Clinical Decision Support Systems (eBook)

Theory and Practice

Eta S. Berner (Herausgeber)

eBook Download: PDF

2016 | 3. Auflage
XI, 319 Seiten
Springer-Verlag
978-3-319-31913-1 (ISBN)

Infos

Building on the success of the previous editions, this fully updated book once again brings together worldwide experts to illustrate the underlying science and day-to-day use of decision support systems in clinical and educational settings.

Topics discussed include:

-Mathematical Foundations of Decision Support Systems
-Design and Implementation Issues
-Ethical and Legal Issues in Decision Support
-Clinical Trials of Information Interventions
-Hospital-Based Decision Support
-Real World Case Studies 

Eta S. Berner, EdD, is a Professor of Health Informatics at the University of Alabama at Birmingham.

Inhalt

Preface 6
Contents 8
Contributors 10
Chapter 1: Overview of Clinical Decision Support Systems 13
1.1 Types of Clinical Decision Support Systems 14
1.1.1 Knowledge-Based Clinical Decision Support Systems 15
1.1.2 Nonknowledge-Based Clinical Decision Support Systems 17
1.2 Effectiveness of Clinical Decision Support Systems 17
1.3 Implementation Challenges 20
1.4 Future Uses of Clinical Decision Support Systems 21
1.5 Guidelines for Selecting and Implementing Clinical Decision Support Systems 22
1.5.1 Assuring That Users Understand the Limitations 22
1.5.2 Assuring That the Knowledge Is from Reputable Sources 23
1.5.3 Assuring That the System Is Appropriate for the Local Site 23
1.5.4 Assuring That Users Are Properly Trained 24
1.5.5 Monitoring Proper Utilization of the Installed Clinical Decision Support Systems 25
1.5.6 Assuring the Knowledge Base Is Monitored and Maintained 25
1.6 Conclusion 26
References 26
Chapter 2: Mathematical Foundations of Decision Support Systems 30
2.1 Review of Logic and Probability 31
2.1.1 Set Theory 31
2.1.2 Boolean Logic 34
2.1.3 Probability 37
2.1.4 Bayes’ Rule 39
2.1.5 Informal Logic 41
2.2 The General Model of Knowledge-Based Decision Support Systems 42
2.2.1 Input 43
2.2.2 Inference Engine 45
2.2.3 Knowledge Base 47
2.2.4 Output 47
2.3 Nonknowledge-Based Systems 47
2.3.1 Neural Networks 48
2.3.2 Genetic Algorithms 49
2.4 Model for Evaluating the Appropriateness of CDSS 49
2.5 Summary 51
References 51
Chapter 3: Data Mining and Clinical Decision Support Systems 55
3.1 Introduction 55
3.2 Data Mining and Clinical Decision Support Systems 56
3.3 Data Mining and Statistical Pattern Recognition 58
3.4 Supervised Versus Unsupervised Learning 59
3.4.1 Supervised Learning 59
3.4.2 A Priori Probability 60
3.4.3 Unsupervised Learning 60
3.4.4 Classifiers for Supervised Learning 61
3.4.5 Decision Trees 61
3.4.6 Logistic Regression 62
3.4.7 Neural Networks 64
3.4.8 Nearest Neighbor Classifier 65
3.5 Evaluation of Classifiers 66
3.5.1 ROC Graphs 66
3.5.2 Kolmogorov-Smirnov Test 68
3.6 Unsupervised Learning 69
3.6.1 Cluster Analysis 69
3.6.2 Gene Expression Data Analysis 70
3.7 Other Techniques 71
3.7.1 Genetic Algorithms 71
3.7.2 Biological Computing 72
3.7.3 Quantum Computing 73
3.7.4 Incorporating Fuzzy Logic and Other Hybrid Methods 74
3.7.5 Big Data Analytics 74
3.8 Conclusions 75
References 75
Chapter 4: Usability and Clinical Decision Support 79
4.1 CDSS Usability and Functionality 79
4.1.1 Clinical Challenges 81
Disseminating Existing Knowledge About CDSS 81
Clinical Workflow Integration 81
Keeping Abreast of New Clinical Research Developments 82
4.1.2 Technical Issues 82
Variety in Types of Data 82
Synthesizing Clinical Knowledge 83
4.2 Strategies to Improve the Usability of CDSS 83
4.2.1 Perform User-Centered Design 85
4.2.2 Create Approaches for Sharing CDS Knowledge, Modules and Services 86
4.2.3 Enhance Quality of Knowledge Base to Support Multimorbidity Decisions 87
4.2.4 Integrate CDSS with the EHR 88
4.2.5 Reduce Errors by Learning from Previous Experience 89
4.3 Safety-Enhanced Design and Usability Assessment 89
4.3.1 Safety-Enhanced Design (SED) 90
4.3.2 Rapid Usability Assessment (RUA) 90
4.3.3 Usability Testing 91
Card Sorting 92
Online Surveys 92
4.4 Summary 93
References 93
Chapter 5: Newer Architectures for Clinical Decision Support 97
5.1 Service-Oriented Architecture: Definition, Benefits, Challenges, History 98
5.2 Benefits and Challenges of SOA for Health IT Systems in General and CDSS in Particular 100
5.3 SOA Services and Capabilities Needed for CDS 102
5.4 Healthcare Services Specification Project 102
5.5 OpenCDS 103
5.6 CDS Consortium 103
5.7 Health eDecisions and Clinical Quality Framework 103
5.8 Healthcare Services Platform Consortium 104
5.9 Other Individual Efforts 104
5.10 Future Directions 105
5.11 Conclusions 105
References 105
Chapter 6: Best Practices for Implementation of Clinical Decision Support 108
6.1 Knowledge Synthesis 109
6.2 Knowledge Formalization 110
6.3 Knowledge Localization 112
6.3.1 Resource Constraints 113
6.3.2 Workflow 113
6.3.3 EHR Functionality 114
6.3.4 CDSS Design 114
6.3.5 CDS Modalities 115
6.3.6 Level of Enforcement 116
6.3.7 Participatory Design 116
6.4 Evaluation and Learning 116
6.5 Summary and Conclusions 117
References 117
Chapter 7: Impact of National Policies on the Use of Clinical Decision Support 119
7.1 United States Federal Government Clinical Decision Support Initiatives 120
7.2 Technical Foundations 121
7.3 Workflow and Knowledge Management 123
7.4 Health eDecisions and the Clinical Quality Framework 123
7.5 Clinical Decision Support and the HITECH Act 124
7.6 2015 CDS Certification Criteria 127
7.6.1 CDS Intervention Interaction 127
7.6.2 CDS Configuration 128
7.6.3 Evidence-Based Decision Support Interventions 128
7.6.4 Linked Referential CDS 129
7.6.5 Source Attributes 129
7.7 Implementation and Optimization Guidance 130
7.8 The Patient Protection and Affordable Care Act 130
7.9 Protecting Access to Medicare Act of 2014 (PAMA) 130
7.10 Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) 132
7.11 Impact of Federal Programs on CDS 133
7.12 FDA Regulation of Clinical Decision Support 133
7.13 Clinical Decision Support for Immunizations (CDSi) 134
7.14 Challenges 135
7.15 Future Initiatives 136
7.16 Summary and Conclusion 136
References 136
Chapter 8: Ethical and Legal Issues in Decision Support 139
8.1 Ethical Issues 140
8.1.1 Background and Current Research 140
8.1.2 Care Standards 141
8.1.3 Appropriate Use and Users 144
8.1.4 Professional Relationships 145
8.1.5 Decision Support in Genetics and Genomics 147
8.2 Regulation and the Law 148
8.2.1 Liability and Decision Support 148
8.2.2 Regulation of Clinical Decision Support Systems 150
8.3 Conclusion and Future Directions 152
References 152
Chapter 9: Evaluation of Clinical Decision Support 155
9.1 Strategies for CDSS Evaluation 156
9.2 Types of CDSS Evaluations 157
9.3 Types of Outcomes Assessed in CDSS Evaluations 158
9.4 Findings from Systematic Reviews of CDSS 158
9.5 Approach to Conducting an Evaluation of a CDSS 161
9.6 Challenges Associated with Evaluation of CDSS 165
9.7 Conclusions/Observations 167
References 168
Chapter 10: Decision Support for Patients 170
10.1 Introduction 170
10.2 Role of Consumer Health Informatics in Patient Care 171
10.2.1 Empowerment and Self-efficacy 172
10.2.2 Incorporating Patient Preferences 173
10.3 Interactive Tools for Patient Decision Support 174
10.3.1 Patient Decision Support for Diagnosis 174
10.3.2 Support for Patients’ Treatment Decisions 175
10.3.3 Other Areas of Decision Support for Patients 176
10.4 Usability of Patient Decision Support Tools 178
10.4.1 Intuitive Interface 178
10.4.2 Complete Coverage/Coordination 179
10.4.3 Hierarchical Presentation 179
10.4.4 Presentation of Materials Tailored to the Individual 179
10.4.5 Facilitate Quality Decision Making 179
10.5 Helping Patients Judge the Quality of Health Information 180
10.6 Patient Access, Literacy and Numeracy 181
10.7 The Future of Decision Support Systems for Patients 182
References 182
Chapter 11: Diagnostic Decision Support Systems 187
11.1 Definitions of Diagnosis 188
11.2 Human Diagnostic Reasoning 191
11.3 Historical Survey of Diagnostic Decision Support Systems 194
11.4 Developing, Implementing, Evaluating, and Maintaining Diagnostic Decision Support Systems 198
11.4.1 Clinical Domain Selection 199
11.4.2 Knowledge Base Construction and Maintenance 200
11.4.3 Diagnostic Algorithms and User Interfaces 201
11.4.4 Testing, Evaluation, and Quality Control 202
Appropriate Evaluation Design 202
Specification of Criteria for Determining Efficacy in the Evaluation 203
Evaluation of the Boundaries or Limitations 204
Identification of Potential Reasons for “Lack of System Effect” 204
11.4.5 Interface and Vocabulary Issues 205
11.4.6 Legal and Ethical Issues 206
11.5 Diagnostic Decision Support Systems Circa 2015 207
11.6 The Future of Diagnostic Decision Support Systems 208
References 210
Chapter 12: Use of Clinical Decision Support to Tailor Drug Therapy Based on Genomics 215
12.1 Opportunities for Integration of -Omic Technologies into CDSS 217
12.2 How Is Genomic Decision Support Different from Other Types of CDS? 217
12.3 State of the Evidence for Germline Pharmacogenomic Intervention 218
12.4 Who, What, and When to Test 221
12.5 Types of CDS Useful for Genomic Medicine 222
12.5.1 Passive Decision Support 222
12.5.2 Active Decision Support 223
12.5.3 Surveillance Decision Support Mechanisms 224
12.6 Standardized Representation of Genetic Variation 224
12.7 CDS Knowledge Bases 225
12.8 Examples of Genomic CDS in Practice 226
12.9 Direct-to-Consumer Genetic Testing 228
12.10 Conclusions 230
References 231
Chapter 13: Clinical Decision Support: The Experience at Brigham and Women’s Hospital/Partners HealthCare 233
13.1 Background 234
13.1.1 Brigham Integrated Computing System (BICS) 234
13.1.2 Longitudinal Medical Record (LMR) 236
13.2 Clinical Decision Support: Inpatient Applications and Assessment 236
13.2.1 Medication-Related Decision Support 236
General Applications 236
Reduction of Adverse Drug Events 238
Anticipatory Medication Decision Support 238
Medication-Specific Decision Support 239
13.2.2 Laboratory-Related Decision Support 239
Display of Charges at Test Ordering 239
Reduction in Redundant Testing 239
Tests Pending at Discharge 240
13.2.3 Radiology-Related Decision Support 241
Appropriateness of Ordered Studies 241
Notification About Critical Radiology Results 241
13.2.4 Transition of Care Support 242
13.2.5 Assessment of CPOE Impact on Users 242
13.3 Clinical Decision Support: Ambulatory Applications and Assessment 242
13.3.1 Ambulatory Medication Decision Support 242
13.3.2 Laboratory-Related Decision Support 243
13.3.3 Radiology-Related Decision Support 244
13.3.4 Clinical Reminders 244
13.3.5 Documentation-Related Decision Support 245
13.3.6 Problem-List Decision Support 245
13.3.7 Assessment of Ambulatory EHR Impact on Users 245
13.3.8 Patient-Centric Applications 245
13.4 Clinical Decision Support: Cost-Effectiveness 246
13.5 Overarching Lessons 246
13.6 Future Directions 247
References 247
Chapter 14: Clinical Decision Support at Intermountain Healthcare 251
14.1 The HELP System 254
14.2 Categories of Decision Support Technologies 256
14.3 Alerting Systems 256
14.3.1 Computerized Laboratory Alerting System 256
14.3.2 Unit-Wide Notification of Ventilator Disconnections 258
14.3.3 Early Prediction of Deterioration (ePOD) 261
14.4 Critiquing Systems 262
14.4.1 Blood Product Order Critiquing 262
14.5 Suggestion Systems 264
14.5.1 Ventilator Management 264
14.6 Diagnostic Decision Support in the HELP System 266
14.7 Diagnostic Applications 267
14.7.1 Adverse Drug Events 267
14.7.2 Nosocomial Infections 269
14.8 Detecting Pneumonia in the Emergency Department 270
14.8.1 Antibiotic Assistant 271
14.9 Research into Complex Peri-diagnostic Applications 272
14.9.1 Assisting Data Collection 273
14.9.2 Assessing the Quality of Medical Reports 274
14.10 Summary 276
References 277
Chapter 15: Decision Support During Inpatient Care Provider Order Entry: Vanderbilt’s WizOrder Experience 281
15.1 Basic Care Provider Order Entry System Functionality 283
15.1.1 Creating Orders 285
15.1.2 Displaying Active Orders 287
15.1.3 Modifying and Finalizing Orders 288
15.1.4 Displaying Information and Providing Complex Decision Support 288
15.2 Philosophy Underlying Decision Support During Care Provider Order Entry 289
15.3 Roles for Decision Support Within Care Provider Order Entry: Categories of Interventions 292
15.3.1 Creating Legible, Complete, Correct, Rapidly Actionable Orders 292
15.3.2 Providing Patient-Specific Clinical Decision Support 292
15.3.3 Optimizing Clinical Care (Improved Workflow, More Cost-Effective and Regulatory-Compliant) 292
15.3.4 Providing Just-in-Time, Focused Education Relevant to Patient Care 294
15.4 Critical Points at Which to Implement Decision Support Within Care Provider Order Entry 295
15.4.1 Stage of Care Provider Order Entry Session Initiation 295
15.4.2 Stage of Selecting Care Provider Order Entry Patient from Hospital Ward Census 295
15.4.3 Stage of Individual Patient Session Initiation 296
15.4.4 Stage of Individual (Single) Order Selection 299
15.4.5 Stage of Individual (Single) Order Construction 300
15.4.6 Stage of Individual Order Completion 300
15.4.7 Stage of Ordering Session Completion 302
15.5 Care Provider Order Entry Intervention Approaches: From Subtle to Intrusive 305
15.5.1 Incidental Display of Relevant Information 305
15.5.2 Incidental Display of Linked Educational Opportunities 305
15.5.3 Interactive Sequential Advice for User-Directed Clinical Activity 306
15.5.4 Recallable Best Practice Guidelines with Actionable Pre-formed Pick List Selections 307
15.5.5 Pop-Up Alerts That Interrupt Workflow and Require a Response for the User to Continue 307
15.5.6 Complex, Computer-Based Protocols That Interact with the User to Make Patient-Specific Calculations and Recommendations 307
15.6 CPOE Systems Circa 2015 310
15.7 Conclusion 311
References 311
Index 317

Produkt-Details

• Erscheint lt. Verlag: 26.7.2016
• Reihe/Serie: Health Informatics
• Zusatzinfo: XI, 313 p. 42 illus., 6 illus. in color.
• Verlagsort: Cham
• Sprache: englisch
• Themenwelt: Medizin / Pharmazie ► Allgemeines / Lexika
• Schlagworte: Care • Computer systems • Data Mining • Health Informatics • Mathematics
• ISBN-10: 3-319-31913-2 / 3319319132
• ISBN-13: 978-3-319-31913-1 / 9783319319131