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Alain F Junod

Publications and source records attributed to Alain F Junod.

18 recordsLinked to original sources
PubMed2006

Live or computerized simulation of clinical encounters: do clinicians work up patient cases differently?

Computer simulation of clinical encounters is increasingly used in clinical settings to train patient work-up. The aim of this prospective, controlled study was to compare the characteristics of data collection and diagnostic exploration of physicians working up cases with a standardized patient and in a computerized simulation. Six clinicians of different clinical experience in internal medicine worked up three cases with a standardized patient and through a computer simulation allowing free inquiry. After each encounter, we asked the subjects to justify the information collected and to comment on their working diagnoses. The characteristics of data collected and working diagnoses generated were assessed and compared, according to the simulation method used. In the computer simulation, physicians limited their data collection and focused earlier and more specifically on information and working diagnoses with high levels of relevance. They reached a similar diagnostic accuracy and made decisions of a similar relevance. Computer simulation with a free-inquiry approach reproduces the data collection and the diagnostic exploration observed in a standardized-patient simulation and promotes an early collection of relevant data. Its contribution to extend the competence of learners in clinical settings should be further evaluated.

Clinical Competence↗
PubMed2006

Brief report: beyond clinical experience: features of data collection and interpretation that contribute to diagnostic accuracy.

BACKGROUND: Clinical experience, features of data collection process, or both, affect diagnostic accuracy, but their respective role is unclear. OBJECTIVE, DESIGN: Prospective, observational study, to determine the respective contribution of clinical experience and data collection features to diagnostic accuracy. METHODS: Six Internists, 6 second year internal medicine residents, and 6 senior medical students worked up the same 7 cases with a standardized patient. Each encounter was audiotaped and immediately assessed by the subjects who indicated the reasons underlying their data collection. We analyzed the encounters according to diagnostic accuracy, information collected, organ systems explored, diagnoses evaluated, and final decisions made, and we determined predictors of diagnostic accuracy by logistic regression models. RESULTS: Several features significantly predicted diagnostic accuracy after correction for clinical experience: early exploration of correct diagnosis (odds ratio [OR] 24.35) or of relevant diagnostic hypotheses (OR 2.22) to frame clinical data collection, larger number of diagnostic hypotheses evaluated (OR 1.08), and collection of relevant clinical data (OR 1.19). CONCLUSION: Some features of data collection and interpretation are related to diagnostic accuracy beyond clinical experience and should be explicitly included in clinical training and modeled by clinical teachers. Thoroughness in data collection should not be considered a privileged way to diagnostic success.

Clinical Competence↗
PubMed2005

Common strategies in clinical data collection displayed by experienced clinician-teachers in internal medicine.

The role model displayed by clinician-teachers influences learning experiences but learners may face various reasoning styles. Our goal was to describe common strategies in clinical data collection displayed by experienced clinician-teachers in internal medicine. We studied six internists heavily involved in teaching while they were working up the same seven cases portrayed by a standardized patient. Each encounter was audio-recorded and replayed to allow the subjects commenting on the purpose and diagnostic hypotheses considered for each piece of information collected. Information and hypotheses elicited by all physicians were considered key items. Although the subjects reached the same final diagnoses, they differed on several characteristics of their data collection process. They also displayed common behaviours, such as: early acquisition of key data (half of them acquired within the first 19 questions asked) through clarification of the patients' complaints and focused data collection; early generation of the final diagnosis (within the first 10 questions asked) and use of diagnostic hypotheses to frame data collection; and summarization of the information at hand during the encounter (at least twice). Whether making teachers explicitly conscious about their own reasoning processes may help them better model and explain their diagnostic approach to specific cases should be assessed in follow-up studies.

Adolescent↗
PubMed2004

Degree of concurrency among experts in data collection and diagnostic hypothesis generation during clinical encounters.

BACKGROUND: Given that there are variations in clinicians' reasoning, methods to elaborate scoring checklists for standardised patient-based assessment need to be valid. The use of data elicited by experts solving problems independently has been advocated as a method of setting performance standards. AIMS: To determine the degree of concurrence and common characteristics among items independently elicited by doctors during patient encounters and to assess the number of experts needed to derive reliable performance standards. METHODS: Six experienced internists worked-up the same 7 chief complaints with standardised patients (SPs). A stimulated recall of the recorded encounter was then performed. The degree of concurrence of the collected history and physical examination information and the generated diagnostic hypotheses was computed. Reliability was derived from generalisability analyses. RESULTS: By case, experts elicited a mean of 114 information items (SD = 15) and generated 30 diagnostic hypotheses (SD = 6). A high concurrence (80-100%) was observed for a mean of 22 information items (20%; SD = 6) and 7 diagnostic hypotheses (24%; SD = 2). More than a third of the 153 highly concurrent information items were clarification questions. At least 3 doctors were needed to obtain a reliability of 0.80 or higher when deriving the scoring checklists. CONCLUSION: The limited concurrency in data elicited by clinicians during a patient encounter supports the use of high-fidelity methods to develop performance checklists used in SP-based assessment. It also suggests that relying only on information collected to assess clinical competence may not be sufficient. Additional criteria, such as structure and style of work-up, should be further explored.

Clinical Competence↗
PubMed2002

Will there be room for the teaching of internal medicine in a university hospital?

To answer the question addressed, two working groups, one made of the staff of a University clinic, the other one composed of practising general internists, have discussed the assets and weaknesses of a University service of Internal Medicine for postgraduate training. The groups agreed on a number of points: patients' characteristics (complexity and co-morbidities), quality of teaching, method acquisition for clinical reasoning, as well as absence of exposure to ambulatory patients and of follow-up. The groups differed in their views related to the lack of training in psychiatry and psychosocial problems or to hospital dysfunctions. Opening of internal medicine to primary care appears to be necessary at the same time as individual qualities among the senior staff are to be developed, such as critical analysis and self-questioning.

Ambulatory Care↗