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Decisionscape : how thinking like an artist can improve our decision-making
\"This book unpacks the organizing metaphor of the \"decisionscape,\" examining how psychological distance alters our decisions by dictating what we foreground and what we diminish, how our personal worldview influences how we interpret information, how the overall \"composition\" of the decisions we are faced with has an effect on how we react to them, and how our decisions are bounded and framed by the invisible forces of culture and context\"-- Provided by publisher.
Book
Shared decision‐making for older adults with cardiovascular disease
Shared decision‐making is appropriate for clinical decisions involving multiple reasonable options, which occur frequently in the cardiovascular care of older adults. The process includes the communication of relevant factual information between the patient and the clinician, elicitation of patient preferences, and a mutual agreement on the best course of action to meet the patient's personal goals. For older adults, there are common challenges and considerations with regard to shared decision‐making, some of which (eg, cognitive impairment) may be biologically linked to cardiovascular disease. There are tools designed to facilitate the shared decision‐making process, known as decision aids, which are broadly effective although have shortcomings when applied to older adults. Novel approaches in clinical research and health systems changes will go some way toward improving shared decision‐making for older adults, but the greatest scope for improvement may be within the grass roots areas of communication skills, interdisciplinary teamwork, and simply asking our patients what matters most.
Journal Article
Financial decision aid using multiple criteria : recent models and applications
This volume highlights recent applications of multiple-criteria decision-making (MCDM) models in the field of finance. Covering a wide range of MCDM approaches, including multiobjective optimization, goal programming, value-based models, outranking techniques, and fuzzy models, it provides researchers and practitioners with a set of MCDM methodologies and empirical results in areas such as portfolio management, investment appraisal, banking, and corporate finance, among others. The book addresses issues related to problem structuring and modeling, solution techniques, comparative analyses, as well as combinations of MCDM models with other analytical methodologies.
Book
Blind spots
When confronted with an ethical dilemma, most of us like to think we would stand up for our principles. But we are not as ethical as we think we are. In Blind Spots, leading business ethicists Max Bazerman and Ann Tenbrunsel examine the ways we overestimate our ability to do what is right and how we act unethically without meaning to. From the collapse of Enron and corruption in the tobacco industry, to sales of the defective Ford Pinto and the downfall of Bernard Madoff, the authors investigate the nature of ethical failures in the business world and beyond, and illustrate how we can become more ethical, bridging the gap between who we are and who we want to be.
eBook
Willful : how we choose what we do
A revelatory alternative to the standard economic models of human behavior that proposes an exciting new way to understand decision-making. Why do we do the things we do? The classical view of economics is that we are rational individuals, making decisions with the intention of maximizing our preferences. Behaviorists, on the other hand, see us as relying on mental shortcuts and conforming to preexisting biases. Richard Robb argues that neither explanation accounts for those things that we do for their own sake, and without understanding these sorts of actions, our picture of decision-making is at best incomplete. Robb explains how these choices made seemingly without reason belong to a realm of behavior he identifies as \"for-itself.\" A provocative combination of philosophy and economics that offers a key to many of our quixotic choices, this groundbreaking volume provides a new way to understand everything from how we formulate our desire to work to how we manage daily interactions.
Book
Computer knows best? The need for value-flexibility in medical AI
Artificial intelligence (AI) is increasingly being developed for use in medicine, including for diagnosis and in treatment decision making. The use of AI in medical treatment raises many ethical issues that are yet to be explored in depth by bioethicists. In this paper, I focus specifically on the relationship between the ethical ideal of shared decision making and AI systems that generate treatment recommendations, using the example of IBM’s Watson for Oncology. I argue that use of this type of system creates both important risks and significant opportunities for promoting shared decision making. If value judgements are fixed and covert in AI systems, then we risk a shift back to more paternalistic medical care. However, if designed and used in an ethically informed way, AI could offer a potentially powerful way of supporting shared decision making. It could be used to incorporate explicit value reflection, promoting patient autonomy. In the context of medical treatment, we need value-flexible AI that can both respond to the values and treatment goals of individual patients and support clinicians to engage in shared decision making.
Journal Article
Making hard decisions with DecisionTools
Teaching the fundamental ideas of decision analysis, this text avoids an overly technical explanation of the mathematics used in management science. This new version incorporates and implements the powerful Decision Tools Suite, a toolkit for risk and decision analysis.
Book
Assessing the Utility of ChatGPT Throughout the Entire Clinical Workflow: Development and Usability Study
Large language model (LLM)-based artificial intelligence chatbots direct the power of large training data sets toward successive, related tasks as opposed to single-ask tasks, for which artificial intelligence already achieves impressive performance. The capacity of LLMs to assist in the full scope of iterative clinical reasoning via successive prompting, in effect acting as artificial physicians, has not yet been evaluated.
This study aimed to evaluate ChatGPT's capacity for ongoing clinical decision support via its performance on standardized clinical vignettes.
We inputted all 36 published clinical vignettes from the Merck Sharpe & Dohme (MSD) Clinical Manual into ChatGPT and compared its accuracy on differential diagnoses, diagnostic testing, final diagnosis, and management based on patient age, gender, and case acuity. Accuracy was measured by the proportion of correct responses to the questions posed within the clinical vignettes tested, as calculated by human scorers. We further conducted linear regression to assess the contributing factors toward ChatGPT's performance on clinical tasks.
ChatGPT achieved an overall accuracy of 71.7% (95% CI 69.3%-74.1%) across all 36 clinical vignettes. The LLM demonstrated the highest performance in making a final diagnosis with an accuracy of 76.9% (95% CI 67.8%-86.1%) and the lowest performance in generating an initial differential diagnosis with an accuracy of 60.3% (95% CI 54.2%-66.6%). Compared to answering questions about general medical knowledge, ChatGPT demonstrated inferior performance on differential diagnosis (β=-15.8%; P<.001) and clinical management (β=-7.4%; P=.02) question types.
ChatGPT achieves impressive accuracy in clinical decision-making, with increasing strength as it gains more clinical information at its disposal. In particular, ChatGPT demonstrates the greatest accuracy in tasks of final diagnosis as compared to initial diagnosis. Limitations include possible model hallucinations and the unclear composition of ChatGPT's training data set.
Journal Article
A Handbook on Multi-Attribute Decision-Making Methods
Clear and effective instruction on MADM methods for students, researchers, and practitioners.A Handbook on Multi-Attribute Decision-Making Methods describes multi-attribute decision-making (MADM) methods and provides step-by-step guidelines for applying them. The authors describe the most important MADM methods and provide an assessment of their performance in solving problems across disciplines. After offering an overview of decision-making and its fundamental concepts, this book covers 20 leading MADM methods and contains an appendix on weight assignment methods. Chapters are arranged with optimal learning in mind, so you can easily engage with the content found in each chapter. Dedicated readers may go through the entire book to gain a deep understanding of MADM methods and their theoretical foundation, and others may choose to review only specific chapters. Each standalone chapter contains a brief description of prerequisite materials, methods, and mathematical concepts needed to cover its content, so you will not face any difficulty understanding single chapters. Each chapter:Describes, step-by-step, a specific MADM method, or in some cases a family of methodsContains a thorough literature review for each MADM method, supported with numerous examples of the method's implementation in various fieldsProvides a detailed yet concise description of each method's theoretical foundationMaps each method's philosophical basis to its corresponding mathematical frameworkDemonstrates how to implement each MADM method to real-world problems in a variety of disciplinesIn MADM methods, stakeholders' objectives are expressible through a set of often conflicting criteria, making this family of decision-making approaches relevant to a wide range of situations. A Handbook on Multi-Attribute Decision-Making Methods compiles and explains the most important methodologies in a clear and systematic manner, perfect for students and professionals whose work involves operations research and decision making.
eBook