Reinventing Clinical Decision Support
Data Analytics, Artificial Intelligence, and Diagnostic Reasoning
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
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
Paul Cerrato, MA, has more than 30 years of experience working in healthcare as a medical journalist, research analyst, clinician, and educator. He has written extensively on clinical medicine, clinical decision support, electronic health records, protected health information security, and practice management. He has served as the Editor of InformationWeek Healthcare, Executive Editor of Contemporary OB/GYN, Senior Editor of RN Magazine, and contributing writer/editor for the Yale University School of Medicine, the American Academy of Pediatrics, InformationWeek, Medscape, Healthcare Finance News, IMedicalapps.com, and MedpageToday. The Health Information Management Systems Society (HIMSS) has listed Mr. Cerrato as one of the most influential columnists in healthcare IT. He has served as a guest lecturer or faculty member at the Columbia University College of Physicians and Surgeons, Harvard Medical School, and Vermont College. Among his achievements are 6 editorial awards from the American Business Media—often referred to as the Pulitzer Prize of business journalism—and the Gold Award from the American Society of Healthcare Publications Editors for best signed editorial.
John D. Halamka, MD, MS, president of the Mayo Clinic Platform, leads a portfolio of new digital platform businesses focused on transforming health by leveraging artificial intelligence, machine learning, and an ecosystem of partners for the Mayo Clinic. He is a practicing emergency medicine physician. Previously, Dr. Halamka was executive director of the Health Technology Exploration Center for Beth Israel Lahey Health in Massachusetts. Previously, he was chief information officer at Beth Israel Deaconess Medical Center for more than 20 years. In addition, he was the International Healthcare Innovation Professor at Harvard Medical School. As the leader for innovation at the $7 billion Beth Israel Lahey Health, he oversaw digital health relationships with industry, academia, and government worldwide. As a Harvard Medical School professor, he served the George W. Bush administration, the Obama administration, and governments around the world planning their health care information (IT) strategies.