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The purpose of this paper is to examine critically the accuracy of expert judgment, drawing on empirical evidence and theory from multiple disciplines. It suggests that counsel offered with confidence by experts might, under certain circumstances, be without merit, and presents approaches to assessing the accuracy of such counsel.

The paper synthesizes research findings on expert judgment drawn from multiple fields, including psychology, criminal justice, political science, and decision analysis. It examines internal and external factors affecting the veracity of what experts may judge to be matters of common sense, using a semiotic structure.

In multiple domains, including management, expert accuracy is, in general, no better than chance. Increased experience, however, is often accompanied by an unjustified increase in self‐confidence.

While the dynamic nature of decision making in organizations renders the development of a codified, reliable knowledge base potentially unachievable, there is value in recognizing these limitations, and employing tactics to explore more thoroughly both problem and solutions spaces

The paper's originality lies in its integration of recent, multiple‐disciplinary research as a basis for persuading decision makers of the perils of accepting expert advice without skepticism.

Although it is widely acknowledged that health care delivery systems are complex adaptive systems, there are gaps in understanding the application of systems engineering approaches to systems analysis and redesign in the health care domain. Commonly employed methods, such as statistical analysis of risk factors and outcomes, are simply not adequate to robustly characterize all system requirements and facilitate reliable design of complex care delivery systems. This is especially apparent in institutional-level systems, such as patient safety programs that must mitigate the risk of infections and other complications that can occur in virtually any setting providing direct and indirect patient care. The case example presented here illustrates the application of various system engineering methods to identify requirements and intervention candidates for a critical patient safety problem known as failure to rescue. Detailed descriptions of the analysis methods and their application are presented along with specific analysis artifacts related to the failure to rescue case study. Given the prevalence of complex systems in health care, this practical and effective approach provides an important example of how systems engineering methods can effectively address the shortcomings in current health care analysis and design, where complex systems are increasingly prevalent.

Failure to rescue events, or events involving preventable deaths from complications, are a significant contributor to inpatient mortality. While many interventions have been designed and implemented over several decades, this patient safety issue remains at the forefront of concern for most hospitals. In the first part of this study, the development and implementation of one type of highly studied and widely adopted rescue intervention, algorithm-based patient assessment tools, is examined. The analysis summarizes how a lack of systems-oriented approaches in the design and implementation of these tools has resulted in suboptimal understanding of patient risk of mortality and complications and the early recognition of patient deterioration. The gaps identified impact several critical aspects of excellent patient care, including information-sharing across care settings, support for the development of shared mental models within care teams, and access to timely and accurate patient information.

This chapter describes the use of several system-oriented design and implementation activities to establish design objectives, model clinical processes and workflows, and create an extensible information system model to maximize the benefits of patient state and risk assessment tools in the inpatient setting. A prototype based on the product of the design activities is discussed along with system-level considerations for implementation. This study also demonstrates the effectiveness and impact of applying systems design principles and practices to real-world clinical applications.

This study aims to explore the objectivity in third-party ratings. Third-party ratings are often based on some form of aggregation of various experts' opinions with the assumption that the potential judgment biases of the experts cancel each other out. While psychology research has suggested that experts can be unintentionally biased, management literature has not considered the effect of expert bias on the objectivity of third-party ratings. Thus, this study seeks to address this issue.

Ranking data from the US News and World Report between 1993 and 2008, institution-related variables and, to represent sports prominence, NCAA football and basketball performance variables are leveraged in testing our hypotheses. A mediating-model is tested using regression with panel-corrected standard errors.

This study finds that the judgments of academicians and recruiters, concerning the quality of universities, have been biased by the prominence of a university's sports teams and that the bias introduced to these experts mediates the aggregated bias in the resultant rankings of MBA programs. Moreover, it finds that experts may inflate rankings by up to two positions.

This study is particularly relevant for university officials as it uncovers how universities can tangibly manipulate the relative perception of quality through sports team prominence. For third-party rating systems, the reliability of ratings based on aggregated expert judgments is called into question.

This study addresses a significant gap in the literature by examining how a rating system may be unintentionally biased through the aggregation of experts' judgments. Given the heavy reliance on third-party rating systems by both academics and the general population, addressing the objectivity of such ratings is crucial.