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Clinical reasoning is a complex cognitive process that is essential to evaluate and manage a patient's medical problem. The aim of this paper was to provide a critical review of the research literature on clinical reasoning theories and models. To conduct our study, we applied the process of conducting a literature review in four stages in accordance with the approach of Carnwell and Daly. First, we defined the scope of the review as being limited to clinical reasoning theories and models in medical education. In the second stage, we conducted a search based on related words in PubMed, Google Scholar, PsycINFO, ERIC, ScienceDirect and Web of Science databases. In the third stage, we classified the results of the review into three categories, and in the fourth stage, we concluded and informed further studies. Based on the inclusion and exclusion criteria, 31 articles were eligible to be reviewed. Three theories and two models were recognized and classified into three categories. Several theories and models have been proposed in relation to clinical reasoning, but it seems that these theories and models could only explain part of this complex process and not the whole process. Therefore, to fulfill this gap, it may be helpful to build a Meta-model or Meta-theory, which unified all the models, and theories of clinical reasoning.

Background Since December 2019, the novel coronavirus disease (COVID-19) has rapidly spread around the world leading to a pandemic with more than 3,000,000 infected people and more than 200,000 death. Several case definitions have been released and revised by countries and organizations. However, collectivization of case definitions has not been fully investigated.Methods In this study, we rapidly reviewed existing COVID-19 case definitions, finally a dynamic case definition algorithm was provided by using Bayesian theorem models of diagnosis.Results Our results showed categorization as suspected, probable, and confirmed cases, is used in majority of case definitions. Furthermore, the criteria for suspected cases and laboratory testing priority was a point of argument. Due to pandemic situation and resource limitation, diagnostic tests were rationed and mainly administered to a selected population, thus it was shown that the fraction of positive test results does not reflect the total infection rate of the population. Case definitions for COVID-19 is changing as we learn more about the disease. RT-PCR and CT Scan of lung seem to be beneficial in COVID-19 diagnosis and combing them with epidemiological criteria helps us in better understanding of the disease.Conclusion Based on our results, in the current case definitions, only symptomatic patients categorized and tested as a susceptible case. While the majority of COVID-19 cases are asymptomatic carriers of the disease, thus making the prevention more challenging. Dynamic statistical models can provide new insights into surveillance systems.

Background: Clinical reasoning plays an important role in the ability of physicians to make diagnoses and decisions. It is considered the physician’s most critical competence, but it is an ambiguous conceptin medicine that needs a clear analysis and definition. Our aim was to clarify the concept of clinical reasoning in medicine by identifying its components and to differentiate it from other similar concepts.It is necessary to have an operational definition of clinical reasoning, and its components must be precisely defined in order to design successful interventions and use it easily in future research.Methods: McKenna’s nine-step model was applied to facilitate the clarification of the concept of clinical reasoning. The literature for this concept analysis was retrieved from several databases, including Scopus, Elsevier, PubMed, ISI, ISC, Medline, and Google Scholar, for the years 1995– 2016 (until September 2016). An extensive search of the literature was conducted using the electronic database. Accordingly, 17 articles and one book were selected for the review. We applied McKenna’s method of concept analysis in studying clinical reasoning, so that definitional attributes, antecedents, and consequences of this concept were extracted.Results: Clinical reasoning has nine major attributes in medicine. These attributes include: (1) clinical reasoning as a cognitive process; (2) knowledge acquisition and application of different types of knowledge; (3) thinking as a part of the clinical reasoning process; (4) patient inputs; (5) contextdependent and domain-specific processes; (6) iterative and complex processes; (7) multi-modal cognitive processes; (8) professional principles; and (9) health system mandates. These attributes are influenced by the antecedents of workplace context, practice frames of reference, practice models of the practitioner, and clinical skills. The consequences of clinical reasoning are the metacognitive improvement of reasoning and tacit knowledge production.Conclusion: The present concept analysis tries to clarify the concept of clinical reasoning in medicine and reduces the ambiguity of this concept to design successful interventions and use it easily in future research.Keywords: CLINICAL REASONING, MEDICINE, CONCEPT ANALYSIS, MCKENNA’S MODEL