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      Accuracy of Practitioner Estimates of Probability of Diagnosis Before and After Testing

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          Key Points

          Question

          Do practitioners understand the probability of common clinical diagnoses?

          Findings

          In this survey study of 553 practitioners performing primary care, respondents overestimated the probability of diagnosis before and after testing. This posttest overestimation was associated with consistent overestimates of pretest probability and overestimates of disease after specific diagnostic test results.

          Meaning

          These findings suggest that many practitioners are unaccustomed to using probability in diagnosis and clinical practice. Widespread overestimates of the probability of disease likely contribute to overdiagnosis and overuse.

          Abstract

          Importance

          Accurate diagnosis is essential to proper patient care.

          Objective

          To explore practitioner understanding of diagnostic reasoning.

          Design, Setting, and Participants

          In this survey study, 723 practitioners at outpatient clinics in 8 US states were asked to estimate the probability of disease for 4 scenarios common in primary care (pneumonia, cardiac ischemia, breast cancer screening, and urinary tract infection) and the association of positive and negative test results with disease probability from June 1, 2018, to November 26, 2019. Of these practitioners, 585 responded to the survey, and 553 answered all of the questions. An expert panel developed the survey and determined correct responses based on literature review.

          Results

          A total of 553 (290 resident physicians, 202 attending physicians, and 61 nurse practitioners and physician assistants) of 723 practitioners (76.5%) fully completed the survey (median age, 32 years; interquartile range, 29-44 years; 293 female [53.0%]; 296 [53.5%] White). Pretest probability was overestimated in all scenarios. Probabilities of disease after positive results were overestimated as follows: pneumonia after positive radiology results, 95% (evidence range, 46%-65%; comparison P < .001); breast cancer after positive mammography results, 50% (evidence range, 3%-9%; P < .001); cardiac ischemia after positive stress test result, 70% (evidence range, 2%-11%; P < .001); and urinary tract infection after positive urine culture result, 80% (evidence range, 0%-8.3%; P < .001). Overestimates of probability of disease with negative results were also observed as follows: pneumonia after negative radiography results, 50% (evidence range, 10%-19%; P < .001); breast cancer after negative mammography results, 5% (evidence range, <0.05%; P < .001); cardiac ischemia after negative stress test result, 5% (evidence range, 0.43%-2.5%; P < .001); and urinary tract infection after negative urine culture result, 5% (evidence range, 0%-0.11%; P < .001). Probability adjustments in response to test results varied from accurate to overestimates of risk by type of test (imputed median positive and negative likelihood ratios [LRs] for practitioners for chest radiography for pneumonia: positive LR, 4.8; evidence, 2.6; negative LR, 0.3; evidence, 0.3; mammography for breast cancer: positive LR, 44.3; evidence range, 13.0-33.0; negative LR, 1.0; evidence range, 0.05-0.24; exercise stress test for cardiac ischemia: positive LR, 21.0; evidence range, 2.0-2.7; negative LR, 0.6; evidence range, 0.5-0.6; urine culture for urinary tract infection: positive LR, 9.0; evidence, 9.0; negative LR, 0.1; evidence, 0.1).

          Conclusions and Relevance

          This survey study suggests that for common diseases and tests, practitioners overestimate the probability of disease before and after testing. Pretest probability was overestimated in all scenarios, whereas adjustment in probability after a positive or negative result varied by test. Widespread overestimates of the probability of disease likely contribute to overdiagnosis and overuse.

          Abstract

          This survey study of physicians, nurse practitioners, and physician assistants explores practitioner understanding of diagnostic reasoning.

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          Most cited references31

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          Judgment under Uncertainty: Heuristics and Biases.

          This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.
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            2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines, and the American College of Physicians, American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons.

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              Cognitive biases associated with medical decisions: a systematic review

              Background Cognitive biases and personality traits (aversion to risk or ambiguity) may lead to diagnostic inaccuracies and medical errors resulting in mismanagement or inadequate utilization of resources. We conducted a systematic review with four objectives: 1) to identify the most common cognitive biases, 2) to evaluate the influence of cognitive biases on diagnostic accuracy or management errors, 3) to determine their impact on patient outcomes, and 4) to identify literature gaps. Methods We searched MEDLINE and the Cochrane Library databases for relevant articles on cognitive biases from 1980 to May 2015. We included studies conducted in physicians that evaluated at least one cognitive factor using case-vignettes or real scenarios and reported an associated outcome written in English. Data quality was assessed by the Newcastle-Ottawa scale. Among 114 publications, 20 studies comprising 6810 physicians met the inclusion criteria. Nineteen cognitive biases were identified. Results All studies found at least one cognitive bias or personality trait to affect physicians. Overconfidence, lower tolerance to risk, the anchoring effect, and information and availability biases were associated with diagnostic inaccuracies in 36.5 to 77 % of case-scenarios. Five out of seven (71.4 %) studies showed an association between cognitive biases and therapeutic or management errors. Of two (10 %) studies evaluating the impact of cognitive biases or personality traits on patient outcomes, only one showed that higher tolerance to ambiguity was associated with increased medical complications (9.7 % vs 6.5 %; p = .004). Most studies (60 %) targeted cognitive biases in diagnostic tasks, fewer focused on treatment or management (35 %) and on prognosis (10 %). Literature gaps include potentially relevant biases (e.g. aggregate bias, feedback sanction, hindsight bias) not investigated in the included studies. Moreover, only five (25 %) studies used clinical guidelines as the framework to determine diagnostic or treatment errors. Most studies (n = 12, 60 %) were classified as low quality. Conclusions Overconfidence, the anchoring effect, information and availability bias, and tolerance to risk may be associated with diagnostic inaccuracies or suboptimal management. More comprehensive studies are needed to determine the prevalence of cognitive biases and personality traits and their potential impact on physicians’ decisions, medical errors, and patient outcomes. Electronic supplementary material The online version of this article (doi:10.1186/s12911-016-0377-1) contains supplementary material, which is available to authorized users.
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                Author and article information

                Journal
                JAMA Intern Med
                JAMA Intern Med
                JAMA Intern Med
                JAMA Internal Medicine
                American Medical Association
                2168-6106
                2168-6114
                5 April 2021
                June 2021
                5 April 2021
                : 181
                : 6
                : 747-755
                Affiliations
                [1 ]Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore
                [2 ]Veterans Affairs (VA) Maryland Healthcare System, Baltimore
                [3 ]Department of Health, Behavior, and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
                [4 ]Adult and Child Consortium of Health Outcomes Research and Delivery Science, University of Colorado School of Medicine, Aurora
                [5 ]Division of Cardiology, University of Colorado School of Medicine, Aurora
                [6 ]Center of Innovation for Veteran-Centered and Value-Driven Care, VA Denver, Denver, Colorado
                [7 ]Division of General Internal Medicine & Geriatrics, Department of Medicine, Oregon Health & Science University, Portland
                [8 ]Department of Medicine, Dell Medical School, the University of Texas at Austin, Austin
                [9 ]Department of Medicine, South Texas Veterans Health Care System, San Antonio
                [10 ]Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison
                [11 ]Department of Medicine, Penn State College of Medicine, Hershey, Pennsylvania
                [12 ]Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania
                [13 ]Department of Medicine, University of Maryland School of Medicine, Baltimore
                [14 ]Department of Informatics, Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
                [15 ]Division of Infectious Diseases, New York University Grossman School of Medicine, New York
                [16 ]Division of General Internal Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
                Author notes
                Article Information
                Accepted for Publication: November 21, 2020.
                Published Online: April 5, 2021. doi:10.1001/jamainternmed.2021.0269
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Morgan DJ et al. JAMA Internal Medicine.
                Corresponding Author: Daniel J. Morgan, MD, MS, Department of Epidemiology and Public Health, University of Maryland School of Medicine, 10 S Pine St, Medical Student Teaching Facility Room 334, Baltimore, MD 21201 ( dmorgan@ 123456som.umaryland.edu ).
                Author Contributions: Dr Morgan had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Morgan, Pineles, Owczarzak, Magder, Scherer, Brown, Terndrup, Feldstein, Foy, Stevens, Koch, Weisenberg, Korenstein.
                Acquisition, analysis, or interpretation of data: Morgan, Pineles, Magder, Scherer, Pfeiffer, Leykum, Stevens, Koch, Masnick.
                Drafting of the manuscript: Morgan, Pineles, Magder, Stevens.
                Critical revision of the manuscript for important intellectual content: Morgan, Pineles, Owczarzak, Magder, Scherer, Brown, Pfeiffer, Terndrup, Leykum, Feldstein, Foy, Koch, Masnick, Weisenberg, Korenstein.
                Statistical analysis: Morgan, Magder.
                Obtained funding: Morgan, Pineles.
                Administrative, technical, or material support: Morgan, Pineles, Owczarzak, Scherer, Brown, Pfeiffer, Terndrup, Leykum, Stevens.
                Supervision: Morgan, Terndrup.
                Conflict of Interest Disclosures: Dr Morgan reported receiving grants from the National Institutes of Health (NIH) during the conduct of the study and grants from the US Department of Veterans Affairs, the Agency for Healthcare Research and Quality, and the Centers for Disease Control and Prevention outside the submitted work. Ms Pineles reported receiving grants from the NIH to the University of Maryland School of Medicine during the conduct of the study. Dr Scherer reported receiving grants from the NIH during the conduct of the study. Dr Brown reported receiving grants from the NIH during the conduct of the study. Dr Pfeiffer reported receiving grants from Pfizer to serve as site investigator for a Clostridium difficile vaccine trial (protocol B5091007) since July 2020 under a Cooperative Research and Development Agreement with VA Portland outside the submitted work. Dr Korenstein reported receiving grants from the NIH and grants from the National Cancer Institute to Memorial Sloan Kettering Cancer Center during the conduct of the study and that her spouse serves on the scientific advisory board and as a consultant for Vedanta Biosciences, serves as a consultant for Takeda, serves on the scientific advisory board and as a consultant for Opentrons. No other disclosures were reported.
                Funding/Support: This project was funded by grant NLM DP2LM012890 (New Innovator Award) from the NIH (Dr Morgan, principal investigator). Dr Korenstein’s work on this project was supported in part by Cancer Center Support Grant P30 CA008748 from the National Cancer Institute to Memorial Sloan Kettering Cancer Center.
                Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
                Article
                ioi210005
                10.1001/jamainternmed.2021.0269
                8022260
                33818595
                c3467c46-52ad-42da-a24b-58248e85386f
                Copyright 2021 Morgan DJ et al. JAMA Internal Medicine.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 1 September 2020
                : 21 November 2020
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