Diagnostic Reasoning by Master Clinicians: What Distinguishes Them From Their Peers?

Abstract Introduction: Master clinicians are a group of physicians recognized in large part for their superior diagnostic reasoning abilities. However, their reasoning skills have not been rigorously and quantitatively compared to other clinicians using a validated instrument.Methods: We surveyed Internal Medicine physicians at the University of Iowa to identify the master clinicians. These master clinicians were administered the Diagnostic Thinking Inventory, along with an equivalent number of their peers in the general population of internists. Scores were tabulated for structure and thinking, as well as four previously identified elements of diagnostic reasoning (data acquisition, problem representation, hypothesis generation, and illness script search and selection). The 2-sample t-test was used to compare scores between the two groups.Results: 17 master clinicians were identified, of whom 17 (100%) completed the inventory. 19 out of 25 randomly-selected internists also completed the inventory (76%). Mean total scores were 187.2 and 175.8 for the Master Clinician (MC) and the Internist (IM) groups respectively. Thinking and structure subscores were 91.5 and 95.71 for MCs, compared to 85.5 and 90.3 for IMs (p-values: 0.0783 and 0.1199, respectively). The mean data acquisition, problem representation, hypothesis generation, and illness script selection subscores for MCs were 4.46, 4.57, 4.71, and 4.46, compared to 4.13, 4.38, 4.45, and 4.13 in the IM group (p-values: 0.2077, 0.4528, 0.095, and 0.029, respectively). Conclusions: Master Clinicians have greater proficiency in searching for and selecting illness scripts compared to their peers. There were no statistically significant differences between the other scores and subscores. These results will help to inform continuing medical education efforts to improve diagnostic reasoning.

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Embedding a longitudinal diagnostic reasoning curriculum in a residency program using a bolus/booster approach

Diagnosis ◽

10.1515/dx-2019-0023 ◽

2020 ◽

Vol 7 (1) ◽

pp. 21-25

Author(s):

Katherine I. Harris ◽

Jane S. Rowat ◽

Manish Suneja

Keyword(s):

Residency Program ◽

Statistical Significance ◽

Diagnostic Reasoning ◽

Problem Representation ◽

University Of Iowa ◽

Self Evaluation ◽

Practice Of Medicine ◽

Reasoning Skills ◽

The University ◽

Diagnostic Thinking

AbstractBackgroundDiagnostic reasoning skills are essential to the practice of medicine, yet longitudinal curricula to teach residents and evaluate performance in this area is lacking. We describe a longitudinal diagnostic reasoning curriculum implemented in a university-based internal medicine residency program and self-evaluation assessment of the curriculum’s effectiveness.MethodsA longitudinal diagnostic reasoning curriculum (bolus/booster) was developed and implemented in the fall of 2015 at the University of Iowa. R1, R2, and R3 cohorts were taught the “bolus” curriculum at the beginning of each academic year followed by a “booster” component to maintain and build upon diagnostic reasoning skills taught during the “bolus” phase. Self-administered diagnostic thinking inventory (DTI) scores were collected in the spring of pre-curriculum (baseline, 2014–2015) and post-curriculum (2016–2017).ResultsThe overall DTI scores improved in the R1 cohort, although statistically significant differences were not seen with R2s and R3s. In the original DTI categories, R1s improved in both flexibility of thinking and structure of thinking, the R2s improved in structure of thinking and the R3s did not improve in either category. R1s showed improvement in three of the four subcategories – data acquisition, problem representation, and hypothesis generation. The R2s improved in the subcategory of problem representation. R3s showed no improvement in any of the subcategories. The R3 cohort had higher mean scores in all categories but this did not reach statistical significance.ConclusionsOur program created and successfully implemented a longitudinal diagnostic reasoning curriculum. DTI scores improved after implementation of a new diagnostic reasoning curriculum, particularly in R1 cohort.

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Overview of diagnostic reasoning: Hypothetical-deductive strategy, problem representation, semantic qualifiers, illness scripts, pattern recognition and prototypes

Medical Journal of Zambia ◽

10.4314/mjz.v36i3.56074 ◽

2010 ◽

Vol 36 (3) ◽

Author(s):

SB Banda

Keyword(s):

Pattern Recognition ◽

Diagnostic Reasoning ◽

Problem Representation ◽

Illness Scripts

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Problem Representation, Background Evidence, Analysis, Recommendation: An Oral Case Presentation Tool to Promote Diagnostic Reasoning

Academic Pediatrics ◽

10.1016/j.acap.2017.08.002 ◽

2018 ◽

Vol 18 (2) ◽

pp. 228-230 ◽

Cited By ~ 3

Author(s):

Cristina Carter ◽

Nicole Akar-Ghibril ◽

Jeff Sestokas ◽

Gabrina Dixon ◽

Wilhelmina Bradford ◽

...

Keyword(s):

Diagnostic Reasoning ◽

Problem Representation ◽

Case Presentation ◽

Evidence Analysis ◽

Presentation Tool

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Diagnostic reasoning in internal medicine: a practical reappraisal

Internal and Emergency Medicine ◽

10.1007/s11739-020-02580-0 ◽

2020 ◽

Author(s):

Gino Roberto Corazza ◽

Marco Vincenzo Lenti ◽

Peter David Howdle

Keyword(s):

Internal Medicine ◽

Clinical Reasoning ◽

Clinical Medicine ◽

Current Knowledge ◽

Diagnostic Reasoning ◽

Intuitive Thinking ◽

Illness Script ◽

Multi Stage ◽

Diagnostic Hypothesis ◽

Stage Process

AbstractThe practice of clinical medicine needs to be a very flexible discipline which can adapt promptly to continuously changing surrounding events. Despite the huge advances and progress made in recent decades, clinical reasoning to achieve an accurate diagnosis still seems to be the most appropriate and distinctive feature of clinical medicine. This is particularly evident in internal medicine where diagnostic boundaries are often blurred. Making a diagnosis is a multi-stage process which requires proper data collection, the formulation of an illness script and testing of the diagnostic hypothesis. To make sense of a number of variables, physicians may follow an analytical or an intuitive approach to clinical reasoning, depending on their personal experience and level of professionalism. Intuitive thinking is more typical of experienced physicians, but is not devoid of shortcomings. Particularly, the high risk of biases must be counteracted by de-biasing techniques, which require constant critical thinking. In this review, we discuss critically the current knowledge regarding diagnostic reasoning from an internal medicine perspective.

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Data acquisition dynamics and hypothesis generation

Cognitive Systems Research ◽

10.1016/j.cogsys.2012.12.006 ◽

2013 ◽

Vol 24 ◽

pp. 9-17 ◽

Cited By ~ 4

Author(s):

Nicholas D. Lange ◽

Eddy J. Davelaar ◽

Rick P. Thomas

Keyword(s):

Data Acquisition ◽

Hypothesis Generation

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Probability in Diagnostic Thinking

Risk and Reason in Clinical Diagnosis ◽

10.1093/med/9780190944001.003.0005 ◽

2019 ◽

pp. 83-100

Author(s):

Cym Anthony Ryle

Keyword(s):

Risk Factors ◽

Population Based ◽

Diagnostic Reasoning ◽

Individual Risk ◽

Diagnostic Process ◽

Test Results ◽

Medically Unexplained ◽

Individual Risk Factors ◽

Diagnostic Thinking ◽

Early Phases

This chapter observes that diagnostic reasoning involves both informal and mathematical estimates of probability. It argues that intuitive estimates of the likelihood of disease are necessary in the early phases of the diagnostic process but notoriously inaccurate. It notes that formal calculations are not possible when the question is, What might be wrong with this person? but are much more accurate than intuition in estimating the probability that a specific disease is present. The chapter suggests that population-based calculations of the likelihood of disease may lead clinicians to play Russian roulette by proxy because individual variation and individual risk factors may alter that risk in a given patient. It refers to evidence that many clinicians are inexpert in statistical methods. The chapter describes some basic statistical processes and their place in the clinical application of test results. It discusses the necessity and challenges of managing patients whose symptoms are medically unexplained.

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A Comment on “Beyond P-value: the Rigor and Power of Study"

Global Clinical and Translational Research ◽

10.36316/gcatr.02.0022 ◽

2019 ◽

pp. 7-9 ◽

Cited By ~ 1

Author(s):

Helena Kraemer

Keyword(s):

Hypothesis Testing ◽

Present Situation ◽

Hypothesis Generation ◽

Statistical Hypothesis ◽

P Value ◽

Exploratory Research ◽

Statistical Hypothesis Testing ◽

P Values

“As ye sow. So shall ye reap”: For almost 100 years, researchers have been taught that the be-all and end-all in data-based research is the p-value. The resulting problems have now generated concern, often from us who have long so taught researchers. We must bear a major responsibility for the present situation and must alter our teachings. Despite the fact that the Zhang and Hughes paper is titled “Beyond p-value”, the total focus remains on statistical hypothesis testing studies (HTS) and p-values(1). Instead, I would propose that there are three distinct, necessary, and important phases of research: 1) Hypothesis Generation Studies (HGS) or Exploratory Research (2-4); 2) Hypothesis Testing Studies (HTS); 3) Replication and Application of Results. Of these, HTS is undoubtedly the most important, but without HGS, HTS is often weak and wasteful, and without Replication and Application, the results of HTS are often misleading.

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