A review of clinical decision-making: Models and current research

Aims and objectives: The aim of this paper was to review the current literature with respect to clinical decision-making models and the educational application of models to clinical practice. This was achieved by exploring the function and related research of the three available models of clinical decision making: information processing model, the intuitive-humanist model and the clinical decision making model. Background: Clinical decision-making is a unique process that involves the interplay between knowledge of pre-existing pathological conditions, explicit patient information, nursing care and experiential learning. Historically, two models of clinical decision making are recognised from the literature; the information processing model and the intuitive-humanist model. The usefulness and application of both models has been examined in relation the provision of nursing care and care related outcomes. More recently a third model of clinical decision making has been proposed. This new multidimensional model contains elements of the information processing model but also examines patient specific elements that are necessary for cue and pattern recognition. Design: Literature review Methods: Evaluation of the literature generated from MEDLINE, CINAHL, OVID, PUBMED and EBESCO systems and the Internet from 1980 – November 2005

Information search and information distortion in the diagnosis of an ambiguous presentation

Physicians often encounter diagnostic problems with ambiguous and conflicting features. What are they likely to do in such situations? We presented a diagnostic scenario to 84 family physicians and traced their information gathering, diagnoses and management. The scenario contained an ambiguous feature, while the other features supported either a cardiac or a musculoskeletal diagnosis. Due to the risk of death, the cardiac diagnosis should be considered and managed appropriately. Forty-seven participants (56%) gave only a musculoskeletal diagnosis and 45 of them managed the patient inappropriately (sent him home with painkillers). They elicited less information and spent less time on the scenario than those who diagnosed a cardiac cause. No feedback was provided to participants. Stimulated recall with 52 of the physicians revealed differences in the way that the same information was interpreted as a function of the final diagnosis. The musculoskeletal group denigrated important cues, making them coherent with their representation of a pulled muscle, whilst the cardiac group saw them as evidence for a cardiac problem. Most physicians indicated that they were fairly or very certain about their diagnosis. The observed behaviours can be described as coherencebased reasoning, whereby an emerging judgment influences the evaluation of incoming information, so that confident judgments can be achieved even with ambiguous, uncertain and conflicting information. The role of coherence-based reasoning in medical diagnosis and diagnostic error needs to be systematically examined

‘Do you see what I see?’ Medical imaging: the interpretation of visual information

Röntgen's discovery of x-rays in 1895, gave to medicine the extraordinary benefit of being able to see inside the living body without surgery. Over time, technology has added to the sophistication of imaging processes in medicine and we now have a wide range of techniques at our disposal for the investigation and early detection of disease. But radiology deals with visual information; and like any information this requires interpretation. It is a practical field and medical images are used to make inferences about the state of peoples' health. These inferences are subject to the same variability and error as any decision-making process and so the criteria for the success of medical imaging are based not entirely on the images themselves but on the performance of the decision-makers. Research in the accuracy of medical imaging must draw on techniques from a wide range of disciplines including physics, psychology, computing, neuroscience and medicine in attempting to better understand the processes involved in visual decision-making in this context and to minimise diagnostic error

Neurocognitive profiles in autism spectrum disorder

textThe current research project examines the performance of a group of high functioning young adult males with autism spectrum disorders on standardized measures of neurocognitive functioning to determine whether distinct cognitive profiles of strengths and weaknesses emerge. Neuropsychological test data across various domains: general cognitive ability, visuospatial processing, verbal learning and memory, visual learning and memory, working memory, reasoning, cognitive flexibility, attention, receptive language, expressive language, social and emotional processing, and fine motor skills was examined. Data were analyzed using cluster analysis to assess for the presence and nature of unique clusters/subgroups based on neuropsychological test performance. Three unique clusters were derived from the analyses. This study highlights the well-documented heterogeneity across the spectrum of autism and suggests a method for parsing a heterogeneous sample of ASD subjects into smaller and more meaningful homogeneous groups using standardized neuropsychological assessments.Educational Psycholog

Measuring the quality of judgement and decision-making in nursing

Aim. This paper discusses measurement of the quality of judgement and decision-making in nursing research. It examines theoretical and research issues surrounding how to measure judgement accuracy as a component of evaluating decision-making in nursing practice. Discussion. Judgement accuracy is discussed with reference to different methods of measurement, including comparing judgements with independent criteria and inter-judge approaches. Existing research on how judgement accuracy has been measured in nursing practice is examined. Evaluation of decisions is then discussed, including consideration of the process of decision-making and evaluating decision outcomes. Finally, existing research on decision-making in nursing is assessed and the strengths and limitations of different types of measurement discussed. Conclusion. We suggests that researchers examining the quality of judgement and decision-making in nursing need to be aware of both the strengths and limitations of existing methods of measurement. We also suggest that researchers need to use a number of different methods, including normative approaches such as Bayes' Theorem and Subjective Expected Utility Theory

Artificial Intelligence and Patient-Centered Decision-Making

Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, calculations, and procedures cannot be meaningfully understood by human practitioners. When AI systems reach this level of complexity, we can also speak of black-box medicine. In this paper, we want to argue that black-box medicine conflicts with core ideals of patient-centered medicine. In particular, we claim, black-box medicine is not conducive for supporting informed decision-making based on shared information, shared deliberation, and shared mind between practitioner and patient