Clinical Decision Support and Beyond

Progress and Opportunities in Knowledge-Enhanced Health and Healthcare

3rd Edition - February 10, 2023
Imprint: Academic Press
Editors: Robert Greenes, Guilherme Del Fiol
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 1 2 0 0 - 6
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 9 5 7 7 - 1

Clinical Decision Support and Beyond: Progress and Opportunities in Knowledge-Enhanced Health and Healthcare, now in its third edition, discusses the underpinnings of effect… Read more

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Clinical Decision Support and Beyond: Progress and Opportunities in Knowledge-Enhanced Health and Healthcare, now in its third edition, discusses the underpinnings of effective, reliable, and easy-to-use clinical decision support systems at the point of care as a productive way of managing the flood of data, knowledge, and misinformation when providing patient care. Incorporating CDS into electronic health record systems has been underway for decades; however its complexities, costs, and user resistance have lagged its potential. Thus it is of utmost importance to understand the process in detail, to take full advantage of its capabilities. The book expands and updates the content of the previous edition, and discusses topics such as integration of CDS into workflow, context-driven anticipation of needs for CDS, new forms of CDS derived from data analytics, precision medicine, population health, integration of personal monitoring, and patient-facing CDS. In addition, it discusses population health management, public health CDS and CDS to help reduce health disparities. It is a valuable resource for clinicians, practitioners, students and members of medical and biomedical fields who are interested to learn more about the potential of clinical decision support to improve health and wellness and the quality of health care.

Cover image
Title page
Table of Contents
Copyright
Contributors
Preface to the third edition, 2023
Preface to the second edition, 2014
Preface to the first edition, 2007
Section I: Goals, methodologies, and challenges for clinical decision support and beyond
Chapter 1: Definition, purposes, and scope
Abstract
1.1: Introduction: CDS And beyond
1.2: CDS and the human
1.3: Design and structure of CDS
1.4: Other considerations
References
Chapter 2: Clinical decision support methods
Abstract
2.1: Introduction
2.2: Primary research methodologies that have been pursued and extended
2.3: Conclusion
References
Chapter 3: The journey to broad adoption
Abstract
3.1: The tale of a relationship
3.2: Where we go from here
References
Chapter 4: The role of quality measurement and reporting feedback as a driver for care improvement
Abstract
4.1: Introduction
4.2: Quality measures and clinical decision support: Similarities and differences
4.3: Creating a quality measure
4.4: Constructing the quality measure equation
4.5: Translating measure concepts into interoperable structures and definitions
4.6: Identifying CDS interventions based on the quality measure
4.7: A CDS rule component taxonomy
4.8: The details about the CDS rules component taxonomy
4.9: The CDS rules component taxonomy as a driver for quality measurement and care improvement
4.10: Assuring the quality of a measure
4.11: Driving care improvement
References
Chapter 5: International dimensions of clinical decision support systems
Abstract
Acknowledgments
5.1: Introduction
5.2: Healthcare system organization in Australia and New Zealand
5.3: Digital health context in Australia and New Zealand
5.4: CDSSs in Australia and New Zealand
5.5: Healthcare system organization in Europe
5.6: Digital health context in Europe
5.7: CDSSs in Europe
5.8: Emerging CDSS uses in high-income nations
5.9: Healthcare system organization in low- and middle-income countries: Focus on East Africa and Haiti
5.10: Health information systems in LMICs
5.11: CDSSs in low- and middle-income countries
5.12: Conclusions
References
Section II: Sources of knowledge for clinical decision support and beyond
Chapter 6: Human-intensive techniques
Abstract
6.1: Introduction
6.2: Theoretical basis for knowledge acquisition
6.3: Cognitive task analysis
6.4: History and evolution of computer-based knowledge acquisition
6.5: Example
6.6: Conclusions
References
Chapter 7: Data-driven approaches to generating knowledge: Machine learning, artificial intelligence, and predictive modeling
Abstract
7.1: Introduction
7.2: Types of machine learning
7.3: Frameworks for developing machine learning models in healthcare
7.4: Learning from data
7.5: Overview of machine learning modeling methods
7.6: Prediction models in medicine
7.7: Machine learning CDS implementation
7.8: Conclusion
References
Chapter 8: Modernizing evidence synthesis for evidence-based medicine
Abstract
8.1: Introduction
8.2: Systematic reviews and meta-analysis: The premise and promise
8.3: Uses of systematic reviews and meta-analyses
8.4: Steps of a systematic review
8.5: Accessing systematic reviews, meta-analyses, and field synopses
8.6: Systematic reviews, meta-analyses, and the evidence ecosystem
8.7: Conclusion
References
Section III: The technology of clinical decision support and beyond
Chapter 9: Decision rules and expressions
Abstract
9.1: Introduction
9.2: Procedural knowledge
9.3: Knowledge as production rules
9.4: A hybrid approach for knowledge transfer: Arden Syntax
9.5: Expression languages
9.6: Standard data models for decision rules
9.7: Toward further standardization: Quality measures and Health e-Decisions
9.8: Future work
9.9: Conclusions
References
Chapter 10: Guidelines and workflow models
Abstract
Acknowledgment
10.1: Introduction
10.2: Supporting knowledge acquisition
10.3: Formal methods for modeling and specifying CIGs
10.4: Integration of guidelines with workflow
10.5: CIG and workflow verification and exception-handling
10.6: CIG and careflow enactment tools
10.7: Process mining and improvement
10.8: CIG-based decision support for multimorbidity patients
10.9: Support of patients as end-users via CIG and workflow models
10.10: Discussion
10.11: Recommended resources
References
Chapter 11: Terminologies, ontologies and data models
Abstract
11.1: Introduction
11.2: Introduction to the semantic spectrum: Ontologies, vocabularies/terminologies, and Data models
11.3: Formalisms and use cases
11.4: Implications
11.5: Implementations of ontologies, terminologies, and models in CDS
11.6: Sharing of decision logic
11.7: Conclusions
References
Chapter 12: Grouped knowledge elements
Abstract
12.1: Introduction
12.2: Clinical documentation
12.3: Order sets
12.4: Current standards for grouped knowledge elements
12.5: Conclusions
References
Chapter 13: Infobuttons and point of care access to knowledge
Abstract
13.1: Introduction
13.2: Understanding and addressing clinician information needs
13.3: Infobuttons
13.4: Question answering systems
13.5: Uptake, user satisfaction, and impact of infobuttons on clinician's decision making
13.6: The HL7 standard for context aware decision support
13.7: Ongoing and future research
13.8: Conclusions
References
Section IV: Adoption of clinical decision support and other modes of knowledge enhancement
Chapter 14: Information visualization and integration
Abstract
Acknowledgments
14.1: Introduction
14.2: How people think, and see
14.3: Visual design principles for CDS information integration and visualization
14.4: Framing problems and designing solutions
14.5: Progress in information integration and visualization in CDS
14.6: Future challenges for CDS information integration and visualization
References
Chapter 15: The role of standards: What we can expect and when
Abstract
15.1: Introduction
15.2: The case for standards
15.3: CDS development with and without standards
15.4: Areas in need of standardization
15.5: Assessment of current state of CDS standards and needed future work
15.6: Beyond the standards—What is needed for widespread CDS adoption?
15.7: How important are standards?
15.8: Vision for potential future impact of standards
References
Chapter 16: Population analytics and decision support
Abstract
16.1: Population health data acquisition methods
16.2: Analytics methods
16.3: Addressing the problems associated with analytic tools
16.4: The Mayo Clinic approach to population data analysis
References
Chapter 17: Expanded sources for precision medicine
Abstract
17.1: Introduction
17.2: Current state of decision support in clinical genomics
17.3: Challenges and opportunities
17.4: Conclusion
References
Chapter 18: Knowledge resources
Abstract
18.1: Introduction
18.2: Knowledge publishers
18.3: Knowledge assets
18.4: Knowledge resources and knowledge distribution
18.5: Conclusion
References
Chapter 19: Cognitive considerations for health information technology in clinical team environments
Abstract
19.1: Introduction
19.2: Challenges for cognitive support in health care
19.3: Developing cognitive support: Distributed cognition
19.4: Building systems with distributed cognition in mind
19.5: Developing tools to support cognition
19.6: Summary
References
Chapter 20: Governance and implementation
Abstract
20.1: Introduction
20.2: Governance structures for CDS
20.3: Participants in CDS governance
20.4: Different governance structures
20.5: Structural and functional aspects of CDS governance
20.6: Prioritization of CDS activities
20.7: Metrics, feedback, and anomalies
20.8: CDS governance models that work
20.9: CDS governance models that fail
20.10: Governance evolution over time
20.11: Challenges to effective governance
20.12: Governance of emerging models of CDS
20.13: Impact of crises on CDS governance
20.14: Implementation
20.15: Creating CDS that matches workflow
20.16: Key CDS guidance
20.17: Facilitators and barriers in CDS implementation
20.18: Role of vendors in CDS
20.19: CDS costs and implementation strategy decisions
20.20: Implementation principles
20.21: CDS management
20.22: CDS change management
20.23: Impact of EHR-to-EHR transitions on CDS
20.24: CDS frontiers
References
Chapter 21: Managing the investment in clinical decision support
Abstract
21.1: Introduction
21.2: Clinical knowledge management
21.3: Organization of the effort
21.4: Key IT strategies and considerations
21.5: Evaluation of the impact and value of knowledge management
21.6: Conclusions
References
Chapter 22: Evaluation of clinical decision support
Abstract
22.1: Clinical decision support adoption
22.2: Evaluation methods and reporting of CDS
22.3: Clinical decision support effectiveness
22.4: Provider responses to clinical decision support
22.5: Clinical decision support measures and utilization
22.6: Limitations of clinical decision support
22.7: Keys to succeeding with CDS
References
Chapter 23: Legal and regulatory issues related to the use of clinical software in healthcare delivery
Abstract
Acknowledgments
23.1: Basic legal standards
23.2: IMDRF
23.3: Legislation and regulation in the United States
23.4: FDA and CDS software regulation
23.5: 21st Century Cures and other HHS agencies
23.6: HiTECH act and MU certification
23.7: Conclusion
References
Chapter 24: The promise of patient-directed decision support
Abstract
24.1: Introduction
24.2: Some definitions
24.3: Technology innovation
24.4: Conclusion
References
Chapter 25: Clinical decision support and health disparities
Abstract
25.1: Introduction
25.2: Health disparities
25.3: Clinical decision support and health disparities
25.4: CDS implementation for underserved populations
25.5: CDS, artificial intelligence, and health equity
25.6: Conclusion
References
Chapter 26: Population health management
Abstract
26.1: Introduction
26.2: Scope of this chapter
26.3: Components of population health management
26.4: Case studies
26.5: Conclusion
References
Chapter 27: CDS for public health
Abstract
27.1: Introduction
27.2: Decision support for public health operations
27.3: Decision support for assessment operations
27.4: Decision support for policy development operations
27.5: Decision support for public health assurance operations
27.6: Public health decision support systems designed to influence clinical care providers
27.7: Prescription drug monitoring programs
27.8: Immunization information systems
27.9: Electronic case reporting (eCR)
27.10: Summary
References
Section V: The journey to a knowledgeenhanced health and health care system
Chapter 28: Clinical knowledge management program
Abstract
Acknowledgments
28.1: Introduction and program overview
28.2: Knowledge engineering process
28.3: Software infrastructure
28.4: Integration with clinical systems
28.5: Future directions
28.6: Conclusions
References
Chapter 29: Integration of knowledge resources into applications to enable CDS: Architectural considerations
Abstract
29.1: Introduction
29.2: Generic system architectures, examples, and their pros and cons
29.3: Exemplar FHIR-aligned system architectures
29.4: Role of the CIS architecture
29.5: Scaling considerations for CDS and knowledge-enhanced health/healthcare
29.6: Other issues to be considered
29.7: Conclusions
References
Chapter 30: Getting to knowledge-enhanced health and healthcare
Abstract
30.1: Where we are now
30.2: Impediments still with us
30.3: Need for new mechanisms
30.4: Orchestration
30.5: A possible paradigm for future CDS&B
30.6: Looking ahead: Epilogue as prologue
References
Index

Robert Greenes

Robert Greenes, MD, PhD, holds an MD and a PhD in Computer Science from Harvard. Dr Greenes is an expert in health care information technology/informatics and has made contributions to the field over many years, initially at Harvard and more recently at Arizona State University in partnership with Mayo Clinic. His passion is the use of information technology in health care to make "the right thing the easy thing to do". He is Ira A. Fulton Chair of Biomedical Informatics at the ASU,  a member of the National Academy of Medicine and of the International Academy of Health Sciences Informatics, and a Fellow of the American College of Radiology, American College of Medical Informatics, and the Society for Imaging Informatics in Medicine.  He was the 2008 recipient of the Morris F. Collen Award for lifetime impact on the field of biomedical informatics, from the American College of Medical Informatics.

Affiliations and expertise

Emeritus Professor of Biomedical Informatics, Arizona State University, Phoenix, AZ, United States

Guilherme Del Fiol

Guilherme Del Fiol, MD, PhD, earned his MD from the University of Sao Paulo, Brazil; his MS in Computer Science from the Catholic University of Parana, Brazil; and his PhD in Biomedical Informatics from the University of Utah. He is currently Professor and Vice-Chair of Research in the University of Utah’s Department of Biomedical Informatics. Prior to the University of Utah, Dr. Del Fiol held positions in Clinical Knowledge Management at Intermountain Healthcare and as faculty at the Duke Community and Family Medicine Department. Since 2008, he has served as an elected co-chair of the Clinical Decision Support Work Group at Health Level International (HL7). He is also an elected Fellow of the American College of Medical Informatics (ACMI) and a member of the Comprehensive Cancer Center at Huntsman Cancer Institute. His research interests are in the investigation of standards-based clinical decision support and digital health interventions.

Affiliations and expertise

Professor, Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States; Vice-Chair of Research, Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

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