For the past few years, the author, a renowned economist, has been applying the statistical tools of economics to decision making under uncertainty in the context of patient health status and response to treatment. He shows how statistical imprecision and identification problems affect empirical research in the patient-care sphere.

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.

The Power of Colleagues What happens when primary care clinicians meet together on set aside time in their practice settings to talk about their own patients? .....Complimenting quality metrics or performance measures through discussing the actual stories of individual patients and their clinician-patient relationships In these settings, how can clinicians pool their collective experience and apply that to ‘the evidence’ for an individual patient? .....Especially for patients who do not fit the standard protocols and have vague and worrisome symptoms, poor response to treatment, unpredictable disease courses, and/or compromised abilities for shared decision making What follows when discussion about individual patients reveals system-wide service gaps and coordination limitations? .....Particularly for patients with complex clinical problems that fall outside performance monitors and quality screens How can collaborative engagement of case-based uncertainties with one’s colleagues help combat the loneliness and helplessness that PCPs can experience, no matter what model or setting in which they practice? .....And where they are expected to practice coordinated, evidence-based, EMR-directed care These questions inspired Lucia Sommers and John Launer and their international contributors to explore the power of colleagues in “Clinical Uncertainty in Primary Care: The Challenge of Collaborative Engagement” and offer antidotes to sub-optimal care that can result when clinicians go it alone. From the Foreword: “Lucia Sommers and John Launer, with the accompanying input of their contributing authors, have done a deeply insightful and close-to-exhaustive job of defining clinical uncertainty. They identify its origins, components and subtypes; demonstrate the ways in which and the extent to which it is intrinsic to medicine...and they present a cogent case for its special relationship to primary care practice...‘Clinical Uncertainty in Primary Care’ not only presents a model of collegial collaboration and support, it also implicitly legitimates it.’’ Renee Fox, Annenberg Professor Emerita of the Social Sciences, University of Pennsylvania.

Manski argues that public policy is based on untrustworthy analysis. Failing to account for uncertainty in an uncertain world, policy analysis routinely misleads policy makers with expressions of certitude. Manski critiques the status quo and offers an innovation to improve both how policy research is conducted and how it is used by policy makers.

Getting the right diagnosis is a key aspect of health care - it provides an explanation of a patient's health problem and informs subsequent health care decisions. The diagnostic process is a complex, collaborative activity that involves clinical reasoning and information gathering to determine a patient's health problem. According to Improving Diagnosis in Health Care, diagnostic errors-inaccurate or delayed diagnoses-persist throughout all settings of care and continue to harm an unacceptable number of patients. It is likely that most people will experience at least one diagnostic error in their lifetime, sometimes with devastating consequences. Diagnostic errors may cause harm to patients by preventing or delaying appropriate treatment, providing unnecessary or harmful treatment, or resulting in psychological or financial repercussions. The committee concluded that improving the diagnostic process is not only possible, but also represents a moral, professional, and public health imperative. Improving Diagnosis in Health Care, a continuation of the landmark Institute of Medicine reports To Err Is Human (2000) and Crossing the Quality Chasm (2001), finds that diagnosis-and, in particular, the occurrence of diagnostic errorsâ€"has been largely unappreciated in efforts to improve the quality and safety of health care. Without a dedicated focus on improving diagnosis, diagnostic errors will likely worsen as the delivery of health care and the diagnostic process continue to increase in complexity. Just as the diagnostic process is a collaborative activity, improving diagnosis will require collaboration and a widespread commitment to change among health care professionals, health care organizations, patients and their families, researchers, and policy makers. The recommendations of Improving Diagnosis in Health Care contribute to the growing momentum for change in this crucial area of health care quality and safety.

Individuals and families make key decisions that impact many aspects of financial stability and determine the future of the economy. These decisions involve balancing current sacrifice against future benefits. People have to decide how much to invest in health care, exercise, their diet, and insurance. They must decide how much debt to take on, and how much to save. And they make choices about jobs that determine employment and unemployment levels. Forward-Looking Decision Making is about modeling this individual or family-based decision making using an optimizing dynamic programming model. Robert Hall first reviews ideas about dynamic programs and introduces new ideas about numerical solutions and the representation of solved models as Markov processes. He surveys recent research on the parameters of preferences--the intertemporal elasticity of substitution, the Frisch elasticity of labor supply, and the Frisch cross-elasticity. He then examines dynamic programming models applied to health spending, long-term care insurance, employment, entrepreneurial risk-taking, and consumer debt. Linking theory with data and applying them to real-world problems, Forward-Looking Decision Making uses dynamic optimization programming models to shed light on individual behaviors and their economic implications.

What makes a good doctor? It's not what you think. A doctor willing to face their own uncertainty in the face of illness and treatment might just be the best medicine. Too often we choose the wrong doctor for the wrong reasons. It doesn't have to be that way. In The Good Doctor, Ken Brigham, MD, and Michael M.E. Johns, MD, argue that we need to change the way we think about health care if we want to be the healthiest we can be. Counterintuitive as it may seem, uncertainty is integral to medicine, and you want a doctor who knows that: someone who sees you as the unique case you are, someone who knows that data isn't everything, someone who is able to change her mind as the information changes. For too long we've clung to the myth of the infallible doctor--one who assuredly tells us this is what's wrong and here is how I will cure you--and our health has suffered for it. Brigham and Johns propose a new model of medicine, one that is comfortable with ambiguity and that centers on an equal partnership between patient and doctor. Uncertainty, properly embraced, opens a new universe of possibilities.