How Expert Clinicians Intuitively Recognize a Medical Diagnosis

A Critical Examination of the Clinical Diagnosis of Functional Tic-like Behaviors

BACKGROUND

Since the COVID-19 pandemic, movement disorder clinics have seen an increase in patients with an unusual type of tic-like symptoms: young adults with abrupt onset complex behaviors. It was quickly suspected that these patients suffered from functional neurological symptoms, later named Functional Tic-Like Behaviors (FTLB). Subsequent research on the differential diagnosis between FTLB and tics has been substantial and led to the development of diagnostic checklists.

OBJECTIVES

We conducted a theoretical reappraisal of the FTLB literature to clarify the validity of the concept and its diagnostic implications.

METHODS

This paper addresses several key aspects of the current FTLB literature: circular reasoning, the complications of the FTLB phenomenology and demographics, the impact of FTLB on tic literature at large, and issues with alignment of the FTLB concept with the diagnostic criteria for functional disorders.

RESULTS

The clinical approach to FTLB might involve circular reasoning due to a lack of clinical benchmarks. The FTLB phenomenology and demographics may need more work to ensure a lack of bias and a proper description of this patient group including a clear distinction from tics. The impact of the FTLB discussion on the wider literature needs consideration. The validation of positive signs may help with both these endeavors and pave way to the inclusion of FTLB within psychiatric classification systems. Furthermore, the coexistence of FTLB and tics within the same patient needs to be addressed.

CONCLUSION

More research may be needed to fully establish the diagnosis of FTLB and differentiate it from tics.

Steps to improve the teaching of clinical reasoning in dermatology: A scoping review and proposal

Clinical reasoning (CR) is an area of active interest since faults in the diagnostic process can result in errors and possibly delays in care or even patient harm. The purpose of this scoping review was to collect information from the medical literature on approaches utilized to teach and assess CR in dermatology, identify gaps, and prepare a proposal on how to enhance the speciality's ability to develop trainee CR skills. We conducted a review of the published literature (1990-2020) from four databases. The initial search yielded 780 papers, and 42 relevant CR publications met inclusion criteria. Demographic, thematic content, theoretical frameworks, continuum of authenticity, competency/milestone, and assessment/educational intervention data were recorded by two screeners. Trainees at different educational levels from 17 different countries have been assessed in the dermatology literature. Most publications were of a single intervention, appeared underpowered, and had small sample sizes. Only two publications examined work-based assessments (use real patients/stimuli). Knowledge-based studies were the dominant theoretical framework with no studies exclusively focused on process-based CR interventions. Simulation was well represented with 23 (55%) investigations. Rigorous studies that examine CR teaching and assessment in dermatology are lacking. Evidence-based best practices for use of work-based assessments, especially direct observation, need to be developed/adapted for dermatology and validated. Dermatology training programs would benefit from longitudinal data on trainee CR development, process-based CR educational programs, metacognition CR exploration specific to skin disease diagnosis, and studies that yield practical recommendations on how to structure multi-faceted assessments that assess CR.

The biomedical knowledge graph of symptom phenotype in coronary artery plaque: machine learning-based analysis of real-world clinical data

A knowledge graph can effectively showcase the essential characteristics of data and is increasingly emerging as a significant means of integrating information in the field of artificial intelligence. Coronary artery plaque represents a significant etiology of cardiovascular events, posing a diagnostic challenge for clinicians who are confronted with a multitude of nonspecific symptoms. To visualize the hierarchical relationship network graph of the molecular mechanisms underlying plaque properties and symptom phenotypes, patient symptomatology was extracted from electronic health record data from real-world clinical settings. Phenotypic networks were constructed utilizing clinical data and protein‒protein interaction networks. Machine learning techniques, including convolutional neural networks, Dijkstra's algorithm, and gene ontology semantic similarity, were employed to quantify clinical and biological features within the network. The resulting features were then utilized to train a K-nearest neighbor model, yielding 23 symptoms, 41 association rules, and 61 hub genes across the three types of plaques studied, achieving an area under the curve of 92.5%. Weighted correlation network analysis and pathway enrichment were subsequently utilized to identify lipid status-related genes and inflammation-associated pathways that could help explain the differences in plaque properties. To confirm the validity of the network graph model, we conducted coexpression analysis of the hub genes to evaluate their potential diagnostic value. Additionally, we investigated immune cell infiltration, examined the correlations between hub genes and immune cells, and validated the reliability of the identified biological pathways. By integrating clinical data and molecular network information, this biomedical knowledge graph model effectively elucidated the potential molecular mechanisms that collude symptoms, diseases, and molecules.

Clinical Interviewing: An Essential but Neglected Method of Medicine

Clinical interviewing is the basic method to understand how a person feels and what are the presenting complaints, obtain medical history, evaluate personal attitudes and behavior related to health and disease, give the patient information about diagnosis, prognosis, and treatment, and establish a bond between patient and physician that is crucial for shared decision making and self-management. However, the value of this basic skill is threatened by time pressures and emphasis on technology. Current health care trends privilege expensive tests and procedures and tag the time devoted to interaction with the patient as lacking cost-effectiveness. Instead, the time spent to inquire about problems and life setting may actually help to avoid further testing, procedures, and referrals. Moreover, the dialogue between patient and physician is an essential instrument to increase patient's motivation to engage in healthy behavior. The aim of this paper was to provide an overview of clinical interviewing and its optimal use in relation to style, flow and hypothesis testing, clinical domains, modifications according to settings and goals, and teaching. This review points to the primacy of interviewing in the clinical process. The quality of interviewing determines the quality of data that are collected and, eventually, of assessment and treatment. Thus, interviewing deserves more attention in educational training and more space in clinical encounters than it is currently receiving.

The Daily Patterns of Emergency Medical Events

This study examines population-level daily patterns of time-stamped emergency medical service (EMS) dispatches to establish their situational predictability. Using visualization, sinusoidal regression, and statistical tests to compare empirical cumulative distributions, we analyzed 311,848,450 emergency medical call records from the US National Emergency Medical Services Information System (NEMSIS) for years 2010 through 2022. The analysis revealed a robust daily pattern in the hourly distribution of distress calls across 33 major categories of medical emergency dispatch types. Sinusoidal regression coefficients for all types were statistically significant, mostly at the p < 0.0001 level. The coefficient of determination ( R 2 ) ranged from 0.84 and 0.99 for all models, with most falling in the 0.94 to 0.99 range. The common sinusoidal pattern, peaking in mid-afternoon, demonstrates that all major categories of medical emergency dispatch types appear to be influenced by an underlying daily rhythm that is aligned with daylight hours and common sleep/wake cycles. A comparison of results with previous landmark studies revealed new and contrasting EMS patterns for several long-established peak occurrence hours-specifically for chest pain, heart problems, stroke, convulsions and seizures, and sudden cardiac arrest/death. Upon closer examination, we also found that heart attacks, diagnosed by paramedics in the field via 12-lead cardiac monitoring, followed the identified common daily pattern of a mid-afternoon peak, departing from prior generally accepted morning tendencies. Extended analysis revealed that the normative pattern prevailed across the NEMSIS data when reorganized to consider monthly, seasonal, daylight-savings versus civil time, and pre-/post-COVID-19 periods. The predictable daily EMS patterns provide impetus for more research that links daily variation with causal risk and protective factors. Our methods are straightforward and presented with detail to provide accessible and replicable implementation for researchers and practitioners.

Clinical Reasoning: Perspectives of Expert Clinicians on Reasoning Through Complex Clinical Cases

Introduction Clinical reasoning is a core skill for physicians; most doctors do not attain the level of expertise associated with that of an expert clinician (EC). The purpose of this study is to identify the clinical reasoning strategies ECs prioritize when reasoning through complex cases. Methods We interviewed 14 ECs and performed a thematic analysis to identify strategies ECs prioritize when reasoning through complex clinical cases. The authors chose ECs based on the recognition of clinical and teaching expertise by trainees and other faculty members (ECs within our institution) and institutional recognition of high achievement in medicine and medical education (ECs outside our institution). We used a semi-structured guide to interview each EC, then reviewed and coded the interview transcriptions. We developed themes based on agreements between all transcript reviewers.  Results We interviewed 11 male and three female ECs, one from outside the study institution. Two (14%) ECs were primary care physicians, and the remaining were sub-specialists. The authors organized strategies for clinical reasoning through complex cases around four themes, which were as follows: (1) connecting clinical reasoning to patient context; (2) embracing uncertainty, then reducing it; (3) returning to the patient's bedside; and (4) remaining humble to limit diagnostic errors. Conclusion Clinical reasoning is a core clinical skill of physicians, and this article describes clinical reasoning strategies prioritized by ECs for complex clinical cases. Recognition and integration of these strategies into medical training and clinical educator practice may facilitate the evolution of clinical reasoning skills and reduce diagnostic errors.

Sarcopenic Dysphagia and Simplified Rehabilitation Nutrition Care Process: An Update

Sarcopenic dysphagia is characterized by weakness of swallowing-related muscles associated with whole-body sarcopenia. As the number of patients with sarcopenia increases with the aging of the world, the number of patients with sarcopenic dysphagia is also increasing. The prevalence of sarcopenic dysphagia is high in the institutionalized older people and in patients hospitalized for pneumonia with dysphagia in acute care hospitals. Prevention, early detection and intervention of sarcopenic dysphagia with rehabilitation nutrition are essential. The diagnosis of sarcopenic dysphagia is based on skeletal and swallowing muscle strength and muscle mass. A reliable and validated diagnostic algorithm for sarcopenic dysphagia is used. Sarcopenic dysphagia is associated with malnutrition, which leads to mortality and Activities of Daily Living (ADL) decline. The rehabilitation nutrition approach improves swallowing function, nutrition status, and ADL. A combination of aggressive nutrition therapy to improve nutrition status, dysphagia rehabilitation, physical therapy, and other interventions can be effective for sarcopenic dysphagia. The rehabilitation nutrition care process is used to assess and problem solve the patient's pathology, sarcopenia, and nutrition status. The simplified rehabilitation nutrition care process consists of a nutrition cycle and a rehabilitation cycle, each with five steps: assessment, diagnosis, goal setting, intervention, and monitoring. Nutrition professionals and teams implement the nutrition cycle. Rehabilitation professionals and teams implement the rehabilitation cycle. Both cycles should be done simultaneously. The nutrition diagnosis of undernutrition, overnutrition/obesity, sarcopenia, and goal setting of rehabilitation and body weight are implemented collaboratively.

Hospital nurses' management of agitation in older cognitively impaired patients: do they recognise pain-related agitation?

BACKGROUND

cognitively impaired hospital patients often experience agitation and aggression due to pain. Agitation complicates care, increasing the risk of adverse outcomes and patient-to-nurse violence. Managing agitation is challenging for nurses. Literature suggests they may rely on antipsychotics while missing other more appropriately targeted treatments. However, nurses' management of agitation remains unclear and under-researched.

OBJECTIVE

the aim of this study was to investigate hospital nurses' management of agitation in older cognitively impaired patients with pain.

DESIGN

this was a descriptive correlational study using virtual simulation.

SETTING AND PARTICIPANTS

a total of 274 registered medical and surgical nurses from 10 public hospitals in Queensland, Australia participated in the study.

METHODS

nurses undertook a virtual simulation requiring them to manage agitation in a patient with dementia and an injury. Nurses also completed a post-simulation questionnaire. Their simulation performances were correlated with demographics such as seniority, workplace, training, experience and gerontology-specific knowledge. Constructed from an original, validated vignette, the simulation included branching pathways, video scenarios and an avatar that could converse with participants.

RESULTS

thirteen nurses (4.7%) recognised and treated the virtual patient's agitation as pain-related. Most nurses (89%) gave antipsychotics of which 207 (78%) gave these first-line and 102 (38%) used them twice. Independent of other variables, nurses most likely to diagnose pain were dementia-unit nurses (OR = 8.7), surgical-unit nurses (OR = 7.3) and senior nurses (OR = 5).

CONCLUSIONS

hospital nurses predominately managed agitation with antipsychotics, a decision that most made after undertaking inadequate patient assessments. This confirmed a common gap in practice that may lead to the missing of pain in the clinical care of agitated patients with dementia and/or delirium.

Anatomy of diagnosis in a clinical encounter: how clinicians discuss uncertainty with patients

Background

Studies consider the clinical encounter as linear, comprising six phases (opening, problem presentation, history-taking, physical examination, diagnosis, treatment and closing). This study utilizes formal conversation analysis to explore patient-physician interactions and understanding diagnostic utterances during these phases.

Methods

This study is a qualitative sub-analysis that explores how the diagnosis process, along with diagnostic uncertainty, are addressed during 28 urgent care visits. We analyzed physicians’ hypothesis-generation process by focusing on: location of diagnostic utterances during the encounter; whether certain/uncertain diagnostic utterances were revised throughout the encounter; and how physicians tested their hypothesis-generation and managed uncertainty. We recruited 7 primary care physicians (PCPs) and their 28 patients from Brigham and Women’s Hospital (BWH) in 3 urgent care settings. Encounters were audiotaped, transcribed, and coded using NVivo12 qualitative data analysis software. Data were analyzed inductively and deductively, using formal content and conversation analysis.

Results

We identified 62 diagnostic communication utterances in 12 different clinical situations. In most (24/28, 86%) encounters, the diagnosis process was initiated before the diagnosis phase (57% during history taking and 64% during physical examination). In 17 encounters (61%), a distinct diagnosis phase was not observed. Findings show that the diagnosis process is nonlinear in two ways. First, nonlinearity was observed when diagnostic utterances occurred throughout the encounter, with the six encounter phases overlapping, integrating elements of one phase with another. Second, nonlinearity was noted with respect to the resolution of diagnostic uncertainty, with physicians acknowledging uncertainty when explaining their diagnostic reasoning, even during brief encounters.

Conclusions

Diagnosis is often more interactive and nonlinear, and expressions of diagnostic assessments can occur at any point during an encounter, allowing more flexible and potentially more patient-centered communication. These findings are relevant for physicians’ training programs and helping clinicians improve their communication skills in managing uncertain diagnoses.

Clinical judgement by primary care physicians for the diagnosis of all-cause dementia or cognitive impairment in symptomatic people

BACKGROUND

In primary care, general practitioners (GPs) unavoidably reach a clinical judgement about a patient as part of their encounter with patients, and so clinical judgement can be an important part of the diagnostic evaluation. Typically clinical decision making about what to do next for a patient incorporates clinical judgement about the diagnosis with severity of symptoms and patient factors, such as their ideas and expectations for treatment. When evaluating patients for dementia, many GPs report using their own judgement to evaluate cognition, using information that is immediately available at the point of care, to decide whether someone has or does not have dementia, rather than more formal tests.

OBJECTIVES

To determine the diagnostic accuracy of GPs' clinical judgement for diagnosing cognitive impairment and dementia in symptomatic people presenting to primary care. To investigate the heterogeneity of test accuracy in the included studies.

SEARCH METHODS

We searched MEDLINE (Ovid SP), Embase (Ovid SP), PsycINFO (Ovid SP), Web of Science Core Collection (ISI Web of Science), and LILACs (BIREME) on 16 September 2021.

SELECTION CRITERIA

We selected cross-sectional and cohort studies from primary care where clinical judgement was determined by a GP either prospectively (after consulting with a patient who has presented to a specific encounter with the doctor) or retrospectively (based on knowledge of the patient and review of the medical notes, but not relating to a specific encounter with the patient). The target conditions were dementia and cognitive impairment (mild cognitive impairment and dementia) and we included studies with any appropriate reference standard such as the Diagnostic and Statistical Manual of Mental Disorders (DSM), International Classification of Diseases (ICD), aetiological definitions, or expert clinical diagnosis.

DATA COLLECTION AND ANALYSIS

Two review authors screened titles and abstracts for relevant articles and extracted data separately with differences resolved by consensus discussion. We used QUADAS-2 to evaluate the risk of bias and concerns about applicability in each study using anchoring statements. We performed meta-analysis using the bivariate method.

MAIN RESULTS

We identified 18,202 potentially relevant articles, of which 12,427 remained after de-duplication. We assessed 57 full-text articles and extracted data on 11 studies (17 papers), of which 10 studies had quantitative data. We included eight studies in the meta-analysis for the target condition dementia and four studies for the target condition cognitive impairment. Most studies were at low risk of bias as assessed with the QUADAS-2 tool, except for the flow and timing domain where four studies were at high risk of bias, and the reference standard domain where two studies were at high risk of bias. Most studies had low concern about applicability to the review question in all QUADAS-2 domains. Average age ranged from 73 years to 83 years (weighted average 77 years). The percentage of female participants in studies ranged from 47% to 100%. The percentage of people with a final diagnosis of dementia was between 2% and 56% across studies (a weighted average of 21%). For the target condition dementia, in individual studies sensitivity ranged from 34% to 91% and specificity ranged from 58% to 99%. In the meta-analysis for dementia as the target condition, in eight studies in which a total of 826 of 2790 participants had dementia, the summary diagnostic accuracy of clinical judgement of general practitioners was sensitivity 58% (95% confidence interval (CI) 43% to 72%), specificity 89% (95% CI 79% to 95%), positive likelihood ratio 5.3 (95% CI 2.4 to 8.2), and negative likelihood ratio 0.47 (95% CI 0.33 to 0.61). For the target condition cognitive impairment, in individual studies sensitivity ranged from 58% to 97% and specificity ranged from 40% to 88%. The summary diagnostic accuracy of clinical judgement of general practitioners in four studies in which a total of 594 of 1497 participants had cognitive impairment was sensitivity 84% (95% CI 60% to 95%), specificity 73% (95% CI 50% to 88%), positive likelihood ratio 3.1 (95% CI 1.4 to 4.7), and negative likelihood ratio 0.23 (95% CI 0.06 to 0.40). It was impossible to draw firm conclusions in the analysis of heterogeneity because there were small numbers of studies. For specificity we found the data were compatible with studies that used ICD-10, or applied retrospective judgement, had higher reported specificity compared to studies with DSM definitions or using prospective judgement. In contrast for sensitivity, we found studies that used a prospective index test may have had higher sensitivity than studies that used a retrospective index test.

AUTHORS' CONCLUSIONS

Clinical judgement of GPs is more specific than sensitive for the diagnosis of dementia. It would be necessary to use additional tests to confirm the diagnosis for either target condition, or to confirm the absence of the target conditions, but clinical judgement may inform the choice of further testing. Many people who a GP judges as having dementia will have the condition. People with false negative diagnoses are likely to have less severe disease and some could be identified by using more formal testing in people who GPs judge as not having dementia. Some false positives may require similar practical support to those with dementia, but some - such as some people with depression - may suffer delayed intervention for an alternative treatable pathology.

Does Walking Help to Generate a Differential Diagnosis?

TheoryIn Medicine, arriving at the correct diagnosis is of paramount importance for patient health and safety, yet is a difficult task especially when a patient presents with symptoms that do not fit typical patterns of disease. This task can be further complicated by errors of judgment, with the failure to consider all possible diagnoses being the most common of such errors. In this study, we investigated the process of differential diagnosis generation within the growing evidence that diagnostic performance can be increased by activities such as walking as was previously shown in Oppezzo and Schwartz's 2014 study. Hypotheses: It was hypothesized that an increase in performance, as expressed by a greater number of plausible differential diagnoses, would be seen in the walking group. Method: Eighteen medical students in their last two months of pre-clerkship training and eighteen second year family medicine residents were shown four different lists of a constellation of signs and symptoms. Participants were asked to generate differential diagnoses over five minutes per each list. All participants sat when completing the first two lists (pretest phase), and then were equally and randomly assigned to sitting versus walking on a treadmill when completing the last two lists (post-test phase). The number of total and unique differential diagnoses generated was determined, before being submitted to a three-member expert panel who identified appropriate unique differential diagnoses. Results: Two-way mixed ANOVAs were conducted to investigate the impact of exercise on the number of total, unique, and appropriate unique ideas generated and compared between pretest and post-test phases. Conclusions: We conclude that there is neither an increase nor a decrease in the number or quality of differential diagnoses generated by the sitting and walking groups within a population that has acquired some level of expertise.

Why is formulation daunting? A response to Bagster et al.'s 'instructions' for developing skills in formulation

OBJECTIVE

To explore the theme identified by Bagster et al.¹ in their selective psychiatric literature review that formulation can appear daunting.

CONCLUSION

Formulation is understandably daunting, even though it occurs in all human encounters. The plural nature of mental symptoms is such that anxiety-provoking intuitive judgement is required at all points in both the process and explication of formulation, a type of instinctive guessing. There are no rules for this, because the laws of vertical integration of systems are not established. Guidelines are more appropriate than 'instructions'. Much of the wider mental health and clinical reasoning literature addresses intuitive judgement, but the current psychiatric literature tends to focus on pattern recognition as a deliberative cognitive act of Type 2 processes. Arguably this reductionism adds to the dauntingness. Anxiety detected about the intuitive judgement involved can be addressed in supervision, taking into account the psychological mindedness of the trainee.

Sex Differences in Symptom Phenotypes Among Older Patients with Acute Myocardial Infarction

BACKGROUND

Clinicians make a medical diagnosis by recognizing diagnostic possibilities, often using memories of prior examples. These memories, called "exemplars," reflect specific symptom combinations in individual patients, yet most clinical studies report how symptoms aggregate in populations. We studied how symptoms of acute myocardial infarction combine in individuals as symptom phenotypes and how symptom phenotypes are distributed in women and men.

METHODS

In this analysis of the SILVER-AMI Study, we studied 3041 patients (1346 women and 1645 men) 75 years of age or older with acute myocardial infarction. Each patient had a standardized in-person interview during the acute myocardial infarction admission to document the presenting symptoms, which enabled a thorough examination of symptom combinations in individuals. Specific symptom combinations defined symptom phenotypes and distributions of symptom phenotypes were compared in women and men using Monte Carlo permutation testing and repeated subsampling.

RESULTS

There were 1469 unique symptom phenotypes in the entire SILVER-AMI cohort of patients with acute myocardial infarction. There were 831 unique symptom phenotypes in women, as compared with 819 in men, which was highly significant, given the larger number of men than women in the study (P < .0001). Women had significantly more symptom phenotypes than men in almost all acute myocardial infarction subgroups.

CONCLUSIONS

Older patients with acute myocardial infarction have enormous variation in symptom phenotypes. Women reported more symptoms and had significantly more symptom phenotypes than men. Appreciation of the diversity of symptom phenotypes may help clinicians recognize the less common phenotypes that occur more often in women.

Diagnostic Accuracy of Suspected Appendicitis: A Comparative Analysis of Misdiagnosed Appendicitis in Children

OBJECTIVE

When evaluating suspected appendicitis, limited data support quality benchmarks for negative appendectomy (NA); none exist for delayed diagnosis of appendicitis (DDA). The objectives of this study are the following: (1) to provide preliminary evidence supporting a quality benchmark for DDA and 2) to compare presenting features and diagnostic evaluations of children with NA and DDA with those with pathology-confirmed appendicitis (PCA) diagnosed during initial emergency department (ED) encounter.

METHODS

Secondary analysis of data from a QI project designed to reduce the use computed tomography when evaluating suspected appendicitis using a case-control design. Patients undergoing appendectomy in an academic tertiary care children's hospital system between January 1, 2015, and December 31, 2016 (n = 1,189) were eligible for inclusion in this case-control study. Negative appendectomy was defined as no pathologic change or findings consistent with a different diagnosis. Delayed diagnosis of appendicitis was defined as patients undergoing appendectomy within 7 days of a prior ED visit for a related complaint. Controls of PCA (n = 150) were randomly selected from all cases undergoing appendectomy.

RESULTS

There were 42 NA (3.5%) and 31 DDA (2.6%). Cases of PCA and NA exhibited similar histories, examination findings, and underwent comparable diagnostic evaluations. Cases of PCA more frequently demonstrated a white blood cell count greater than 10 × 103/μL (85% vs 67%; P = 0.01), a left-shift (77% vs 45%; P < 0.001), and an ultrasound interpretation with high probability for appendicitis (73% vs 54%; P = 0.03). Numerous significant differences in history, examination findings, and diagnostic tests performed existed between cases of PCA and DDA.

CONCLUSIONS

Children with PCA and NA present similarly and undergo comparable evaluations resulting in appendectomy. A 3% to 4% NA rate may be unavoidable given these similarities. Presenting features in DDA significantly differ from those of PCA. An irreducible proportion of appendicitis diagnoses may be delayed.

Diagnostic reasoning in cardiovascular medicine

Research in cognitive psychology shows that expert clinicians make a medical diagnosis through a two step process of hypothesis generation and hypothesis testing. Experts generate a list of possible diagnoses quickly and intuitively, drawing on previous experience. Experts remember specific examples of various disease categories as exemplars, which enables rapid access to diagnostic possibilities and gives them an intuitive sense of the base rates of various diagnoses. After generating diagnostic hypotheses, clinicians then test the hypotheses and subjectively estimate the probability of each diagnostic possibility by using a heuristic called anchoring and adjusting. Although both novices and experts use this two step diagnostic process, experts distinguish themselves as better diagnosticians through their ability to mobilize experiential knowledge in a manner that is content specific. Experience is clearly the best teacher, but some educational strategies have been shown to modestly improve diagnostic accuracy. Increased knowledge about the cognitive psychology of the diagnostic process and the pitfalls inherent in the process may inform clinical teachers and help learners and clinicians to improve the accuracy of diagnostic reasoning. This article reviews the literature on the cognitive psychology of diagnostic reasoning in the context of cardiovascular disease.

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

Objectives

Expert clinicians (ECs) are defined in large part as a group of physicians recognized by their peers for their diagnostic reasoning abilities. However, their reasoning skills have not been quantitatively compared to other clinicians using a validated instrument.

Methods

We surveyed Internal Medicine physicians at the University of Iowa to identify ECs. These 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). We compared scores between the two groups using the two-sample t-test.

Results

Seventeen ECs completed the inventory (100%). Out of 25 randomly-selected non-EC internists (IM), 19 completed the inventory (76%). Mean total scores were 187.2 and 175.8 for the EC and the IM groups respectively. Thinking and structure subscores were 91.5 and 95.71 for ECs, 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 ECs 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

ECs 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.

Differences in Performance of ASD and ADHD Subjects Facing Cognitive Loads in an Innovative Reasoning Experiment

We aim to investigate whether EEG dynamics differ in adults with ASD (Autism Spectrum Disorders) and ADHD (attention-deficit/hyperactivity disorder) compared with healthy subjects during the performance of an innovative cognitive task, Aristotle's valid and invalid syllogisms, and how these differences correlate with brain regions and behavioral data for each subject. We recorded EEGs from 14 scalp electrodes (channels) in 21 adults with ADHD, 21 with ASD, and 21 healthy, normal subjects. The subjects were exposed in a set of innovative cognitive tasks (inducing varying cognitive loads), Aristotle's two types of syllogism mentioned above. A set of 39 questions were given to participants related to valid-invalid syllogisms as well as a separate set of questionnaires, in order to collect a number of demographic and behavioral data, with the aim of detecting shared information with values of a feature extracted from EEG, the multiscale entropy (MSE), in the 14 channels ('brain regions'). MSE, a nonlinear information-theoretic measure of complexity, was computed to extract a feature that quantifies the complexity of the EEG. Behavior-Partial Least Squares Correlation, PLSC, is the method to detect the correlation between two sets of data, brain, and behavioral measures. -PLSC, a variant of PLSC, was applied to build a functional connectivity of the brain regions involved in the reasoning tasks. Graph-theoretic measures were used to quantify the complexity of the functional networks. Based on the results of the analysis described in this work, a mixed 14 × 2 × 3 ANOVA showed significant main effects of group factor and brain region* syllogism factor, as well as a significant brain region* group interaction. There are significant differences between the means of MSE (complexity) values at the 14 channels of the members of the 'pathological' groups of participants, i.e., between ASD and ADHD, while the difference in means of MSE between both ASD and ADHD and that of the control group is not significant. In conclusion, the valid-invalid type of syllogism generates significantly different complexity values, MSE, between ASD and ADHD. The complexity of activated brain regions of ASD participants increased significantly when switching from a valid to an invalid syllogism, indicating the need for more resources to 'face' the task escalating difficulty in ASD subjects. This increase is not so evident in both ADHD and control. Statistically significant differences were found also in the behavioral response of ASD and ADHD, compared with those of control subjects, based on the principal brain and behavior saliences extracted by PLSC. Specifically, two behavioral measures, the emotional state and the degree of confidence of participants in answering questions in Aristotle's valid-invalid syllogisms, and one demographic variable, age, statistically and significantly discriminate the three groups' ASD. The seed-PLC generated functional connectivity networks for ASD, ADHD, and control, were 'projected' on the regions of the Default Mode Network (DMN), the 'reference' connectivity, of which the structural changes were found significant in distinguishing the three groups. The contribution of this work lies in the examination of the relationship between brain activity and behavioral responses of healthy and 'pathological' participants in the case of cognitive reasoning of the type of Aristotle's valid and invalid syllogisms, using PLSC, a machine learning approach combined with MSE, a nonlinear method of extracting a feature based on EEGs that captures a broad spectrum of EEGs linear and nonlinear characteristics. The results seem promising in adopting this type of reasoning, in the future, after further enhancements and experimental tests, as a supplementary instrument towards examining the differences in brain activity and behavioral responses of ASD and ADHD patients. The application of the combination of these two methods, after further elaboration and testing as new and complementary to the existing ones, may be considered as a tool of analysis in helping detecting more effectively such types of disorders.

Qualitative development and content validation of the "SPART" model; a focused ethnography study of observable diagnostic and therapeutic activities in the emergency medical services care process

BACKGROUND

Clinical reasoning is a crucial task within the Emergency Medical Services (EMS) care process. Both contextual and cognitive factors make the task susceptible to errors. Understanding the EMS care process' structure could help identify and address issues that interfere with clinical reasoning. The EMS care process is complex and only basically described. In this research, we aimed to define the different phases of the process and develop an overarching model that can help detect and correct potential error sources, improve clinical reasoning and optimize patient care.

METHODS

We conducted a focused ethnography study utilizing non-participant video observations of real-life EMS deployments combined with thematic analysis of peer interviews. After an initial qualitative analysis of 7 video observations, we formulated a tentative conceptual model of the EMS care process. To test and refine this model, we carried out a qualitative, thematic analysis of 28 video-recorded cases. We validated the resulting model by evaluating its recognizability with a peer content analysis utilizing semi-structured interviews.

RESULTS

Based on real-life observations, we were able to define and validate a model covering the distinct phases of an EMS deployment. We have introduced the acronym "SPART" to describe ten different phases: Start, Situation, Prologue, Presentation, Anamnesis, Assessment, Reasoning, Resolution, Treatment, and Transfer.

CONCLUSIONS

The "SPART" model describes the EMS care process and helps to understand it. We expect it to facilitate identifying and addressing factors that influence both the care process and the clinical reasoning task embedded in this process.

Developing Clinical Reasoning in a Physician Assistant Curriculum: The University of Sheffield approach

ABSTRACT

Clinical reasoning remains a key area for development amongst clinical students across the world. Physician assistant (PA) roles (physician associate in the United Kingdom) are rapidly expanding, and there is a need to ensure that this core skill is reflected in course curricula. This article presents how the University of Sheffield has integrated clinical reasoning into the curriculum for its PA course. This includes recognizing the need to consider different approaches to reasoning and how they are taught, the assessment of reasoning across Miller's pyramid, and other considerations that contribute to embedding reasoning within the course. We discuss the implications of our approach and comment on issues that we may need to consider in the future.

Complexity analysis of the brain activity in Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) due to cognitive loads/demands induced by Aristotle's type of syllogism/reasoning. A Power Spectral Density and multiscale entropy (MSE) analysis

OBJECTIVE

We aim to investigate whether EEG dynamics differ in adults with ASD (Autism Spectrum Disorders), ADHD (attention-deficit/hyperactivity disorder), compared with healthy subjects during the performance of an innovative cognitive task: Aristotle's valid and invalid syllogisms. We follow the Neuroanatomical differences type of criterion in assessing the results of our study in supporting or not the dual-process theory of Kahneman, 2011) (Systems I & II of thinking).

METHOD

We recorded EEGs from 14 scalp electrodes in 30 adults with ADHD, 30 with ASD and 24 healthy, normal subjects. The subjects were exposed in a set of innovative cognitive tasks (inducing varying cognitive loads), the Aristotle's four types of syllogism mentioned above. The multiscale entropy (MSE), a nonlinear information-theoretic measure or tool was computed to extract features that quantify the complexity of the EEG.

RESULTS

The dynamics of the curves of the grand average of MSE values of the ADHD and ASD participants was significantly in higher levels for the majority of time scales, than the healthy subjects over a number of brain regions (electrodes locations), during the performance of both valid and invalid types of syllogism. This result is seemingly not in accordance of the broadly accepted 'theory' of complexity loss in 'pathological' subjects, but actually this is not the case as explained in the text. ADHD subjects are engaged in System II of thinking, for both Valid and Invalid syllogism, ASD and Control in System I for valid and invalid syllogism, respectively. A surprising and 'provocative' result of this paper, as shown in the next sections, is that the Complexity-variability of ASD and ADHD subjects, when they face Aristotle's types of syllogisms, is higher than that of the control subjects. An explanation is suggested as described in the text. Also, in the case of invalid type of Aristotelian syllogisms, the linguistic and visuo-spatial systems are both engaged ONLY in the temporal and occipital regions of the brain, respectively, of ADHD subjects. In the case of valid type, both above systems are engaged in the temporal and occipital regions of the brain, respectively, of both ASD and ADHD subjects, while in the control subjects only the visuo-spatial type is engaged (Goel et al., 2000; Knauff, 2007).

CONCLUSION

Based on the results of the analysis described in this work, the differences in the EEG complexity between the three groups of participants lead to the conclusion that cortical information processing is changed in ASD and ADHD adults, therefore their level of cortical activation may be insufficient to meet the peculiar cognitive demand of Aristotle's reasoning.

SIGNIFICANCE

The present paper suggest that MSE, is a powerful and efficient nonlinear measure in detecting neural dysfunctions in adults with ASD and ADHD characteristics, when they are called on to perform in a very demanding as well as innovative set of cognitive tasks, that can be considered as a new diagnostic 'benchmark' in helping detecting more effectively such type of disorders. A linear measure alone, as the typical PSD, is not capable in making such a distinction. The work contributes in shedding light on the neural mechanisms of syllogism/reasoning of Aristotelian type, as well as toward understanding how humans reason logically and why 'pathological' subjects deviate from the norms of formal logic.

AI models and the future of genomic research and medicine: True sons of knowledge?: Artificial intelligence needs to be integrated with causal conceptions in biomedicine to harness its societal benefits for the field

The increasing availability of large-scale, complex data has made research into how human genomes determine physiology in health and disease, as well as its application to drug development and medicine, an attractive field for artificial intelligence (AI) approaches. Looking at recent developments, we explore how such approaches interconnect and may conflict with needs for and notions of causal knowledge in molecular genetics and genomic medicine. We provide reasons to suggest that-while capable of generating predictive knowledge at unprecedented pace and scale-if and how these approaches will be integrated with prevailing causal concepts will not only determine the future of scientific understanding and self-conceptions in these fields. But these questions will also be key to develop differentiated policies, such as for education and regulation, in order to harness societal benefits of AI for genomic research and medicine.

Use of a structured approach and virtual simulation practice to improve diagnostic reasoning

OBJECTIVES

In 2015, the National Academy of Medicine IOM estimated that 12 million patients were misdiagnosed annually. This suggests that despite prolonged training in medical school and residency there remains a need to improve diagnostic reasoning education. This study evaluates a new approach.

METHODS

A total of 285 medical students were enrolled in this 8 center, IRB approved trial. Students were randomized to receive training in either abdominal pain (AP) or loss of consciousness (LOC). Baseline diagnostic accuracy of the two different symptoms was assessed by completing a multiple-choice question (MCQ) examination and virtual patient encounters. Following a structured educational intervention, including a lecture on the diagnostic approach to that symptom and three virtual patient practice cases, each student was re-assessed.

RESULTS

The change in diagnostic accuracy on virtual patient encounters was compared between (1) baseline and post intervention and (2) post intervention students trained in the prescribed symptom vs. the alternate symptom (controls). The completeness of the student's differential diagnosis was also compared. Comparison of proportions were conducted using χ ²-tests. Mixed-effects regressions were used to examine differences accounting for case and repeated measures. Compared with baseline, both the AP and LOC groups had marked post-intervention improvements in obtaining a correct final diagnosis; a 27% absolute improvement in the AP group (p<0.001) and a 32% absolute improvement in the LOC group (p<0.001). Compared with controls (the groups trained in the alternate symptoms), the rate of correct diagnoses increased by 13% but was not statistically significant (p=0.132). The completeness and efficiency of the differential diagnoses increased by 16% (β=0.37, p<0.001) and 17% respectively (β=0.45, p<0.001).

CONCLUSIONS

The study showed that a virtual patient platform combined with a diagnostic reasoning framework could be used for education and diagnostic assessment and improved correct diagnosis compared with baseline performance in a simulated platform.

Forecasting the Walking Assistance Rehabilitation Level of Stroke Patients Using Artificial Intelligence

Cerebrovascular accidents (CVA) cause a range of impairments in coordination, such as a spectrum of walking impairments ranging from mild gait imbalance to complete loss of mobility. Patients with CVA need personalized approaches tailored to their degree of walking impairment for effective rehabilitation. This paper aims to evaluate the validity of using various machine learning (ML) and deep learning (DL) classification models (support vector machine, Decision Tree, Perceptron, Light Gradient Boosting Machine, AutoGluon, SuperTML, and TabNet) for automated classification of walking assistant devices for CVA patients. We reviewed a total of 383 CVA patients’ (1623 observations) prescription data for eight different walking assistant devices from five hospitals. Among the classification models, the advanced tree-based classification models (LightGBM and tree models in AutoGluon) achieved classification results of over 90% accuracy, recall, precision, and F1-score. In particular, AutoGluon not only presented the highest predictive performance (almost 92% in accuracy, recall, precision, and F1-score, and 86.8% in balanced accuracy) but also demonstrated that the classification performances of the tree-based models were higher than that of the other models on its leaderboard. Therefore, we believe that tree-based classification models have potential as practical diagnosis tools for medical rehabilitation.

What faculty write versus what students see? Perspectives on multiple-choice questions using Bloom's taxonomy

BACKGROUND

Using revised Bloom's taxonomy, some medical educators assume they can write multiple choice questions (MCQs) that specifically assess higher (analyze, apply) versus lower-order (recall) learning. The purpose of this study was to determine whether three key stakeholder groups (students, faculty, and education assessment experts) assign MCQs the same higher- or lower-order level.

METHODS

In Phase 1, stakeholders' groups assigned 90 MCQs to Bloom's levels. In Phase 2, faculty wrote 25 MCQs specifically intended as higher- or lower-order. Then, 10 students assigned these questions to Bloom's levels.

RESULTS

In Phase 1, there was low interrater reliability within the student group (Krippendorf's alpha = 0.37), the faculty group (alpha = 0.37), and among three groups (alpha = 0.34) when assigning questions as higher- or lower-order. The assessment team alone had high interrater reliability (alpha = 0.90). In Phase 2, 63% of students agreed with the faculty as to whether the MCQs were higher- or lower-order. There was low agreement between paired faculty and student ratings (Cohen's Kappa range .098-.448, mean .256).

DISCUSSION

For many questions, faculty and students did not agree whether the questions were lower- or higher-order. While faculty may try to target specific levels of knowledge or clinical reasoning, students may approach the questions differently than intended.

Influence of patient immigrant status on physician trainee diabetes treatment decisions: a virtual patient experimental study

To determine the effect of patient immigrant status on physician trainees' diabetes treatment decisions. Participants were 140 physician trainees ('providers'). Providers viewed videos and vignettes of virtual patients differing in immigrant status (born in Mexico or U.S.; other characteristics held constant). Analyses were completed at the group and individual levels. Providers were less likely to refer foreign-born (vs. U.S.-born) patients to endocrinology. Individual-level results showed an almost even split between treatment ratings for foreign-born vs. U.S.-born patients for three decisions (take no action, add oral hypoglycemic agent, add/switch to insulin), explaining why group-level differences for these ratings did not emerge (i.e., they were cancelled out). Physician trainees are less likely to refer foreign-born patients to endocrinology. Half of individual-level decisions were influenced by patient immigrant status, but group-level analyses mask these differences. Systematic treatment differences based on non-relevant factors could lead to adverse outcomes for immigrants.

Diagnostic reasoning in internal medicine: a practical reappraisal

The 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.

How do cognitive processes influence script concordance test responses?

INTRODUCTION

The script concordance test (SCT) is a test of clinical decision-making (CDM) that compares the thought process of learners to that of experts to determine to what extent their cognitive 'scripts' align. Without understanding test-takers' cognitive process, however, it is unclear what influences their responses. The objective of this study was to gather response process validity evidence by studying the cognitive process of test-takers to determine whether the SCT tests CDM and what cognitive processes may influence SCT responses.

METHODS

Cases from an SCT used in a national validation study were administered and semi-structured cognitive interviews were conducted with ten residents and five staff surgeons. A retrospective verbal probing technique was used. Data was independently analysed and coded by two analysts. Themes were identified as factors that influence SCT responses during the cognitive interview.

RESULTS

Cognitive interviews demonstrated variability in CDM among test-takers. Consistent with dual process theory, test-takers relied on scripts formed through past experiences, when available, to make decisions and used conscious deliberation in the absence of experience. However, test-takers' response process was also influenced by their comprehension of specific terms, desire for additional information, disagreement with the planned management, underlying knowledge gaps and desire to demonstrate confidence or humility.

CONCLUSION

The rationale behind SCT answers may be influenced by comprehension, underlying knowledge and social desirability in addition to formed scripts and/or conscious deliberation. Having test-takers verbalise their rationale for responses provides a depth of assessment that is otherwise lost in the SCT's current format. With the improved ability to standardise CDM assessment using the SCT, consideration of test-makers improving the SCT construction process and combining the SCT question format with verbal responses may improve the use of the SCT for CDM assessment.

Predictors of Failure for Nonoperative Management of Spinal Epidural Abscess

STUDY DESIGN

Retrospective cohort study.

OBJECTIVES

The aim of this study is to identify predictive factors associated with failure of nonoperative management of spinal epidural abscess (SEA).

METHODS

Between January 2007 and January 2017, there were 97 patients 18 years or older treated for SEA at a tertiary referral center. Of these, 58 were initially managed nonoperatively. Details on presenting complaint, laboratory parameters, radiographic evaluation, demographics, comorbidities, and neurologic status (Frankel grades A-E) were collected. Success of treatment was defined as eradication of infection with no requirement for further antimicrobial therapy. Diagnosis of SEA was made via evaluation of imaging and intraoperative findings. Patients with repeat presentation of SEA, children, and those who were transferred for immediate surgical decompression were excluded.

RESULTS

Fifty-eight patients initially treated nonoperatively were included. Of these, 21 failed nonoperative management and required surgical intervention. The mean age was 60 years, 66% male, and 19% of Maori ethnicity. Abscess location was predominantly dorsal, and in the lumbar region (53%). Multivariate analysis identified Maori ethnicity, multifocal sepsis, and elevated white cell count as predictors of failure of nonoperative management. With 1 predictor the risk of failure was 44%. In the presence of 2 predictive variables, failure rate increased to 60%, and if all 3 variables were present, patients had a 75% risk of failure.

CONCLUSION

Thirty-six percent of patients treated nonoperatively failed nonoperative management-the failure rate was significantly increased in patients with multifocal sepsis, in patients with elevated white cell count, and in patients of Maori ethnicity.

A dual process model for paleopathological diagnosis

OBJECTIVES

This paper aims to provide an explicit theoretical model for the cognitive processes involved in paleopathological diagnosis.

METHODS

The approach adopted is a dual process model (DPM). DPMs recognize that cognition is a result of both Type 1 (intuitive) and Type 2 (analytical) processes. DPMs have been influential for understanding decision-making in a range of fields, including diagnosis in clinical medicine. Analogies are drawn between diagnosis in a clinical and a paleopathological setting.

RESULTS

In clinical medicine, both Type 1 and Type 2 processes play a part in diagnosis. In paleopathology the role of Type 1 processes has been unacknowledged. However, like clinical diagnosis, paleopathological diagnosis is inherently a result of a combination of both Type 1 and Type 2 processes. A model is presented by which Type 1 processes can be explicitly incorporated into a scientific approach to diagnosis from skeletal remains, and in which diagnosis is formalized as a process of hypothesis testing.

SIGNIFICANCE

Accurately modelling our diagnostic processes allows us to understand the biases and limitations in our work and potentially helps us to improve our procedures, including how we impart diagnostic skills in pedagogical settings.

LIMITATIONS

This work provides a theoretical model for paleopathological diagnosis. However, such models are by their nature dynamic and developing rather than static entities; it is hoped that this work stimulates further debate and discussion in this important area.

An Artificial Neural Network Approach and a Data Augmentation Algorithm to Systematize the Diagnosis of Deep-Vein Thrombosis by Using Wells’ Criteria

The use of a back-propagation artificial neural network (ANN) to systematize the reliability of a Deep Vein Thrombosis (DVT) diagnostic by using Wells’ criteria is introduced herein. In this paper, a new ANN model is proposed to improve the Accuracy when dealing with a highly unbalanced dataset. To create the training dataset, a new data augmentation algorithm based on statistical data known as the prevalence of DVT of real cases reported in literature and from the public hospital is proposed. The above is used to generate one dataset of 10,000 synthetic cases. Each synthetic case has nine risk factors according to Wells’ criteria and also the use of two additional factors, such as gender and age, is proposed. According to interviews with medical specialists, a training scheme was established. In addition, a new algorithm is presented to improve the Accuracy and Sensitivity/Recall. According to the proposed algorithm, two thresholds of decision were found, the first one is 0.484, which is to improve Accuracy. The other one is 0.138 to improve Sensitivity/Recall. The Accuracy achieved is 90.99%, which is greater than that obtained with other related machine learning methods. The proposed ANN model was validated performing the k-fold cross validation technique using a dataset with 10,000 synthetic cases. The test was performed by using 59 real cases obtained from a regional hospital, achieving an Accuracy of 98.30%.

Assessing clinical reasoning in undergraduate medical students during history taking with an empirically derived scale for clinical reasoning indicators

BACKGROUND

The clinical reasoning process, which requires biomedical knowledge, knowledge about problem-solving strategies, and knowledge about reasons for diagnostic procedures, is a key element of physicians' daily practice but difficult to assess. The aim of this study was to empirically develop a Clinical Reasoning Indicators-History Taking-Scale (CRI-HT-S) and to assess the clinical reasoning ability of advanced medical students during a simulation involving history taking.

METHODS

The Clinical Reasoning Indictors-History Taking-Scale (CRI-HT-S) including a 5-point Likert scale for assessment was designed from clinical reasoning indicators identified in a qualitative study in 2017. To assess indicators of clinical reasoning ability, 65 advanced medical students (semester 10, n = 25 versus final year, n = 40) from three medical schools participated in a 360-degree competence assessment in the role of beginning residents during a simulated first workday in hospital. This assessment included a consultation hour with five simulated patients which was videotaped. Videos of 325 patient consultations were assessed using the CRI-HT-S. A factor analysis was conducted and the students' results were compared according to their advancement in undergraduate medical training.

RESULTS

The clinical reasoning indicators of the CRI-HT-S loaded on three factors relevant for clinical reasoning: 1) focusing questions, 2) creating context, and 3) securing information. Students reached significantly different scores (p < .001) for the three factors (factor 1: 4.07 ± .47, factor 2: 3.72 ± .43, factor 3: 2.79 ± .83). Students in semester 10 reached significantly lower scores for factor 3 than students in their final year (p < .05).

CONCLUSIONS

The newly developed CRI-HT-S worked well for quantitative assessment of clinical reasoning indicators during history taking. Its three-factored structure helped to explore different aspects of clinical reasoning. Whether the CRI-HT-S has the potential to be used as a scale in objective structured clinical examinations (OCSEs) or in workplace-based assessments of clinical reasoning has to be investigated in further studies with larger student cohorts.

How good is our diagnostic intuition? Clinician prediction of bacteremia in critically ill children

Background

Clinical intuition and nonanalytic reasoning play a major role in clinical hypothesis generation; however, clinicians’ intuition about whether a critically ill child is bacteremic has not been explored. We endeavored to assess pediatric critical care clinicians’ ability to predict bacteremia and to evaluate what affected the accuracy of those predictions.

Methods

We conducted a retrospective review of clinicians’ responses to a sepsis screening tool (“Early Sepsis Detection Tool” or “ESDT”) over 6 months. The ESDT was completed during the initial evaluation of a possible sepsis episode. If a culture was ordered, they were asked to predict if the culture would be positive or negative. Culture results were compared to predictions for each episode as well as vital signs and laboratory data from the preceding 24 h.

Results

From January to July 2017, 266 ESDTs were completed. Of the 135 blood culture episodes, 15% of cultures were positive. Clinicians correctly predicted patients with bacteremia in 82% of cases, but the positive predictive value was just 28% as there was a tendency to overestimate the presence of bacteremia. The negative predictive value was 96%. The presence of bandemia, thrombocytopenia, and abnormal CRP were associated with increased likelihood of correct positive prediction.

Conclusions

Clinicians are accurate in predicting critically ill children whose blood cultures, obtained for symptoms of sepsis, will be negative. Clinicians frequently overestimate the presence of bacteremia. The combination of evidence-based practice guidelines and bedside judgment should be leveraged to optimize diagnosis of bacteremia.

Person- and job-specific factors of intuitive decision-making in clinical practice: results of a sample survey among Hungarian physicians and nurses

Objective: To assess the prevalence of intuitive decision-making (IDM) among health care practitioners (HCPs) and explore its person- and job-specific factors. Design and Outcome Measures: We used on-line survey data from a cross-sectional sample of Hungarian physicians and nurses (N = 460) to assess their reliance on IDM. In a second survey we asked physicians (N = 104) to rate medical specialties on dimensions of 'emergency' (necessity of making instantaneous decisions in unforeseeable situations) and 'complexity' (necessity of considering multiple perceptual and diagnostic aspects of patients' health condition along with diverse treatment options). Results: Altogether 40% of participants reported ever relying on IDM. Using logistic regression analysis, we found the estimated probability of IDM was 0.24 greater for physicians than for nurses, 0.10 greater for females than for males, and 0.11 greater for advanced level HCPs than for novices. Reaching expert level further increased (by 0.31) the probability of IDM for physicians, but not for nurses. Concerning physicians, practicing in a medical specialty of 'high likelihood of emergency' or 'high complexity' increased the probability of IDM by 0.25 and 0.23; the same effects for nurses were 0.20 and 0.07. We found some (inconclusive) evidence for education positively influencing HCPs' propensity for IDM. Additionally, we performed content analysis of participants' free-text answers to explore the psychological background of IDM instances. HCPs educated in the subject of IDM were found more disposed to perform or request further medical investigation, less prone to deviate from medical protocols, apter to reflect on their mental processes, and more inclined to rely on a large scope of information for their decisions. Conclusions: The associations between job- and person-specific factors and HCPs' propensity for IDM may have implications for their training and allocation in the health care system. Education has great potential for enhancing the quality of IDM in clinical practice.

Thinking fast or slow? Functional magnetic resonance imaging reveals stronger connectivity when experienced neurologists diagnose ambiguous cases

For ∼40 years, thinking about reasoning has been dominated by dual-process theories. This model, consisting of two distinct types of human reasoning, one fast and effortless and the other slow and deliberate, has also been applied to medical diagnosis. Medical experts are trained to diagnose patients based on their symptoms. When symptoms are prototypical for a certain diagnosis, practitioners may rely on fast, recognition-based reasoning. However, if they are confronted with ambiguous clinical information slower, analytical reasoning is required. To examine the neural underpinnings of these two hypothesized forms of reasoning, 16 highly experienced clinical neurologists were asked to diagnose two types of medical cases, straightforward and ambiguous cases, while functional magnetic resonance imaging was being recorded. Compared with reading control sentences, diagnosing cases resulted in increased activation in brain areas typically found to be active during reasoning such as the caudate nucleus and frontal and parietal cortical regions. In addition, we found vast increased activity in the cerebellum. Regarding the activation differences between the two types of reasoning, no pronounced differences were observed in terms of regional activation. Notable differences were observed, though, in functional connectivity: cases containing ambiguous information showed stronger connectivity between specific regions in the frontal, parietal and temporal cortex in addition to the cerebellum. Based on these results, we propose that the higher demands in terms of controlled cognitive processing during analytical medical reasoning may be subserved by stronger communication between key regions for detecting and resolving uncertainty.

Sex Differences in Symptom Phenotypes Among Patients With Acute Myocardial Infarction

BACKGROUND

The diagnosis of acute myocardial infarction (AMI) is missed more frequently in young women than men, which may be related to the cognitive psychology of the diagnostic process. Physicians start the diagnostic process by intuitively recognizing familiar symptom phenotypes, but little is known about how symptoms combine in individuals as unique symptom phenotypes. We examined how symptoms of AMI combine as unique symptom phenotypes in individual patients to compare the distribution of symptom phenotypes in women versus men.

METHODS AND RESULTS

The VIRGO study (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients) was a multicenter, observational cohort study of 3501 young adults hospitalized with AMI. Data were collected on presenting symptoms with standardized interviews and from medical record abstraction. The number and distribution of unique symptom phenotypes were compared between women and men. Because of the 2:1 female-to-male enrollment ratio, women and men were compared with permutation testing and repeated subsampling. There were 426 interview-symptom phenotypes in women and 280 in men. The observed difference between women and men of 146 phenotypes was significant, even allowing for the greater enrollment of women (permutation P=0.004, median difference 110 under the null hypothesis of no association between sex and phenotype). The repeated subsample analysis also showed significantly more interview-symptom phenotypes in women than men (206.8±7.3 versus 188.6±6.0, P<0.001). Women were more broadly distributed among symptom phenotype subgroups than men (P<0.001). Similar findings were observed in the analysis of symptoms abstracted from the medical record.

CONCLUSIONS

Women exhibited substantially more variation in unique symptom phenotypes than men, regardless of whether the symptoms were derived from structured interviews or abstracted from the medical record. These findings may provide an explanation for the higher missed diagnosis rate in young women with AMI and may have important implications for teaching and improving clinicians' ability to recognize the diagnosis of AMI in women.

Management Reasoning: Implications for Health Professions Educators and a Research Agenda

Substantial research has illuminated the clinical reasoning processes involved in diagnosis (diagnostic reasoning). Far less is known about the processes entailed in patient management (management reasoning), including decisions about treatment, further testing, follow-up visits, and allocation of limited resources. The authors' purpose is to articulate key differences between diagnostic and management reasoning, implications for health professions education, and areas of needed research.Diagnostic reasoning focuses primarily on classification (i.e., assigning meaningful labels to a pattern of symptoms, signs, and test results). Management reasoning involves negotiation of a plan and ongoing monitoring/adjustment of that plan. A diagnosis can usually be established as correct or incorrect, whereas there are typically multiple reasonable management approaches. Patient preferences, clinician attitudes, clinical contexts, and logistical constraints should not influence diagnosis, whereas management nearly always involves prioritization among such factors. Diagnostic classifications do not necessarily require direct patient interaction, whereas management prioritizations require communication and negotiation. Diagnoses can be defined at a single time point (given enough information), whereas management decisions are expected to evolve over time. Finally, management is typically more complex than diagnosis.Management reasoning may require educational approaches distinct from those used for diagnostic reasoning, including teaching distinct skills (e.g., negotiating with patients, tolerating uncertainty, and monitoring treatment) and developing assessments that account for underlying reasoning processes and multiple acceptable solutions.Areas of needed research include if and how cognitive processes differ for management and diagnostic reasoning, how and when management reasoning abilities develop, and how to support management reasoning in clinical practice.

Teaching Diagnostic Reasoning: A Review of Evidence-Based Interventions

Problem/Background: The ability to accurately diagnose patients based on symptom profiles is a vital yet challenging skill that Nurse Practitioners (NPs) undertake frequently.

PURPOSE

This integrative literature review highlights a variety of evidence based, practical educational strategies that foster the development of diagnostic reasoning.

METHODS

An integrative literature review was conducted in order to identify original research focusing on diagnostic reasoning educational interventions.

RESULTS

Eighteen primary sources met inclusion and exclusion criteria. Results are synthesized in terms of sample and setting, methodological features, interventions, and outcomes. Interventions broadly fit into five educational themes: testing strategies, cognitive biases, simulation programs, course formats, and instructional approaches.

DISCUSSION

Interventions are simple and can be implemented in multiple educational settings. Future research should occur in populations of NP students. Validated, easy-to-use measurement tools as well as more precise diagnostic reasoning concept development should occur.