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Evidence in clinical reasoning: a computational linguistics analysis of 789,712 medical case summaries 1983-2012.

ABSTRACT: Better understanding of clinical reasoning could reduce diagnostic error linked to 8% of adverse medical events and 30% of malpractice cases. To a greater extent than the evidence-based movement, the clinical reasoning literature asserts the importance of practitioner intuition—unconscious elements of diagnostic reasoning. The study aimed to analyse the content of case report summaries in ways that explored the importance of an evidence concept, not only in relation to research literature but also intuition.The study sample comprised all 789,712 abstracts in English for case reports contained in the database PUBMED for the period 1 January 1983 to 31 December 2012. It was hypothesised that, if evidence and intuition concepts were viewed by these clinical authors as essential to understanding their case reports, they would be more likely to be found in the abstracts. Computational linguistics software was used in 1) concept mapping of 21,631,481 instances of 201 concepts, and 2) specific concept analyses examining 200 paired co-occurrences for 'evidence' and research 'literature' concepts.'Evidence' is a fundamentally patient-centred, intuitive concept linked to less common concepts about underlying processes, suspected disease mechanisms and diagnostic hunches. In contrast, the use of research literature in clinical reasoning is linked to more common reasoning concepts about specific knowledge and descriptions or presenting features of cases. 'Literature' is by far the most dominant concept, increasing in relevance since 2003, with an overall relevance of 13% versus 5% for 'evidence' which has remained static.The fact that the least present types of reasoning concepts relate to diagnostic hunches to do with underlying processes, such as what is suspected, raises questions about whether intuitive practitioner evidence-making, found in a constellation of dynamic, process concepts, has become less important. The study adds support to the existing corpus of research on clinical reasoning, by suggesting that intuition involves a complex constellation of concepts important to how the construct of evidence is understood. The list of concepts the study generated offers a basis for reflection on the nature of evidence in diagnostic reasoning and the importance of intuition to that reasoning.

SUBMITTER: Seidel BM

PROVIDER: S-EPMC4381480 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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Evidence in clinical reasoning: a computational linguistics analysis of 789,712 medical case summaries 1983-2012.

Seidel Bastian M BM Campbell Steven S Bell Erica E

BMC medical informatics and decision making 20150321

<h4>Background</h4>Better understanding of clinical reasoning could reduce diagnostic error linked to 8% of adverse medical events and 30% of malpractice cases. To a greater extent than the evidence-based movement, the clinical reasoning literature asserts the importance of practitioner intuition—unconscious elements of diagnostic reasoning. The study aimed to analyse the content of case report summaries in ways that explored the importance of an evidence concept, not only in relation to researc ...[more]

PMID: 25880840

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Project description:PurposeDeveloping medical students' clinical reasoning requires a structured longitudinal curriculum with frequent targeted assessment and feedback. Performance-based assessments, which have the strongest validity evidence, are currently not feasible for this purpose because they are time-intensive to score. This study explored the potential of using machine learning technologies to score one such assessment-the diagnostic justification essay.MethodFrom May to September 2018, machine scoring algorithms were trained to score a sample of 700 diagnostic justification essays written by 414 third-year medical students from the Southern Illinois University School of Medicine classes of 2012-2017. The algorithms applied semantically based natural language processing metrics (e.g., coherence, readability) to assess essay quality on 4 criteria (differential diagnosis, recognition and use of findings, workup, and thought process); the scores for these criteria were summed to create overall scores. Three sources of validity evidence (response process, internal structure, and association with other variables) were examined.ResultsMachine scores correlated more strongly with faculty ratings than faculty ratings did with each other (machine: .28-.53, faculty: .13-.33) and were less case-specific. Machine scores and faculty ratings were similarly correlated with medical knowledge, clinical cognition, and prior diagnostic justification. Machine scores were more strongly associated with clinical communication than were faculty ratings (.43 vs .31).ConclusionsMachine learning technologies may be useful for assessing medical students' long-form written clinical reasoning. Semantically based machine scoring may capture the communicative aspects of clinical reasoning better than faculty ratings, offering the potential for automated assessment that generalizes to the workplace. These results underscore the potential of machine scoring to capture an aspect of clinical reasoning performance that is difficult to assess with traditional analytic scoring methods. Additional research should investigate machine scoring generalizability and examine its acceptability to trainees and educators.

Project description:OBJECTIVE:Fostering clinical reasoning is a mainstay of medical education. Based on the clinicopathological conferences, we propose a case-based peer teaching approach called clinical case discussions (CCDs) to promote the respective skills in medical students. This study compares the effectiveness of different CCD formats with varying degrees of social interaction in fostering clinical reasoning. DESIGN, SETTING, PARTICIPANTS:A single-centre randomised controlled trial with a parallel design was conducted at a German university. Study participants (N=106) were stratified and tested regarding their clinical reasoning skills right after CCD participation and 2 weeks later. INTERVENTION:Participants worked within a live discussion group (Live-CCD), a group watching recordings of the live discussions (Video-CCD) or a group working with printed cases (Paper-Cases). The presentation of case information followed an admission-discussion-summary sequence. PRIMARY AND SECONDARY OUTCOME MEASURES:Clinical reasoning skills were measured with a knowledge application test addressing the students' conceptual, strategic and conditional knowledge. Additionally, subjective learning outcomes were assessed. RESULTS:With respect to learning outcomes, the Live-CCD group displayed the best results, followed by Video-CCD and Paper-Cases, F(2,87)=27.07, p<0.001, partial η2=0.384. No difference was found between Live-CCD and Video-CCD groups in the delayed post-test; however, both outperformed the Paper-Cases group, F(2,87)=30.91, p<0.001, partial η2=0.415. Regarding subjective learning outcomes, the Live-CCD received significantly better ratings than the other formats, F(2,85)=13.16, p<0.001, partial η2=0.236. CONCLUSIONS:This study demonstrates that the CCD approach is an effective and sustainable clinical reasoning teaching resource for medical students. Subjective learning outcomes underline the importance of learner (inter)activity in the acquisition of clinical reasoning skills in the context of case-based learning. Higher efficacy of more interactive formats can be attributed to positive effects of collaborative learning. Future research should investigate how the Live-CCD format can further be improved and how video-based CCDs can be enhanced through instructional support.

Project description:Background: Clinical reasoning refers to a thinking process including medical problem solving and medical decision making skills. Several studies have shown that the clinical reasoning process can be influenced by a number of factors, e.g. context or personality traits, and the results of this thinking process are expressed in case presentation. The aim of this study was to identify factors, which predict the quality of case summary statements as an indicator of clinical reasoning of undergraduate medical students in an assessment simulating the first day of residency. Methods: To investigate factors predicting aspects of clinical reasoning 67 advanced undergraduate medical students participated in the role of a beginning resident in our competence-based assessment, which included a consultation hour, a patient management phase, and a handover. Participants filled out a Post Encounter Form (PEF) to document their case summary statements and other aspects of clinical reasoning. After each phase, they filled out the Strain Perception Questionnaire (STRAIPER) to measure their situation dependent mental strain. To assess medical knowledge the participants completed a 100 questions multiple choice test. To measure stress resistance, adherence to procedures, and teamwork students took part in the Group Assessment of Performance (GAP) test for flight school applicants. These factors were included in a multiple linear regression analysis. Results: Medical knowledge and teamwork predicted the quality of case summary statements as an indicator of clinical reasoning of undergraduate medical students and explained approximately 20.3% of the variance. Neither age, gender, undergraduate curriculum, academic advancement nor high school grade point average of the medical students of our sample had an effect on their clinical reasoning skills. Conclusion: The quality of case summary statements as an indicator of clinical reasoning can be predicted in undergraduate medical students by their medical knowledge and teamwork. Students should be supported in developing abilities to work in a team and to acquire long term knowledge for good case summary statements as an important aspect of clinical reasoning.

Dataset Information

Evidence in clinical reasoning: a computational linguistics analysis of 789,712 medical case summaries 1983-2012.

Publications

Evidence in clinical reasoning: a computational linguistics analysis of 789,712 medical case summaries 1983-2012.

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