Patient Care under Uncertainty

Description

How cutting-edge economics can improve decision-making methods for doctors Although uncertainty is a common element of patient care, it has largely been overlooked in research on evidence-based medicine. Patient Care under Uncertainty strives to correct this glaring omission. Applying the tools of economics to medical decision making, Charles Manski shows how uncertainty influences every stage, from risk analysis to treatment, and how this can be reasonably confronted. In the language of econometrics, uncertainty refers to the inadequacy of available evidence and knowledge to yield accurate information on outcomes. In the context of health care, a common example is a choice between periodic surveillance or aggressive treatment of patients at risk for a potential disease, such as women prone to breast cancer. While these choices make use of data analysis, Manski demonstrates how statistical imprecision and identification problems often undermine clinical research and practice. Reviewing prevailing practices in contemporary medicine, he discusses the controversy regarding whether clinicians should adhere to evidence-based guidelines or exercise their own judgment. He also critiques the wishful extrapolation of research findings from randomized trials to clinical practice. Exploring ways to make more sensible judgments with available data, to credibly use evidence, and to better train clinicians, Manski helps practitioners and patients face uncertainties honestly. He concludes by examining patient care from a public health perspective and the management of uncertainty in drug approvals. Rigorously interrogating current practices in medicine, Patient Care under Uncertainty explains why predictability in the field has been limited and furnishes criteria for more cogent steps forward.

Charles F. Manski is Board of Trustees Professor of Economics at Northwestern University and a member of the National Academy of Sciences. His books include Public Policy in an Uncertain World, Identification for Prediction and Decision, and Social Choice with Partial Knowledge of Treatment Response (Princeton).

"Medicine has made spectacular advances, but its techniques for approving and selecting treatments remain locked in statistical and experimental design methods developed in the first decades of the twentieth century. In this book, Manski gives an accessible, practical explanation for why failures to confront the ambiguities of treatment decisions harm patients. He describes how modern statistical decision theory, including his own contributions, can save lives if incorporated into medical training and the decision making of clinicians, standard-setting bodies, and regulatory agencies. This is a must-read for those who take the Hippocratic Oath seriously."-Daniel McFadden, Nobel Laureate in Economics "Manski proposes clear, powerful strategies for improving patient care amid the many uncertainties typifying healthcare-delivery environments. Patient Care under Uncertainty offers valuable insights that wise clinicians-and others working in healthcare systems or on health policy design-would do well to consider and to implement in practice."-John Mullahy, University of Wisconsin-Madison "A thoughtful critique of medical decision making, Patient Care under Uncertainty furthers clinical care and evidence-based medicine. Manski examines identification practices, introduces partial identification to a clinical audience, and builds our econometric/statistical toolkit. Just as social scientists have adopted randomized clinical trials, it would be worthwhile for clinicians to adopt Manski's rich approach to econometrics."-Ahmad von Schlegell, MD "In this book, Manski highlights the tension between evidence generation in medicine and its credible use in healthcare decisions. Relying on decades of research, he presents various decision-theoretic principles for making better choices in the face of uncertainty. Accessible to a wide range of audiences, this book is a must-read for anyone grappling with the place of evidence in medical choices."-Anirban Basu, University of Washington