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Clinical Decision Support: The Road to Broad Adoption 2nd edition [Kõva köide]

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Edited by (Emeritus Professor of Biomedical Informatics, Arizona State University, Phoenix, AZ, United States)
  • Formaat: Hardback, 930 pages, kõrgus x laius: 235x191 mm, kaal: 1720 g, 150 illustrations (50 in full color); Illustrations, unspecified
  • Ilmumisaeg: 09-Jun-2014
  • Kirjastus: Academic Press Inc
  • ISBN-10: 0123984769
  • ISBN-13: 9780123984760
Teised raamatud teemal:
Clinical Decision Support: The Road to Broad Adoption 2nd editionSuurem pilt
  • Formaat: Hardback, 930 pages, kõrgus x laius: 235x191 mm, kaal: 1720 g, 150 illustrations (50 in full color); Illustrations, unspecified
  • Ilmumisaeg: 09-Jun-2014
  • Kirjastus: Academic Press Inc
  • ISBN-10: 0123984769
  • ISBN-13: 9780123984760
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780123984760.html
Märksõnad:

With at least 40% new or updated content since the last edition, Clinical Decision Support, 2nd Edition explores the crucial new motivating factors poised to accelerate Clinical Decision Support (CDS) adoption. This book is mostly focused on the US perspective because of initiatives driving EHR adoption, the articulation of 'meaningful use', and new policy attention in process including the Office of the National Coordinator for Health Information Technology (ONC) and the Center for Medicare and Medicaid Services (CMS). A few chapters focus on the broader international perspective. Clinical Decision Support, 2nd Edition explores the technology, sources of knowledge, evolution of successful forms of CDS, and organizational and policy perspectives surrounding CDS.

Exploring a roadmap for CDS, with all its efficacy benefits including reduced errors, improved quality, and cost savings, as well as the still substantial roadblocks needed to be overcome by policy-makers, clinicians, and clinical informatics experts, the field is poised anew on the brink of broad adoption. Clinical Decision Support, 2nd Edition provides an updated and pragmatic view of the methodological processes and implementation considerations. This book also considers advanced technologies and architectures, standards, and cooperative activities needed on a societal basis for truly large-scale adoption.

  • At least 40% updated, and seven new chapters since the previous edition, with the new and revised content focused on new opportunities and challenges for clinical decision support at point of care, given changes in science, technology, regulatory policy, and healthcare finance
  • Informs healthcare leaders and planners, health IT system developers, healthcare IT organization leaders and staff, clinical informatics professionals and researchers, and clinicians with an interest in the role of technology in shaping healthcare of the future

Muu info

A detailed 'how-to' and roadmap on the technology development and successful integration of clinical decision support systems into point-of-care and national regulatory frameworks
Foreword xxv
Preface xxvii
Preface to the first edition xxix
Contributors xxxv
SECTION I COMPUTER-BASED CLINICAL DECISION SUPPORT: OVERVIEW, STATUS, AND CHALLENGES
Chapter 1 Definition, Scope, and Challenges
3(46)
Robert A. Greenes
1.1 Introduction
3(5)
1.2 Definition of computer-based clinical decision support
8(1)
1.3 Features of CDS
8(1)
1.4 The tale of a relationship
9(26)
1.4.1 A long infatuation
9(3)
1.4.2 A troubled courtship
12(9)
1.4.3 Renewed passions
21(1)
1.4.4 Getting the support of the relatives (stakeholders)
22(2)
1.4.5 Knowledge management and infrastructure - new parties to the relationship
24(6)
1.4.6 Building the foundations for a lasting relationship: new drivers for adoption
30(5)
1.5 Scope and plan of this book
35(14)
1.5.1 What we do not cover
36(1)
1.5.2 Organization of subsequent sections
37(1)
1.5.3 Goals
38(1)
References
39(10)
Chapter 2 A Brief History of Clinical Decision Support
49(62)
Robert A. Greenes
2.1 Primary research methodologies that have been pursued and extended
49(32)
2.1.1 Information retrieval
50(6)
2.1.2 Evaluation of logical conditions
56(6)
2.1.3 Probabilistic and data-driven classification or prediction
62(8)
2.1.4 Heuristic modeling and expert systems
70(4)
2.1.5 Calculations, algorithms, and multistep processes
74(4)
2.1.6 Associative groupings of elements
78(3)
2.2 Driving forces for CDS
81(17)
2.2.1 The technology imperative
81(3)
2.2.2 Knowledge explosion
84(1)
2.2.3 Adoption of new technologies/resources for diagnosis and treatment
85(1)
2.2.4 Assimilating discovery and knowledge
86(1)
2.2.5 The internet, media, and mobile communications
86(1)
2.2.6 Empowerment of patients and consumers
87(1)
2.2.7 Medical errors
87(1)
2.2.8 Variability in quality, access, and adoption of best practices - calls for movement to a "learning health system" and the digital infrastructure to support it
88(1)
2.2.9 Spread of EHRs
89(1)
2.2.10 Aging of population and increased complexity of disease
90(1)
2.2.11 The no-win proposition: decreasing time and increasing pressure on doctors
91(1)
2.2.12 Fragmentation and difficulty coordinating care
92(1)
2.2.13 Defensive medicine
93(1)
2.2.14 Health care costs
93(1)
2.2.15 Pay for performance and pay for value
94(2)
2.2.16 Demonstrated benefits
96(1)
2.2.17 Top-down initiatives
96(2)
2.3 Conclusion
98(13)
References
98(13)
Chapter 3 Features of Computer-Based Clinical Decision Support
111(34)
Robert A. Greenes
3.1 CDS and the human
112(7)
3.1.1 Computer as omniscient sage
113(1)
3.1.2 Computer as out-of-touch meddler
113(1)
3.1.3 A more symbiotic view
113(1)
3.1.4 Limitations of the technology
114(2)
3.1.5 Considerations regarding human-computer interaction
116(3)
3.2 Design and structure of CDS
119(21)
3.2.1 Purpose
120(7)
3.2.2 Design of CDS: components and interactions
127(9)
3.2.3 Modes of interactions
136(4)
3.3 Other considerations
140(5)
References
141(4)
Chapter 4 The Role of Quality Measurement and Reporting Feedback as a Driver for Care Improvement
145(20)
Floyd Eisenberg
4.1 Introduction
145(1)
4.2 Quality measures and clinical decision support: similarities and differences
146(1)
4.3 Creating a quality measure
146(2)
4.4 Constructing the quality measure equation
148(4)
4.4.1 Proportion measure
150(1)
4.4.2 Proportion measure 1
150(1)
4.4.3 Proportion measure 2
150(1)
4.4.4 Proportion measure 3
150(1)
4.4.5 Proportion measure 4
151(1)
4.4.6 Continuous variable measure
151(1)
4.4.7 Continuous variable measure 1
151(1)
4.4.8 Continuous variable measure 2
151(1)
4.5 Identifying CDS interventions based on the quality measure
152(3)
4.6 A CDS rule component taxonomy
155(1)
4.7 The details about the CDS rules component taxonomy
156(1)
4.8 The CDS rules component taxonomy as a driver for quality measurement and care improvement
156(2)
4.9 Assuring the quality of a measure
158(1)
4.10 Driving care improvement
159(6)
References
160(5)
SECTION II EXPERIENCE WITH CDS DEVELOPMENT AND ADOPTION: CASE STUDIES, NATIONAL INITIATIVES, AND LESSONS LEARNED
Chapter 5 Regenstrief Medical Informatics
165(24)
Paul Biondich
Brian E. Dixon
Jon Duke
Burke Mamlin
Shaun Grannis
Blaine Takesue
Steve Downs
William Tierney
5.1 Introduction
165(1)
5.2 History
166(14)
5.2.1 Early system development and paper-based reminders
166(3)
5.2.2 RMRS's maturation into a hospital-wide medical record system
169(3)
5.2.3 Early development of computerized physician order entry
172(2)
5.2.4 Evolution of Regenstrief's Medical Gopher System
174(3)
5.2.5 Next-generation clinical decision support
177(3)
5.3 Expanding roles for decision support
180(2)
5.3.1 The Indiana network for patient care
180(1)
5.3.2 Public health decision support
181(1)
5.3.3 Meaningful use
182(1)
5.4 Conclusion
182(7)
References
184(5)
Chapter 6 Patients, Doctors, and Information Technology Clinical Decision Support at Brigham and Women's Hospital and Partners Healthcare
189(20)
Adam Wright
David W. Bates
6.1 Introduction
189(1)
6.2 History
190(1)
6.3 Clinical decision support and inpatient CPOE at BWH
190(8)
6.3.1 Early decision support at BWH
190(2)
6.3.2 Medication-related decision support
192(3)
6.3.3 Laboratory interventions
195(1)
6.3.4 Radiology interventions
196(1)
6.3.5 Signout
197(1)
6.3.6 Assessment of satisfaction with CPOE
197(1)
6.3.7 Impact of CPOE on provider time
198(1)
6.4 Decision support delivered using the outpatient electronic health record
198(4)
6.4.1 Medication-related decision support
198(1)
6.4.2 Laboratory-related decision support
198(1)
6.4.3 Radiology decision support
199(1)
6.4.4 Impact on provider time
200(1)
6.4.5 Reminders
201(1)
6.4.6 Personal health records
201(1)
6.4.7 Smart forms
201(1)
6.4.8 Problem list accuracy
201(1)
6.5 Overarching studies
202(1)
6.6 Overarching lessons
203(2)
6.7 Future directions
205(4)
References
205(4)
Chapter 7 Computer-Based Approaches to Improving Healthcare Quality and Safety at LDS Hospital
209(32)
R. Scott Evans
7.1 Overview
209(1)
7.2 Introduction
209(5)
7.2.1 Key features for clinical decision support tools
210(4)
7.3 Tools for information management
214(2)
7.3.1 Pharmacy system
214(1)
7.3.2 Respiratory therapy charting
215(1)
7.4 Tools for focusing attention
216(12)
7.4.1 Infectious Disease Monitor
216(2)
7.4.2 Automated reportable case reports
218(1)
7.4.3 Therapeutic antibiotic monitor
219(1)
7.4.4 Adverse drug event monitor
220(2)
7.4.5 Preoperative antibiotic monitor
222(1)
7.4.6 Drug-dose monitor
222(1)
7.4.7 Enhanced notification of ventilator-related events
223(1)
7.4.8 Enhanced notification of infusion pump programming errors
224(1)
7.4.9 PICC DVTs
225(1)
7.4.10 Multi-drug resistant organism (MDRO) alerts
226(2)
7.5 Tools for patient-specific consultation
228(6)
7.5.1 Ventilator protocols
228(1)
7.5.2 Anti-infective agent assistance
229(3)
7.5.3 Computer-based glucose control
232(1)
7.5.4 Patient isolation program
233(1)
7.6 Conclusions and lessons learned
234(7)
References
236(5)
Chapter 8 International Dimensions of Clinical Decision Support
241(28)
Hamish Fraser
Jeremy Wyatt
8.1 Introduction
241(1)
8.2 CDS experience in the UK
241(12)
8.2.1 Overview of UK national health service structure and policies
242(2)
8.2.2 Connecting for health, the UK national programme for information technology and the 2012 NHS information strategy
244(2)
8.2.3 Implementation of EHR systems and CDS in UK hospitals
246(2)
8.2.4 Implementation of CDS in UK primary care settings
248(1)
8.2.5 Case studies of successful CDS adoption and spread in UK NHS
248(3)
8.2.6 Current developments likely to promote adoption and spread of CDS in the UK
251(2)
8.3 CDS experience in low and middle income countries
253(8)
8.3.1 Health information needs in LMICs
253(1)
8.3.2 Health information systems in LMICs
254(2)
8.3.3 Mobile health systems
256(1)
8.3.4 Examples of the use of clinical decision support
257(4)
8.4 Conclusions
261(8)
References
262(7)
Chapter 9 Current State of CDS Utilization
269(16)
Robert A. Greenes
9.1 Introduction
269(4)
9.2 Academic prototypes
273(5)
9.2.1 Nature of the project
273(1)
9.2.2 Consequences for operation
274(1)
9.2.3 Local differences
275(1)
9.2.4 Maintenance and update
276(2)
9.3 Standards and sharing of interoperable content and tools
278(1)
9.4 Users
279(1)
9.5 Other considerations
280(5)
References
281(4)
SECTION III SOURCES OF KNOWLEDGE FOR CLINICAL DECISION SUPPORT
Chapter 10 Human-Intensive Techniques
285(24)
Vimla L. Patel
Edward H. Shortliffe
10.1 Introduction
285(4)
10.2 Theoretical basis for knowledge acquisition
289(3)
10.2.1 The nature of expertise
289(2)
10.2.2 Role of mental models
291(1)
10.2.3 Team-based decisions and shared knowledge
292(1)
10.3 Cognitive task analysis
292(9)
10.3.1 Knowledge elicitation (KE) methods
293(3)
10.3.2 Data analysis methods
296(3)
10.3.3 Representational methods
299(2)
10.4 History and current status of computer-based knowledge acquisition
301(3)
10.5 Conclusions
304(5)
References
305(4)
Chapter 11 Generation of Knowledge for Clinical Decision Support
309(30)
Michael E. Matheny
Lucila Ohno-Machado
11.1 Introduction
309(3)
11.2 Learning from data
312(1)
11.3 Overview of logistic regression
313(4)
11.4 Overview of some machine learning models
317(4)
11.4.1 Classification trees
317(2)
11.4.2 Artificial neural networks
319(2)
11.5 Prediction models in medicine
321(6)
11.5.1 Prognosis of ICU mortality
322(2)
11.5.2 Cardiovascular disease risk
324(1)
11.5.3 Prognosis in interventional cardiology
325(1)
11.5.4 Pneumonia severity-of-illness index
326(1)
11.6 Conclusions
327(12)
References
328(11)
Chapter 12 Modernizing Evidence Synthesis for Evidence-Based Medicine
339(24)
Byron C. Wallace
Issa J. Dahabreh
Christopher H. Schmid
Joseph Lau
Thomas A. Trikalinos
12.1 Introduction
339(1)
12.2 Systematic reviews and meta-analysis: the premise and promise
340(3)
12.2.1 Uses of systematic reviews and meta-analyses
343(1)
12.3 The systematic review pipeline
343(5)
12.3.1 Formulating the research question
344(2)
12.3.2 Searching the literature
346(1)
12.3.3 Data extraction
347(1)
12.4 Statistical methods in meta-analysis
348(3)
12.4.1 Exploring heterogeneity
350(1)
12.5 Meta-analysis of complex datasets
351(2)
12.6 Accessing systematic reviews, meta-analyses and field synopses
353(1)
12.7 Conclusion
354(9)
References
356(7)
Chapter 13 Big Data and Population-Based Decision Support
363(20)
Michael A. Krall
Adi V. Gundlapalli
Matthew H. Samore
13.1 Introduction
363(2)
13.2 Core concepts
365(1)
13.3 Population health data
366(4)
13.3.1 Types of populations
366(1)
13.3.2 Database development and maintenance
367(1)
13.3.3 Population analytics and "big data"
368(2)
13.3.4 Government initiatives to improve big data mining techniques
370(1)
13.4 Decision support to improve identification and response to population health needs
370(2)
13.4.1 Measuring quality
370(1)
13.4.2 Forecasting resource needs
371(1)
13.4.3 Tracking disease burden
371(1)
13.4.4 Outbreak detection
371(1)
13.5 Decision support to implement interventions to address specific population health needs
372(2)
13.5.1 Treating disease
372(1)
13.5.2 Preventing disease
373(1)
13.5.3 Proactive care
373(1)
13.5.4 Medication stewardship
373(1)
13.5.5 Integrating data with interventions
374(1)
13.6 Quantitative methods
374(1)
13.7 Examples of population health decision support
375(3)
13.7.1 Field Uses of "big data"
375(2)
13.7.2 Examples of disease management
377(1)
13.8 Challenges
378(5)
Acknowledgments
378(1)
References
378(5)
Chapter 14 Clinical Decision Support for Personalized Medicine
383(34)
Brandon M. Welch
Kensaku Kawamoto
Brian Drohan
Kevin S. Hughes
14.1 Introduction
383(3)
14.2 Challenges to adoption of genomic and personalized medicine
386(2)
14.2.1 Complexity of genetics
386(1)
14.2.2 Inadequate physician training in genetics
386(2)
14.2.3 Limited number of genetics experts
388(1)
14.3 Clinical decision support as a solution to achieving genomic and personalized medicine
388(2)
14.3.1 History of CDS for personalized medicine
389(1)
14.4 Focal areas of CDS for personalized medicine
390(15)
14.4.1 Family health history
390(5)
14.4.2 Genetic testing
395(5)
14.4.3 Pharmacogenomics
400(4)
14.4.4 Whole genome sequencing
404(1)
14.5 Complexities and considerations
405(3)
14.5.1 Genomics knowledge base
406(1)
14.5.2 Software architectures for scalable knowledge dissemination
406(1)
14.5.3 Standards
407(1)
14.5.4 EHR systems
407(1)
14.6 Conclusions
408(9)
References
408(9)
SECTION IV THE TECHNOLOGY OF CLINICAL DECISION SUPPORT
Chapter 15 Decision Rules and Expressions
417(18)
Robert A. Jenders
15.1 Introduction
417(1)
15.2 Procedural knowledge
418(2)
15.3 Knowledge as production rules
420(4)
15.4 The hybrid approach for knowledge transfer: Arden Syntax
424(5)
15.5 Expression languages
429(1)
15.6 Standard data models for decision rules
430(1)
15.7 Toward further standardization: quality measures and health e-decisions
431(1)
15.8 Future work
432(1)
15.9 Conclusions
433(2)
References
433(2)
Chapter 16 Guidelines and Workflow Models
435(30)
Mor Peleg
Arturo Gonzalez-Ferrer
16.1 Introduction
435(4)
16.1.1 Increasing and standardizing quality of care via clinical practice guidelines
435(2)
16.1.2 Supporting management of routine medical actions via workflow systems
437(1)
16.1.3 The life cycle of health care process and decision support systems
438(1)
16.2 Supporting knowledge acquisition
439(3)
16.2.1 The quality of narrative guidelines
439(1)
16.2.2 The types of knowledge contained in narrative guidelines
439(1)
16.2.3 From narrative to formal representations of guidelines
440(2)
16.3 Formal methods for modeling and specifying CIGs
442(8)
16.3.1 Task-network models
443(6)
16.3.2 Other CIG modeling methods
449(1)
16.4 Integration of guidelines with workflow
450(3)
16.5 CIG and workflow verification and exception-handling
453(2)
16.6 CIG and careflow enactment tools
455(1)
16.7 Process mining and improvement
455(1)
16.8 Discussion
456(9)
16.8.1 Successes
456(3)
16.8.2 Limitations
459(1)
16.8.3 Future research
459(2)
Acknowledgment
461(1)
References
461(3)
Recommended resources
464(1)
Chapter 17 Ontologies, Vocabularies and Data Models
465(34)
Stanley M. Huff
Thomas A. Oniki
Joseph F. Coyle
Craig G. Parker
Roberto A. Rocha
17.1 Introduction
465(1)
17.2 Referencing data in decision logic
466(1)
17.2.1 The curly braces problem
466(1)
17.3 The need for coded data
466(3)
17.3.1 Advantages of coded data
467(1)
17.3.2 Enabling a "Learning Health System" environment
467(1)
17.3.3 The opportunity and challenges of "Big Data"
468(1)
17.3.4 The challenge from REDCap
469(1)
17.4 Modeling formalisms
469(3)
17.4.1 Example models
469(1)
17.4.2 Terminology and models
470(1)
17.4.3 Terminologies and ontologies
471(1)
17.4.4 Referencing patient data based on models
472(1)
17.5 Issues of pre- and postcoordination
472(3)
17.5.1 Trade-offs of pre- and postcoordination
473(1)
17.5.2 Combinatorial explosion
474(1)
17.6 Placing information in the terminology model or the information model
475(1)
17.6.1 Overlap between the models
475(1)
17.7 Iso-semantic models
476(4)
17.7.1 "Assertion" model versus a supertype or context model
476(2)
17.7.2 Precoordinated versus postcoordinated models
478(1)
17.7.3 Iso-semantic use cases
478(1)
17.7.4 The need for transforms
479(1)
17.8 Data representation using name-value pairs
480(7)
17.8.1 HL7 example
482(1)
17.8.2 Another name-value pair alternative
482(2)
17.8.3 Implications of the name-value pair strategy for sharing data
484(1)
17.8.4 Logical vs. physical structure of the data
484(1)
17.8.5 LOINC vs. SNOMED observables
485(1)
17.8.6 Current formal modeling activities
485(2)
17.9 Terminology and the implementation of clinical decision support interventions
487(4)
17.9.1 Authoring and browsing applications
488(1)
17.9.2 Run-time terminology services
489(1)
17.9.3 Terminology as a modular component of an EHR
489(1)
17.9.4 Common terminology services (CTS)
490(1)
17.9.5 Challenges of importing decision logic
490(1)
17.9.6 Data normalization
490(1)
17.9.7 Protecting against changes in the terminology
491(1)
17.10 Contextual restrictions within the terminology
491(3)
17.10.1 Using context to modulate subsumption logic
492(1)
17.10.2 Dimensions of context
492(1)
17.10.3 Context and terminology services
493(1)
17.10.4 Context and individualized care
493(1)
17.10.5 Representation of contextual restrictions
493(1)
17.11 What needs to be done to allow sharing of decision logic?
494(2)
17.11.1 The problem
494(1)
17.11.2 Standard terminologies
494(1)
17.11.3 Binding to detailed clinical models
494(1)
17.11.4 A repository for collecting and sharing clinical models
495(1)
17.11.5 Selecting models for interoperability
495(1)
17.11.6 Interoperable models and standard application programming interfaces
495(1)
17.12 Conclusions
496(3)
References
496(3)
Chapter 18 Grouped Knowledge Elements
499(16)
Margarita Sordo
Aziz A. Boxwala
18.1 Introduction
499(1)
18.2 Clinical documentation
500(3)
18.3 Order sets
503(3)
18.4 Current standards for grouped knowledge elements
506(5)
18.4.1 HL7 clinical document architecture
506(1)
18.4.2 HL7 CDS knowledge artifact
507(4)
18.5 Conclusions
511(4)
References
513(2)
Chapter 19 Infobuttons and Point of Care Access to Knowledge
515(36)
Guilherme Del Fiol
Hong Yu
James J. Cimino
19.1 Introduction
515(1)
19.2 Understanding and addressing clinician information needs
516(6)
19.2.1 Information needs in clinical practice
516(2)
19.2.2 Use and impact of online information resources
518(1)
19.2.3 Barriers to use of online information resources
519(1)
19.2.4 Understanding the context of information needs
520(1)
19.2.5 History of linking clinical information systems to online resources
521(1)
19.3 Infobuttons
522(9)
19.3.1 History of infobutton development
522(4)
19.3.2 Managing infobuttons
526(4)
19.3.3 Uptake, user satisfaction, and impact of infobuttons on clinicians' decision making
530(1)
19.4 Question answering systems
531(6)
19.4.1 History of question answering
531(1)
19.4.2 Clinical question answering
531(1)
19.4.3 The QA framework
532(4)
19.4.4 A fully implemented biomedical QA system: AskHERMES
536(1)
19.4.5 A key challenge for future clinical QA
537(1)
19.5 The HL7 standard for context-aware decision support
537(4)
19.5.1 Motivation
537(1)
19.5.2 Context information model
538(1)
19.5.3 Web services implementation
538(1)
19.5.4 Adoption
539(1)
19.5.5 Recent work
540(1)
19.5.6 The librarian infobutton tailoring environment (LITE)
540(1)
19.6 Ongoing and future research
541(1)
19.6.1 Other EHR integration approaches enabled by SOA-based infobuttons
541(1)
19.6.2 Knowledge summarization techniques and context-specific knowledge summaries
542(1)
19.6.3 Other areas of ongoing research
542(1)
19.7 Conclusions
542(9)
References
543(8)
Chapter 20 Formal Representations and Semantic Web Technologies
551(48)
Alan Rector
Davide Sottara
20.1 Introduction
551(1)
20.2 Background
552(3)
20.2.1 Kinds of "knowledge"
552(1)
20.2.2 Representation languages, syntax, semantics and linguistics
553(1)
20.2.3 "Ontology"
554(1)
20.3 Semantics of terminologies: taxonomies, ontologies, classification, thesauri, and other hierarchical structures
555(3)
20.3.1 Hierarchies, taxonomies, and partonomies
555(1)
20.3.2 Logic-based models (logic-based ontologies)
556(1)
20.3.3 Classifications
557(1)
20.3.4 Thesauri
557(1)
20.3.5 Object-oriented and other structured models
558(1)
20.3.6 Controlled vocabularies
558(1)
20.3.7 Grammatical expressions
558(1)
20.4 A brief introduction to logic-based ontologies and OWL
558(21)
20.4.1 Basics
558(4)
20.4.2 Composition
562(3)
20.4.3 More advanced notions
565(2)
20.4.4 Beyond OWL-EL: OWL-DL and more expressive constructs
567(3)
20.4.5 Common pitfalls
570(4)
20.4.6 Binding and value sets
574(1)
20.4.7 Axiom-based vs template-based formalisms: OWL and UML
575(2)
20.4.8 Developing in OWL
577(2)
20.5 Other issues in terminology
579(2)
20.5.1 Ontological issues and upper ontologies
579(2)
20.5.2 Evidence for the correctness of terminologies and ontologies
581(1)
20.5.3 A note on SNOMED CT
581(1)
20.6 Introduction to RDF, SKOS, SPARQL and network formalisms
581(4)
20.6.1 Basics
581(1)
20.6.2 RDF Schema (RDFs)
582(1)
20.6.3 SPARQL
583(1)
20.6.4 Tutorials and tools
583(1)
20.6.5 RDF and OWL
584(1)
20.6.6 SKOS - an RDF vocabulary for thesauri
585(1)
20.7 Basics of rules
585(4)
20.7.1 Kinds of rules
586(2)
20.7.2 Example rule systems and applications
588(1)
20.8 Ontologies and rules
589(4)
20.8.1 Kinds of rules from a description logic perspective
590(1)
20.8.2 Semantic web rule and query languages
590(3)
20.9 Reasoning algorithms
593(1)
20.10 Conclusions
593(6)
References
594(5)
Chapter 21 The Role of Standards
599(20)
Kensaku Kawamoto
Robert A. Greenes
21.1 The case for standards
599(1)
21.2 CDS development with and without standards
599(3)
21.3 Areas in need of standardization
602(1)
21.4 Assessment of current state of CDS standards and needed future work
603(6)
21.5 Beyond the standards - what is needed for widespread CDS adoption?
609(2)
21.5.1 The vision for standards-enabled, scalable CDS
609(1)
21.5.2 Resources needed
610(1)
21.6 How important are standards?
611(3)
21.7 Vision for potential future impact of standards
614(5)
References
614(5)
SECTION V ADOPTION OF CLINICAL DECISION SUPPORT
Chapter 22 Cognitive Considerations for Health Information Technology
619(22)
Amy Franklin
Jiajie Zhang
22.1 Introduction
619(2)
22.2 Challenges for cognitive support in health care
621(2)
22.2.1 Unintended consequences
622(1)
22.2.2 Complex team environments
622(1)
22.3 Developing cognitive support: distributed cognition
623(6)
22.3.1 Distributed cognition between individuals and artifacts
624(1)
22.3.2 The power of external representations
625(1)
22.3.3 Distributed cognition across individuals
625(1)
22.3.4 Cognitive work in distributed system
626(1)
22.3.5 Organizational memory
626(2)
22.3.6 Group decision making and technology
628(1)
22.3.7 Group decision making in clinical contexts
628(1)
22.4 Building systems with distributed cognition in mind
629(1)
22.4.1 TURF: A framework for HIT usability and cognitive support
629(1)
22.5 Developing tools to support cognition
630(4)
22.5.1 Situation awareness
631(2)
22.5.2 Data aggregation
633(1)
22.5.3 Visualization
633(1)
22.6 Summary
634(7)
References
635(6)
Chapter 23 Organizational and Cultural Change
641(24)
Joan S. Ash
Timothy H. Hartzog
23.1 Introduction
641(4)
23.1.1 Framework for addressing organizational change and transitions
642(2)
23.1.2 Identifying the barriers and facilitators for implementing CDS
644(1)
23.1.3 Stakeholder analyses and Lewin's force field analysis as useful techniques
644(1)
23.2 Organizational issues related to clinical decision support
645(7)
23.2.1 Differences among kinds of organizations and cultures
645(2)
23.2.2 Issues of control, autonomy, and trust
647(2)
23.2.3 Difference between commercial and locally produced decision support
649(1)
23.2.4 Upsides and downsides to clinical decision support from the user perspective
649(1)
23.2.5 Cognitive, emotional, and environmental issues
650(1)
23.2.6 Addressing the issues judiciously
651(1)
23.3 Planning with these issues in mind
652(4)
23.3.1 Vision and philosophy
654(1)
23.3.2 Organizing for decision making
654(2)
23.4 Development, implementation, and modification
656(3)
23.4.1 Preparing
656(1)
23.4.2 Committee work
657(1)
23.4.3 Providing resources to support and train
657(1)
23.4.4 Strategies
658(1)
23.5 Evaluation and maintenance
659(2)
23.5.1 Have data to back you up and gain involvement: Impact assessment and other techniques
659(1)
23.5.2 Soliciting clinician feedback
659(1)
23.5.3 Knowledge management
659(1)
23.5.4 The importance of ongoing organizational support
660(1)
23.6 Summary and conclusions
661(4)
Acknowledgments
661(1)
Resources
661(1)
References
661(4)
Chapter 24 Managing the Investment in Clinical Decision Support
665(24)
John Glaser
Tonya Hongsermeier
24.1 Introduction
665(1)
24.2 Clinical knowledge management
666(5)
24.2.1 Management of clinical decision support as a component of clinical knowledge management
666(1)
24.2.2 The boundaries of clinical knowledge management
667(1)
24.2.3 The key functions of clinical knowledge management
668(1)
24.2.4 The business case for clinical knowledge management investment
669(2)
24.3 Organization of the effort
671(6)
24.3.1 Objectives of organization
671(1)
24.3.2 Examples of approaches
672(1)
24.3.3 Clinical knowledge management organizations at Partners Healthcare and Intermountain Healthcare
673(1)
24.3.4 Observations on organization
674(3)
24.4 Key IT strategies and considerations
677(5)
24.4.1 Legacy systems
678(2)
24.4.2 Knowledge management tools
680(1)
24.4.3 Foundations
681(1)
24.5 Evaluation of the impact and value of knowledge management
682(5)
24.5.1 Alignment
683(2)
24.5.2 Performance
685(1)
24.5.3 Knowledge management function and organizational learning
686(1)
24.6 Conclusions
687(2)
References
687(2)
Chapter 25 A Clinical Decision Support Implementation Guide: Practical-Considerations
689(22)
Donald Levick
Jerome Osheroff
25.1 Introduction
689(1)
25.1.1 Source material for this chapter
689(1)
25.2 Foundational issues
689(8)
25.2.1 Definition of CDS and the "CDS five rights"
690(1)
25.2.2 Organizational support
690(1)
25.2.3 IT support
691(1)
25.2.4 Measurement
691(1)
25.2.5 The CDS committee
692(1)
25.2.6 The physician champion
693(1)
25.2.7 Engaging stakeholders and communication
693(2)
25.2.8 Assessing the readiness for change
695(1)
25.2.9 System issues: Hardware and infrastructure
695(1)
25.2.10 Software
696(1)
25.3 Implementation issues
697(10)
25.3.1 Identifying specific CDS objectives
697(2)
25.3.2 The CDS Five Rights
699(1)
25.3.3 The CDS package
699(1)
25.3.4 CDS intervention types
699(2)
25.3.5 CDS and computerized provider order entry (CPOE) implementation are not the same thing
701(1)
25.3.6 Selecting CDS interventions
701(1)
25.3.7 Connecting CDS interventions to organizational priorities
701(1)
25.3.8 Using a worksheet to determine CDS interventions
702(1)
25.3.9 Workflow
702(2)
25.3.10 Selecting an intervention package
704(1)
25.3.11 Configuring the intervention
705(1)
25.3.12 Factors to consider when building the intervention
705(1)
25.3.13 Approval process for interventions
706(1)
25.3.14 Intervention go-live
706(1)
25.4 Conclusions
707(4)
Acknowledgment
708(1)
References
708(3)
Chapter 26 Legal and Regulatory Issues Related to the Use of Clinical Software in Health Care Delivery
711(30)
Steven H. Brown
Randolph A. Miller
26.1 Introduction
711(1)
26.2 Legal issues related to using embedded and free-standing decision support software in clinical settings
712(16)
26.2.1 Software used in medical devices
715(7)
26.2.2 CDS software used by licensed practitioners during medical practice
722(6)
26.3 Responsibility for CDS software at the institutional level and potential governmental regulation
728(10)
26.3.1 The complexity of institutional clinical software environments
728(1)
26.3.2 Past and current FDA regulation of clinical software systems
729(2)
26.3.3 1997 Health care and informatics consortium recommendations
731(3)
26.3.4 Meaningful use and clinical decision support
734(4)
26.4 Conclusion
738(3)
Acknowledgment
738(1)
References
739(2)
Chapter 27 Consumers and Clinical Decision Support
741(32)
Nananda Col
Rosaly Correa-de-Araujo
27.1 Introduction
741(4)
27.1.1 Consumerism in health
741(1)
27.1.2 Expectations about engaged health care consumers
741(1)
27.1.3 Rise in consumers' interest in being involved in their health and shared decision making
742(1)
27.1.4 The internet as a source of health information
743(1)
27.1.5 Benefiting from online health information
744(1)
27.2 Clinical decision support
745(15)
27.2.1 Consumer clinical decision support systems and tools
747(6)
27.2.2 Theoretical underpinnings of consumers clinical decision support
753(3)
27.2.3 Factors affecting the use and uptake of consumer CDS within clinical environments
756(1)
27.2.4 Evidence base underlying the impact of consumer clinical decision support
756(4)
27.3 Opportunities to improve consumer clinical decision support
760(13)
References
762(11)
SECTION VI THE JOURNEY TO WIDESPREAD USE OF CLINICAL DECISION SUPPORT
Chapter 28 A Clinical Knowledge Management Program
773(46)
Roberto A. Rocha
Saverio M. Maviglia
Margarita Sordo
Beatriz H. Rocha
28.1 Introduction and program overview
773(12)
28.1.1 Motivation and opportunities
773(4)
28.1.2 Requirements
777(4)
28.1.3 Implementation
781(4)
28.2 Knowledge engineering process
785(8)
28.2.1 Knowledge lifecycle
785(3)
28.2.2 Knowledge modeling
788(5)
28.3 Software infrastructure
793(9)
28.3.1 General capabilities
793(2)
28.3.2 Tools and services
795(7)
28.4 Integration with clinical systems
802(6)
28.4.1 Knowledge engineering activities
802(2)
28.4.2 Integration of knowledge assets
804(1)
28.4.3 Clinical decision support interventions
804(4)
28.5 Future directions
808(2)
28.5.1 Advanced clinical decision support
808(1)
28.5.2 Advanced curation and maintenance tools
809(1)
28.6 Conclusions
810(9)
Acknowledgments
811(1)
References
811(8)
Chapter 29 Integration of Knowledge Resources into Applications to Enable CDS
819(32)
Kensaku Kawamoto
Emory Fry
Robert Greenes
29.1 Introduction
819(1)
29.2 Generic system architectures, examples, and their pros and cons
820(4)
29.2.1 Range of architectural approaches
821(3)
29.3 Philosophy on role of knowledge resources
824(2)
29.3.1 Knowledge resource-centric knowledge integration architecture
824(1)
29.3.2 Application-centric knowledge integration architecture
825(1)
29.4 Role of the EHR/CIS architecture
826(2)
29.4.1 Trend to more open, component-based, service-oriented architecture
826(1)
29.4.2 Implications for knowledge integration
827(1)
29.5 Scaling considerations for CDS
828(13)
29.5.1 Rule syntax and execution
829(1)
29.5.2 Data model
830(2)
29.5.3 Semantics and terminology services
832(1)
29.5.4 Localization
833(1)
29.5.5 Example localization approach in the socratic grid project
834(1)
29.5.6 State management
835(4)
29.5.7 Human tasks
839(2)
29.5.8 Health eDecisions
841(1)
29.6 Other issues to be considered
841(2)
29.6.1 Legal issues
841(1)
29.6.2 Reusability and standards
842(1)
29.7 Examples of specific approaches
843(1)
29.7.1 OpenCDS
843(1)
29.7.2 Socratic Grid
843(1)
29.8 Conclusions
844(7)
References
846(5)
Chapter 30 Looking Ahead: The Road to Broad Adoption
851(14)
Robert A. Greenes
30.1 Where we are now
851(3)
30.2 Impediments still with us
854(1)
30.3 Need for new mechanisms
854(4)
30.4 Organization of process
858(3)
30.5 A possible paradigm for future CDS
861(1)
30.6 Looking ahead: epilogue as prologue
862(3)
References
863(2)
Index 865
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.