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

Morgan, Daniel J.; Pineles, Lisa; Owczarzak, Jill; Magder, Larry; Scherer, Laura D.; Brown, Jessica; Pfeiffer, Chris; Terndrup, Chris; Leykum, Luci K.; Feldstein, David; Foy, Andrew; Stevens, Deborah; Koch, C.; Masnick, Max; Weisenberg, Scott A.; Korenstein, Deborah

doi:10.1001/jamainternmed.2021.0269

Cited by 88 publications

(126 citation statements)

References 31 publications

(97 reference statements)

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“…The survey also included other aspects of risk perceptions, which have been presented elsewhere. 15 A draft survey was developed by primary investigators based in part on previous surveys of risk understanding. [16][17][18] This survey was reviewed by an expert panel of clinicians with different areas of expertise, practicing in community and academic settings.…”

Section: Surveymentioning

confidence: 99%

Clinician Conceptualization of the Benefits of Treatments for Individual Patients

et al. 2021

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IMPORTANCE Knowing the expected effect of treatment on an individual patient is essential for patient care. OBJECTIVE To explore clinicians' conceptualizations of the chance that treatments will decrease the risk of disease outcomes. DESIGN, SETTING, AND PARTICIPANTS This survey study of attending and resident physicians, nurse practitioners, and physician assistants was conducted in outpatient clinical settings in 8 US states from June 2018 to November 2019. The survey was an in-person, paper, 26-item survey in which clinicians were asked to estimate the probability of adverse disease outcomes and expected effects of therapies for diseases common in primary care. MAIN OUTCOMES AND MEASURES Estimated chance that treatments would benefit an individual patient. RESULTS Of 723 clinicians, 585 (81%) responded, and 542 completed all the questions necessary for analysis, with a median (interquartile range [IQR]) age of 32 (29-44) years, 287 (53%) women, and 294 (54%) White participants. Clinicians consistently overestimated the chance that treatmentswould benefit an individual patient. The median (IQR) estimated chance that warfarin would prevent a stroke in the next year was 50% (5%-80%) compared with scientific evidence, which indicates an absolute risk reduction (ARR) of 0.2% to 1.0% based on a relative risk reduction (RRR) of 39% to 50%. The median (IQR) estimated chance that antihypertensive therapy would prevent a cardiovascular event within 5 years was 30% (10%-70%) vs evidence of an ARR of 0% to 3% based on an RRR of 0% to 28%. The median (IQR) estimated chance that bisphosphonate therapy would prevent a hip fracture in the next 5 years was 40% (10%-60%) vs evidence of ARR of 0.1% to 0.4% based on an RRR of 20% to 40%. The median (IQR) estimated chance that moderate-intensity statin therapy would prevent a cardiovascular event in the next 5 years was 20% (IQR 5%-50%) vs evidence of an ARR of 0.3% to 2% based on an RRR of 19% to 33%. Estimates of the chance that a treatment would prevent an adverse outcome exceeded estimates of the absolute chance of that outcome for 60% to 70% of clinicians. Clinicians whose overestimations were greater were more likely to report using that treatment for patients in their practice (eg, use of warfarin: correlation coefficient, 0.46; 95% CI, 0.40-0.53; P < .001). CONCLUSIONS AND RELEVANCEIn this survey study, clinicians significantly overestimated the benefits of treatment to individual patients. Clinicians with greater overestimates were more likely to report using treatments in actual patients.

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Section: Surveymentioning

confidence: 99%

Clinician Conceptualization of the Benefits of Treatments for Individual Patients

et al. 2021

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“…(6) GPs should carefully gauge their index of concern and use this to inform decision making before and after receiving test results. (7) Not all 'normal' results are equal Age strongly affects the pre-test probability, and therefore also the post-test probability, of cancer. For example, the estimated risk of ovarian cancer in a 70-year-old woman with a CA125 of 34 U/ml (i.e.…”

Section: Pre and Post Test Probabilitymentioning

confidence: 99%

Interpreting negative test results when assessing cancer risk in general practice

2021

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“…What are the implications of these answers? Despite decades of articles and dozens of books on clinical problem solving claiming that rational diagnosis and therapeutics require formal probabilities of disease [14][15][16][37][38][39][40][41] , controversy about the role of probability in diagnosis is ongoing 6,10,11,[42][43][44][45][46][47][48] . This may stem from the fact that the formal systems proposed for probabilistic problem solving are too complicated and cumbersome for day-to-day use in the hustle and bustle of medical practice 15,26,40,41,49 .…”

Section: Incident Diseasementioning

confidence: 99%

“…Failure to make explicit probabilistic predictions hinders calibration by fostering the "I knew it all along" effect of hindsight bias [52][53][54][55] . Physicians, when they have been studied, have shown unimpressive proficiency at forecasting and calibration 7,10,53 . Whether explicit consideration of numerical disease incidences can improve diagnostic accuracy must await empirical research dedicated to that question.…”

Section: Incident Diseasementioning

confidence: 99%

“…This method, which is inconsistently used, is loosely probabilistic but can obscure large differences in probability between components of the differential; an ideal method would assign probabilities to each possibility that sum to 100% 4 . Practical difficulties limit the ability to accurately estimate the probabilities of components of the differential [5][6][7] , yet probabilistic reasoning is nonetheless an essential part of expert forecasting and diagnosis [8][9][10][11][12][13][14][15][16] .…”

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Common things are common, but what is common? A foundation for probabilistic diagnosis.

Aberegg

Callahan

2021

Preprint

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The well-known clinical axiom stating that “common things are common” attests to the pivotal role of probability in diagnosis. Despite the popularity of this and related axioms, there is no operationalized definition of a common disease, and no practicable way of incorporating actual disease frequencies into differential diagnosis. In this expository essay, we aim to reduce the ambiguity surrounding the definition of a common (or rare) disease and show that incidence – not prevalence – is the proper metric of disease frequency for diagnosis. We explore how a numerical estimates of disease frequencies based on incidence can be incorporated into differential diagnosis as well as the inherent limitations of this method. These concepts have important implications for diagnostic decision making and medical education, and hold promise as a method to improve diagnostic accuracy.

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

Cited by 88 publications

References 31 publications

Clinician Conceptualization of the Benefits of Treatments for Individual Patients

Clinician Conceptualization of the Benefits of Treatments for Individual Patients

Interpreting negative test results when assessing cancer risk in general practice

Common things are common, but what is common? A foundation for probabilistic diagnosis.

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