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Uncovering the relation between clinical reasoning and diagnostic accuracy - An analysis of learner's clinical reasoning processes in virtual patients.

ABSTRACT: BACKGROUND:Clinical reasoning is an important topic in healthcare training, assessment, and research. Virtual patients (VPs) are a safe environment to teach, assess and perform research on clinical reasoning and diagnostic accuracy. Our aim was to explore the details of the clinical reasoning process and diagnostic accuracy of undergraduate medical students when working with VPs using a concept mapping tool. METHODS:Over seven months we provided access to 67 German and 30 English VPs combined with a concept mapping tool to visualize and measure the clinical reasoning process of identifying problems, differential diagnoses, recommended tests and treatment options, and composing a summary statement about a VP. A final diagnosis had to be submitted by the learners in order to conclude the VP scenario. Learners were allowed multiple attempts or could request the correct diagnosis from the system. RESULTS:We analyzed 1,393 completed concept maps from 317 learners. We found significant differences between maps with a correct final diagnosis on one or multiple attempts and maps in which learners gave up and requested the solution from the system. These maps had lower scores, fewer summary statements, and fewer problems, differential diagnoses, tests, and treatments. CONCLUSIONS:The different use patterns and scores between learners who had the correct final diagnosis on one or multiple attempts and those who gave up, indicate that diagnostic accuracy in the form of a correct final diagnosis on the first attempt has to be reconsidered as a sole indicator for clinical reasoning competency. For the training, assessment, and research of clinical reasoning we suggest focusing more on the details of the process to reach a correct diagnosis, rather than whether it was made in the first attempt.

SUBMITTER: Hege I

PROVIDER: S-EPMC6171878 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Uncovering the relation between clinical reasoning and diagnostic accuracy - An analysis of learner's clinical reasoning processes in virtual patients.

Hege Inga I Kononowicz Andrzej A AA Kiesewetter Jan J Foster-Johnson Lynn L

PloS one 20181004 10

<h4>Background</h4>Clinical reasoning is an important topic in healthcare training, assessment, and research. Virtual patients (VPs) are a safe environment to teach, assess and perform research on clinical reasoning and diagnostic accuracy. Our aim was to explore the details of the clinical reasoning process and diagnostic accuracy of undergraduate medical students when working with VPs using a concept mapping tool.<h4>Methods</h4>Over seven months we provided access to 67 German and 30 English ...[more]

PMID: 30286136

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Project description:BackgroundUsing malpractice claims cases as vignettes is a promising approach for improving clinical reasoning education (CRE), as malpractice claims can provide a variety of content- and context-rich examples. However, the effect on learning of adding information about a malpractice claim, which may evoke a deeper emotional response, is not yet clear. This study examined whether knowing that a diagnostic error resulted in a malpractice claim affects diagnostic accuracy and self-reported confidence in the diagnosis of future cases. Moreover, suitability of using erroneous cases with and without a malpractice claim for CRE, as judged by participants, was evaluated.MethodsIn the first session of this two-phased, within-subjects experiment, 81 first-year residents of general practice (GP) were exposed to both erroneous cases with (M) and erroneous cases without (NM) malpractice claim information, derived from a malpractice claims database. Participants rated suitability of the cases for CRE on a five-point Likert scale. In the second session, one week later, participants solved four different cases with the same diagnoses. Diagnostic accuracy was measured with three questions, scored on a 0-1 scale: (1) What is your next step? (2) What is your differential diagnosis? (3) What is your most probable diagnosis and what is your level of certainty on this? Both subjective suitability and diagnostic accuracy scores were compared between the versions (M and NM) using repeated measures ANOVA.ResultsThere were no differences in diagnostic accuracy parameters (M vs. NM next step: 0.79 vs. 0.77, p = 0.505; differential diagnosis 0.68 vs. 0.75, p = 0.072; most probable diagnosis 0.52 vs. 0.57, p = 0.216) and self-reported confidence (53.7% vs. 55.8% p = 0.390) of diagnoses previously seen with or without malpractice claim information. Subjective suitability- and complexity scores for the two versions were similar (suitability: 3.68 vs. 3.84, p = 0.568; complexity 3.71 vs. 3.88, p = 0.218) and significantly increased for higher education levels for both versions.ConclusionThe similar diagnostic accuracy rates between cases studied with or without malpractice claim information suggests both versions are equally effective for CRE in GP training. Residents judged both case versions to be similarly suitable for CRE; both were considered more suitable for advanced than for novice learners.

Project description:BackgroundVirtual patient simulators (VPSs) log all users' actions, thereby enabling the creation of a multidimensional representation of students' medical knowledge. This representation can be used to create metrics providing teachers with valuable learning information.ObjectiveThe aim of this study is to describe the metrics we developed to analyze the clinical diagnostic reasoning of medical students, provide examples of their application, and preliminarily validate these metrics on a class of undergraduate medical students. The metrics are computed from the data obtained through a novel VPS embedding natural language processing techniques.MethodsA total of 2 clinical case simulations (tests) were created to test our metrics. During each simulation, the students' step-by-step actions were logged into the program database for offline analysis. The students' performance was divided into seven dimensions: the identification of relevant information in the given clinical scenario, history taking, physical examination, medical test ordering, diagnostic hypothesis setting, binary analysis fulfillment, and final diagnosis setting. Sensitivity (percentage of relevant information found) and precision (percentage of correct actions performed) metrics were computed for each issue and combined into a harmonic mean (F1), thereby obtaining a single score evaluating the students' performance. The 7 metrics were further grouped to reflect the students' capability to collect and to analyze information to obtain an overall performance score. A methodological score was computed based on the discordance between the diagnostic pathway followed by students and the reference one previously defined by the teacher. In total, 25 students attending the fifth year of the School of Medicine at Humanitas University underwent test 1, which simulated a patient with dyspnea. Test 2 dealt with abdominal pain and was attended by 36 students on a different day. For validation, we assessed the Spearman rank correlation between the performance on these scores and the score obtained by each student in the hematology curricular examination.ResultsThe mean overall scores were consistent between test 1 (mean 0.59, SD 0.05) and test 2 (mean 0.54, SD 0.12). For each student, the overall performance was achieved through a different contribution in collecting and analyzing information. Methodological scores highlighted discordances between the reference diagnostic pattern previously set by the teacher and the one pursued by the student. No significant correlation was found between the VPS scores and hematology examination scores.ConclusionsDifferent components of the students' diagnostic process may be disentangled and quantified by appropriate metrics applied to students' actions recorded while addressing a virtual case. Such an approach may help teachers provide students with individualized feedback aimed at filling competence drawbacks and methodological inconsistencies. There was no correlation between the hematology curricular examination score and any of the proposed scores as these scores address different aspects of students' medical knowledge.

Project description:BackgroundImproving clinical reasoning skills-the thought processes used by clinicians to formulate appropriate questions and diagnoses-is essential for reducing missed diagnostic opportunities. The electronic Clinical Reasoning Educational Simulation Tool (eCREST) was developed to improve the clinical reasoning of future physicians. A feasibility trial demonstrated acceptability and potential impacts; however, the processes by which students gathered data were unknown.ObjectiveThis study aims to identify the data gathering patterns of final year medical students while using eCREST and how eCREST influences the patterns.MethodsA mixed methods design was used. A trial of eCREST across 3 UK medical schools (N=148) measured the potential effects of eCREST on data gathering. A qualitative think-aloud and semistructured interview study with 16 medical students from one medical school identified 3 data gathering strategies: Thorough, Focused, and Succinct. Some had no strategy. Reanalysis of the trial data identified the prevalence of data gathering patterns and compared patterns between the intervention and control groups. Patterns were identified based on 2 variables that were measured in a patient case 1 month after the intervention: the proportion of Essential information students identified and the proportion of irrelevant information gathered (Relevant). Those who scored in the top 3 quartiles for Essential but in the lowest quartile for Relevant displayed a Thorough pattern. Those who scored in the top 3 quartiles for Relevant but in the lowest quartile for Essential displayed a Succinct pattern. Those who scored in the top 3 quartiles on both variables displayed a Focused pattern. Those whose scores were in the lowest quartile on both variables displayed a Nonspecific pattern.ResultsThe trial results indicated that students in the intervention group were more thorough than those in the control groups when gathering data. The qualitative data identified data gathering strategies and the mechanisms by which eCREST influenced data gathering. Students reported that eCREST promoted thoroughness by prompting them to continuously reflect and allowing them to practice managing uncertainty. However, some found eCREST to be less useful, and they randomly gathered information. Reanalysis of the trial data revealed that the intervention group was significantly more likely to display a Thorough data gathering pattern than controls (21/78, 27% vs 6/70, 9%) and less likely to display a Succinct pattern (13/78, 17% vs 20/70, 29%; χ23=9.9; P=.02). Other patterns were similar across groups.ConclusionsQualitative data suggested that students applied a range of data gathering strategies while using eCREST and that eCREST encouraged thoroughness by continuously prompting the students to reflect and manage their uncertainty. Trial data suggested that eCREST led students to demonstrate more Thorough data gathering patterns. Virtual patients that encourage thoroughness could help future physicians avoid missed diagnostic opportunities and enhance the delivery of clinical reasoning teaching.

Dataset Information

Uncovering the relation between clinical reasoning and diagnostic accuracy - An analysis of learner's clinical reasoning processes in virtual patients.

Publications

Uncovering the relation between clinical reasoning and diagnostic accuracy - An analysis of learner's clinical reasoning processes in virtual patients.

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