The Reliability and Validity of a Paediatric Script Concordance Test with Medical Students, Paediatric Residents and Experienced Paediatricians

Background: The Script Concordance (SC) approach was used as an alternative test format to measure the presence of knowledge organization reflective in one’s clinical reasoning skills (i.e., diagnostic, investigation and treatment knowledge).Methods:  The present study investigated the reliability and validity of a 40-item paediatric version of the SC test with three groups representing 53 medical students (novices), 42 paediatric residents (intermediates) and 11 paediatricians (experts).Results:  A comparison between scoring techniques based on experts’ ratings of the items showed internal reliability coefficients from .74 for the one-best answer up to .78 for alternative scoring techniques.  An ANOVA showed an increase in test performance from medical students through to expert paediatricians (F(2,103) = 84.05, p < .001), but did not differentiate between the postgraduate year 1 to 3 paediatric residents.  A large effect size (Cohen’s d) difference of 1.06 was found between medical students and residents total SC test scores.Conclusions:  These results support other findings indicating the SC test format can be used to differentiate between the clinical reasoning skills of novices, intermediates and experts in paediatrics.  An alternative scoring method that includes one best answer and partial marks was also supported for grading SC test items

The neurocognitive gains of diagnostic reasoning training using simulated interactive veterinary cases

The present longitudinal study ascertained training-associated transformations in the neural underpinnings of diagnostic reasoning, using a simulation game named “Equine Virtual Farm” (EVF). Twenty participants underwent structural, EVF/task-based and resting-state MRI and diffusion tensor imaging (DTI) before and after completing their training on diagnosing simulated veterinary cases. Comparing playing veterinarian versus seeing a colorful image across training sessions revealed the transition of brain activity from scientific creativity regions pre-training (left middle frontal and temporal gyrus) to insight problem-solving regions post-training (right cerebellum, middle cingulate and medial superior gyrus and left postcentral gyrus). Further, applying linear mixed-effects modelling on graph centrality metrics revealed the central roles of the creative semantic (inferior frontal, middle frontal and angular gyrus and parahippocampus) and reward systems (orbital gyrus, nucleus accumbens and putamen) in driving pre-training diagnostic reasoning; whereas, regions implicated in inductive reasoning (superior temporal and medial postcentral gyrus and parahippocampus) were the main post-training hubs. Lastly, resting-state and DTI analysis revealed post-training effects within the occipitotemporal semantic processing region. Altogether, these results suggest that simulation-based training transforms diagnostic reasoning in novices from regions implicated in creative semantic processing to regions implicated in improvised rule-based problem-solving

A conceptual treadmill: the need for ‘middle ground’ in clinical decision making theory in nursing

This paper explores the two predominant theoretical approaches to the process of nurse decision making prevalent within the nursing research literature: systematic-positivistic approaches as exemplifed by information processing theory, and the intuitive-humanistic approach of Patricia Benner. The two approaches' strengths and weaknesses are explored and as a result a third theoretical stance is proffered: the idea of a cognitive continuum. According to this approach the systematic and intuitive theoretical camps occupy polar positions at either end of a continuum as opposed to separate theoretical planes. The methodological and professional benefits of adopting such a stance are also briefly outlined

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

The influence of patient and doctor gender on diagnosing coronary heart disease

Using novel methods, this paper explores sources of uncertainty and gender bias in primary care doctors’ diagnostic decision making about coronary heart disease (CHD). Claims about gendered consultation styles and quality of care are re-examined, along with the adequacy of CHD models for women. Randomly selected doctors in the UK and the US (n=112, 56 per country, stratified by gender) were shown standardised videotaped vignettes of actors portraying patients with CHD. ‘Patients’ age, gender, ethnicity and social class were varied systematically. During interviews, doctors gave free-recall accounts of their decision making, which were analysed to determine patient and doctor gender effects. We found differences in male and female doctors’ responses to different types of patient information. Female doctors recall more patient cues overall, particularly about history presentation, and particularly amongst women. Male doctors appear less affected by patient gender but both male and especially female doctors take more account of male patients’ age and consider more age-related disease possibilities for men than women. Findings highlight the need for better integration of knowledge about female presentations within accepted CHD risk models, and do not support the contention that women receive better quality care from female doctors

Structured computer-based training in the interpretation of neuroradiological images

Computer-based systems may be able to address a recognised need throughout the medical profession for a more structured approach to training. We describe a combined training system for neuroradiology, the MR Tutor that differs from previous approaches to computer-assisted training in radiology in that it provides case-based tuition whereby the system and user communicate in terms of a well-founded Image Description Language. The system implements a novel method of visualisation and interaction with a library of fully described cases utilising statistical models of similarity, typicality and disease categorisation of cases. We describe the rationale, knowledge representation and design of the system, and provide a formative evaluation of its usability and effectiveness

The influence of patient's age on clinical decision-making about coronary heart disease in the USA and the UK

This paper examines UK and US primary care doctors' decision-making about older (aged 75 years) and midlife (aged 55 years) patients presenting with coronary heart disease (CHD). Using an analytic approach based on conceptualising clinical decision-making as a classification process, it explores the ways in which doctors' cognitive processes contribute to ageism in health-care at three key decision points during consultations. In each country, 56 randomly selected doctors were shown videotaped vignettes of actors portraying patients with CHD. The patients' ages (55 or 75 years), gender, ethnicity and social class were varied systematically. During the interviews, doctors gave free-recall accounts of their decision-making. The results do not establish that there was substantial ageism in the doctors' decisions, but rather suggest that diagnostic processes pay insufficient attention to the significance of older patients' age and its association with the likelihood of co-morbidity and atypical disease presentations. The doctors also demonstrated more limited use of ‘knowledge structures’ when diagnosing older than midlife patients. With respect to interventions, differences in the national health-care systems rather than patients' age accounted for the differences in doctors' decisions. US doctors were significantly more concerned about the potential for adverse outcomes if important diagnoses were untreated, while UK general practitioners cited greater difficulty in accessing diagnostic tests