managers make informed business decisions in various levels and domains such as in healthcare. These technologies can handle large structured and unstructured data (big data) in the healthcare industry. Because of the complexity of healthcare data and the significant impact of healthcare data analysis, it is important to understand both theories and practices of business intelligence in healthcare. This collection of research introduces data mining, modeling, and analytic techniques to health and healthcare data; articulates the value of big data to health and healthcare; evaluates business intelligence tools; and explores business intelligence use and applications in healthcare. Topics include digital health, operations intelligence, and patient empowerment. For healthcare professionals, IT consultants, hospital directors, data management staff, data analysts, hospital administrators, executives, managers, academicians, students, and researchers.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Advances in Healthcare Information Systems and Administration (AHISA) Book Series.
Table of Contents.
Detailed Table of Contents.
Foreword.
Foreword.
Preface.
Acknowledgment.
Introduction.
1: Business Intelligence and Analytics (BI&A) Capabilities in Healthcare.
2: Billing and Review Perspectives in Healthcare.
3: Leveraging Intelligence in Value Creation Across Provider Patient Ecosystems.
4: Operations-Intelligence-Strategy (OIS) Process in Healthcare.
5: Generation and Management of Data for Healthcare and Health Diagnostics.
6: Data to Analytics to Insight: Role of rtDashboard at St. Joseph Mercy Health.
7: Deep Learning of Data Analytics in Healthcare.
8: Integration of BI in Healthcare: From Data and Information to Decisions.
9: A Proposed Architecture to Sustain Public-Private Partnership: The Case of the Arizona ASHLine.
10: Patient Empowerment and Analytics.
11: Guiding Assistive-Technology Adaptations Through Intelligent Stream Mining of Patient Data.
12: Ethically Building Business Intelligence in Healthcare: A Value-Sensitive Perspective.
Compilation of References.
About the Contributors.
Index.