Physician judgment in clinical settings: methodological influences and cognitive performance

1993 ◽  
Vol 39 (7) ◽  
pp. 1468-1480 ◽  
Author(s):  
N V Dawson

Abstract Understanding the quality of physicians' intuitive judgments is essential in determining the appropriate use of their judgments in medical decision-making (vis-a-vis analytical or actuarial approaches). As part of this process, the quality of physicians' predictions must be assessed because prediction is fundamental to common clinical tasks: determining diagnosis, prognosis, and therapy; establishing monitoring intervals; performing screening and preventive maneuvers. Critical evaluation of predictive capabilities requires an assessment of the components of the prediction process: the data available for prediction, the method used for prediction, and the accuracy of prediction. Although variation in and uncertainty about the underlying data elements are often acknowledged as a source of inaccurate predictions, prediction also can be confounded by both methodological and cognitive limitations. During the past two decades, numerous factors have been recognized that may bias test characteristics (sensitivity and specificity). These same factors may also produce bias in intuitive judgments. The use of cognitive processes to simplify judgment tasks (e.g., the availability and representativeness heuristics) and the presence of certain biases in the judgment process (e.g., ego, regret) may present obstacles to accurate estimation of probabilities by physicians. Limitations on the intuitive use of information (cognitive biases) have been demonstrated in both medical and nonmedical decision-making settings. Recent studies have led to a deepening understanding of the advantages and disadvantages of intuitive and analytical approaches to decision making. Here, many aspects of the basis for this understanding are reviewed.

2006 ◽  
Vol 130 (5) ◽  
pp. 613-616 ◽  
Author(s):  
Roger E. McLendon

Abstract Context.—A significant difficulty that pathologists encounter in arriving at a correct diagnosis is related to the way information from various sources is processed and assimilated in context. Objective.—These issues are addressed by the science of cognitive psychology. Although cognitive biases are the focus of a number of studies on medical decision making, few if any focus on the visual sciences. Data Sources.—A recent publication authored by Richards Heuer, Jr, The Psychology of Intelligence Analysis, directly addresses many of the cognitive biases faced by neuropathologists and anatomic pathologists in general. These biases include visual anticipation, first impression, and established mindsets and subconsciously influence our critical decision-making processes. Conclusions.—The book points out that while biases are an inherent property of cognition, the influence of such biases can be recognized and the effects blunted.


2022 ◽  
pp. 338-349
Author(s):  
Theodoros Galanis ◽  
Ploutarhos Kerpelis

Humans in addition with other factors have increased the environmental pollution of the planet. Many highly populated cities like Athens have problems with air quality due to the poor quality of construction, high temperatures in summer, noise, no existence of city plans, etc. The scope of this study is the investigation of urban towns' benefits using planted roofs. All types of planted roofs have many environmental, constructional, social, and financial benefits. The research suggests a method from the design, the study until the construction, using decision making, informing the citizens, and taking into account their opinion. The original design of buildings must have adopted an integrated energy strategy such as the solution of planted roofs so as to maximize the benefits to the environment and human beings. The research is specialized using a case study about a planted roof of an existing school building in Athens. The advantages and disadvantages of the usage are shown focusing on environmental, social, and construction aspects.


2019 ◽  
pp. bmjebm-2019-111247
Author(s):  
David Slawson ◽  
Allen F Shaughnessy

Overdiagnosis and overtreatment—overuse—is gaining wide acceptance as a leading nosocomial intervention in medicine. Not only does overuse create anxiety and diminish patients’ quality of life, in some cases it causes harm to both patients and others not directly involved in clinical care. Reducing overuse begins with the recognition and acceptance of the potential for unintended harm of our best intentions. In this paper, we introduce five cases to illustrate where harm can occur as the result of well-intended healthcare interventions. With this insight, clinicians can learn to appreciate the critical role of probability-based, evidence-informed decision-making in medicine and the need to consider the outcomes for all who may be affected by their actions. Likewise, educators need to evolve medical education and medical decision-making so that it focuses on the hierarchy of evidence and that what ‘ought to work’, based on traditional pathophysiological, disease-focused reasoning, should be subordinate to what ‘does work’.


Safety ◽  
2019 ◽  
Vol 5 (4) ◽  
pp. 69
Author(s):  
Burggraaf ◽  
Groeneweg ◽  
Sillem ◽  
van Gelder

The field of safety and incident prevention is becoming more and more data based. Data can help support decision making for a more productive and safer work environment, but only if the data can be, is and should be trusted. Especially with the advance of more data collection of varying quality, checking and judging the data is an increasingly complex task. Within such tasks, cognitive biases are likely to occur, causing analysists to overestimate the quality of the data and safety experts to base their decisions on data of insufficient quality. Cognitive biases describe generic error tendencies of persons, that arise because people tend to automatically rely on their fast information processing and decision making, rather than their slow, more effortful system. This article describes five biases that were identified in the verification of a safety indicator related to train driving. Suggestions are also given on how to formalize the verification process. If decision makers want correct conclusions, safety experts need good quality data. To make sure insufficient quality data is not used for decision making, a solid verification process needs to be put in place that matches the strengths and limits of human cognition.


2013 ◽  
Vol 3 (2) ◽  
pp. 10
Author(s):  
Ofir Ben Assuli

Background: Modeling medical decision-making has attracted considerable attention over the years, and has become the topic of many investigations. Researchers have attempted to model this critical and extremely complex process from several different angles to enable hospital clinicians to engage in decision-making using empirical tools. Purpose: This paper takes a famous managerial model of decision-making in a non-medical setting and integrates it with a well- known model of medical decision-making to generate a unified illustration of the process. Both models deal with decision-making. However, Simon’s model is less easily applied to the unique process of medical decision making. The proposed integration may help bridge the gap between the models and approaches by creating a unified framework to deal with the challenge of medical decision making in hospital environments through empirical methods. Approach: Simon’s model of automation provides the general structure of the decision-making process by dividing it into three stages: Intelligence, Design and Choice. The Pauker & Kassirer model deals with probabilistic and statistical applications of clinical processes, and introduces a threshold approach and decision trees as the main decision tools. The discussion explores the advantages and disadvantages of each model and what can be gained by combining them. Research limitations: Although these models were used to form an integrated framework, they were developed almost three decades apart. Therefore, caution is of the essence when applying them to real-life circumstances, and further research is needed to validate this integration. 


2015 ◽  
Vol 33 (29_suppl) ◽  
pp. 41-41
Author(s):  
Eric Rackow ◽  
Afua Ofori ◽  
Wendy Rodkey ◽  
Roy A. Beveridge

41 Background: Patients with advanced illness often face painful conversations and difficult decisions. A program was deployed to help patients identify, communicate, and incorporate their personal preferences and priorities into decisions about their care. The program was assessed by measuring movement along the readiness for change continuum. Methods: Patients residing in the home and participating in a chronic care program were referred by their case managers based on clinical conditions and whether the patient appeared to be in their last 12 months of life. Counseling sessions with patients or family caregiver/s were designed to move participants toward the following actions: be fully informed about their medical situation, describe their detailed quality of life priorities, articulate a self-defined medical decision making process, effectively communicate to their family and physicians, and implement and repeat the aforementioned steps. After 5 months (Sept-2014 to Feb-2015), movement along the readiness for change continuum (pre-contemplation, contemplation, preparation, action, maintenance, and advocacy) was reported. Results: Of the 427 patients referred, 33 could not be reached, 116 were ineligible, 50 declined or did not engage. Of the 228 participants, 191 (84%) moved at least one step in readiness for change continuum over the 5-month period. In Nov-2014, 13% of participants were in action, maintenance, or advocacy, which increased to 19% by Feb-2015. The largest observed movement to action, maintenance, or advocacy was in defining quality of life priorities: 2% Nov-2014 to 21% Feb-2015. The least movement to action, maintenance, or advocacy was observed in articulating a self-defined medical decision making process: 3% Nov-2014 to 16% Feb-2015. Case managers reported discomfort in referring members based on their assessment of length of life. Early surveys show high levels of satisfaction. Conclusions: A very high percentage of patients progressed in incorporating their preferences and priorities into end of life care as measured by the readiness to change continuum. This program is currently expanding and the referral process is changing from case manager to algorithm based identification referrals.


2020 ◽  
Vol 189 (4) ◽  
pp. 1477-1484 ◽  
Author(s):  
Dale F. Whelehan ◽  
Kevin C. Conlon ◽  
Paul F. Ridgway

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e18286-e18286
Author(s):  
Benjamin Garmezy ◽  
Francis P. Worden ◽  
Jennifer Blumenthal-Barby

e18286 Background: Receiving a malignant diagnosis is challenging and patients must be properly informed of their prognosis to make the best decisions for themselves and their families. The general population has a low degree of statistical literacy and therefore approximations should be considered favorable. However, given that half of patients desire quantitative data, physicians must provide this information within a context that is relevant and comprehensible. Single-point median survival data is less meaningful for patient decision-making. What is preferable is that physicians give experience-based estimations on how a patient might compare to the ‘average’. Including this information leads to more informed consent and best follows the ethical principle of autonomy. Methods: This is a novel framework for providing a prognosis that builds upon the multiples-of-the-mean model established in 2010 by Kiely, Tattersall, and Stockler. This framework was derived from original ethics research involving patient preferences, statistical comprehension, healthcare communication, and medical decision-making. Results: Graphical representation of Kiely’s model provides an intelligible view of quantitative data and allows for the physician to include an experience-based estimation. Steps: (1) Draw a Horizontal line, (2) Calculate from median survival data by using five multiples: 0.25 (Worst-Case, 10% of patients), 0.5 (Lower-Typical, 25%), 1 (Median, 50%), 2 (Upper-Typical, 75%), and 3 (Best-Case, 90%), (3) Label the chart using simple time points in months or years (4) Draw a circle over the region that best represents the patient’s expectation, (5) Provide the diagram alongside easy-to-understand language that clarifies the meaning of the numerical data with written likelihood of the occurrence, such as “almost no chance”, “not likely”, “even chances”, “likely”, and “fairly confident”. Conclusions: Kiely’s model has proven an accurate estimation in colorectal, castration-resistant prostate, non-small-cell lung, and metastatic breast cancer. A graphical representation should provide physicians an easy tool to strengthen informed consent and better aid their patients in decision-making.


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