Biasing Influence of ‘Mental Shortcuts’ on Diagnostic Decision-Making: Radiologists Can Overlook Breast Cancer in Mammograms When Prior Diagnostic Information Is Available

When making decisions under uncertainty, people in all walks of life, including highly trained medical professionals, tend to resort to using ‘mental shortcuts’, or heuristics. Anchoring-and-adjustment (AAA) is a well-known heuristic in which subjects reach a judgment by starting from an initial internal judgment (‘anchored position’) based on available external information (‘anchoring information’) and adjusting it until they are satisfied. We studied the effects of the AAA heuristic during diagnostic decision-making in mammography. We provided practicing radiologists (N = 27 across two studies) a random number that we told them was the estimate of a previous radiologist of the probability that a mammogram they were about to see was positive for breast cancer. We then showed them the actual mammogram. We found that the radiologists’ own estimates of cancer in the mammogram reflected the random information they were provided and ignored the actual evidence in the mammogram. However, when the heuristic information was not provided, the same radiologists detected breast cancer in the same set of mammograms highly accurately, indicating that the effect was solely attributable to the availability of heuristic information. Thus, the effects of the AAA heuristic can sometimes be so strong as to override the actual clinical evidence in diagnostic tasks.

Evolution of Hemodynamic Parameters Simulated by Means of Diffusion Models

Diffusion is a well-known physical phenomenon governing such processes as movement of particles or transportation of heat. In this paper, we prove that a close analogy to those processes exists in medical data behavior, and that changes in the values of medical parameters measured while treating patients may be described using diffusion models as well. The medical condition of a patient is usually described by a set of discrete values. The evolution of that condition and, consequently, of the disease has the form of a transition of that set of discrete values, which correspond to specific parameters. This is a typical medical diagnosis scheme. However, disease evolution is a phenomenon that is characterized by continuously varying, temporal characteristics. A mathematical disease evolution model is, in fact, a continuous diffusion process from one discrete slot of the diagnosed parameter value to another inside the mentioned set. The ability to predict such diffusion-related properties offer precious support in diagnostic decision-making. We have examined several hundred patients while conducting a medical research project. All patients were under treatment to stabilize their hemodynamic parameters. A diffusion model relied upon simulating the results of treatment is proposed here. Time evolution of thoraric fluid content (TFC) has been used as the illustrative example. The objective is to prove that diffusion models are a proper and convenient solution for predicting disease evolution processes. We applied the Fokker-Planck equation (FPE), considering it to be most adequate for examining the treatment results by means of diffusion. We confirmed that the phenomenon of diffusion explains the evolution of the heart disease parameters observed. The evolution of TFC has been chosen as an example of a hemodynamic parameter.

Emergency medicine physicians’ perspectives on diagnostic accuracy in neurology: a qualitative study

Objectives We sought to understand the knowledge, attitudes, and beliefs of emergency medicine (EM) physicians towards non-specific neurological conditions and the use of clinical decision support (CDS) to improve diagnostic accuracy. Methods We conducted semi-structured interviews of EM physicians at four emergency departments (EDs) affiliated with a single US healthcare system. Interviews were conducted until thematic saturation was achieved. Conventional content analysis was used to identify themes related to EM physicians’ perspectives on acute diagnostic neurology; directed content analysis was used to explore views regarding CDS. Each interview transcript was independently coded by two researchers using an iteratively refined codebook with consensus-based resolution of coding differences. Results We identified two domains regarding diagnostic safety: (1) challenges unique to neurological complaints and (2) challenges in EM more broadly. Themes relevant to neurology included: (1) knowledge gaps and uncertainty, (2) skepticism about neurology, (3) comfort with basic as opposed to detailed neurological examination, and (4) comfort with non-neurological diseases. Themes relevant to diagnostic decision making in the ED included: (1) cognitive biases, (2) ED system/environmental issues, (3) patient barriers, (4) comfort with diagnostic uncertainty, and (5) concerns regarding diagnostic error identification and measurement. Most participating EM physicians were enthusiastic about the potential for well-designed CDS to improve diagnostic accuracy for non-specific neurological complaints. Conclusions Physicians identified diagnostic challenges unique to neurological diseases as well as issues related more generally to diagnostic accuracy in EM. These physician-reported issues should be accounted for when designing interventions to improve ED diagnostic accuracy.

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

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.