Reinventing Clinical Decision Support-Data Analytics, Artificial Intelligence, and Diagnostic Reasoning

✍ Scribed by Paul Cerrato (Author); John Halamka (Author)

• Publisher: Taylor & Francis
• Year: 2020
• Leaves: 183
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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

Image Analysis

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

Network Medicine

Chapter 7: Precision Medicine

Addressing Genetic Predisposition

Pharmacogenomics

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