This book takes an in-depth look at the emerging technologies that are transforming the way clinicians manage patients, while at the same time emphasizing that the best practitioners use both artificial and human intelligence to make decisions.
AI and machine learning are explored at length, with plain clinical English explanations of convolutional neural networks, back propagation, and digital image analysis. Real-world examples of how these tools are being employed are also discussed, including their value in diagnosing diabetic retinopathy, melanoma, breast cancer, cancer metastasis, and colorectal cancer, as well as in managing severe sepsis.
With all the enthusiasm about AI and machine learning, it was also necessary to outline some of criticisms, obstacles, and limitations of these new tools. Among the criticisms discussed: the relative lack of hard scientific evidence supporting some of the latest algorithms and the so-called black box problem. A chapter on data analytics takes a deep dive into new ways to conduct subgroup analysis and how it’s forcing healthcare executives to rethink the way they apply the results of large clinical trials to everyday medical practice. This re-evaluation is slowly affecting the way diabetes, heart disease, hypertension, and cancer are treated. The research discussed also suggests that data analytics will impact emergency medicine, medication management, and healthcare costs.
An examination of the diagnostic reasoning process itself looks at how diagnostic errors are measured, what technological and cognitive errors are to blame, and what solutions are most likely to improve the process. It explores Type 1 and Type 2 reasoning methods; cognitive mistakes like availability bias, affective bias, and anchoring; and potential solutions such as the Human Diagnosis Project. Finally, the book explores the role of systems biology and precision medicine in clinical decision support and provides several case studies of how next generation AI is transforming patient care.
Chapter 1: Clinical Reasoning and Diagnostic Errors
Measuring Diagnostic Errors
Understanding the Multiple Causes of Diagnostic Errors
Type 1 and Type 2 Thinking
Combining Cognitive Approaches
Listening More, Talking Less
Chapter 2: The Promise of Artificial Intelligence and Machine Learning
Machine Learning Impacts Several Medical Specialties
AI and Medication Management
Chapter 3: AI Criticisms, Obstacles, and Limitations
Explainability Remains a Challenge
Generalizability Remains Elusive
Addressing Hype, Fraud, and Misinformation
Chapter 4: CDS Systems: Past, Present, and Future
CDS Has Improved Dramatically Over Time
How Effective Are CDS Systems?
Obstacles to CDS Implementation and Effectiveness
Commercially Available CDS Systems
Chapter 5: Reengineering Data Analytics
The Future of Subgroup Analysis
Predicting MS and Emergency Response
Big Data Meets Medication Management
The Role of Data Analytics in Cancer Risk Assessment
Impact of Data Analytics on Healthcare Costs
Chapter 6: Will Systems Biology Transform Clinical Decision Support?
Redefining Health and Disease
Is Systems Biology Ready for Prime Time Medicine?
The Whole Is Greater than the Sum of its Parts
Chapter 7: Precision Medicine
Addressing Genetic Predisposition
Chapter 8: Reinventing Clinical Decision Support: Case Studies
Improving Patient Scheduling, Optimizing ED Functioning
Embracing Mobile Tools
Technological Approach to Diagnostic Error Detection
Promising Solutions, Unrealistic Expectations