Tailored opening questions to the context of using medical questionnaires: Qualitative analysis in first-visit consultations.

Background: Communication skills required for doctors do not consist of simple uses of particular linguistic forms but include uses that are sensitive to the interactional context. In consultations where the doctors have pre-existing information about their patients, this can complicate the context of problem solicitation. We investigated how doctors tailor opening questions to a context in which they get pre-existing information from a medical questionnaire (MQ) filled out by the patients. Methods: The data for this study were 87 video recordings of first visits to the department of general medicine at a university hospital in Japan. We qualitatively analyzed doctors' practices in problem solicitation in an opening phase using conversation analysis and triangulated it with quantitative analysis. Results: Open-ended questions accounted for 26.4% of opening questions. Among the closed-ended questions, 75.0% were confirming questions about symptoms. In cases with open-ended questions, doctors minimized the relevance of the MQ to problem solicitation by giving license to repeat the description from the MQ. In cases with closed-ended questions, doctors highlighted the relevance of the MQ by sharing the MQ. Through these practices, they avoided patients' possible confusion about problem presentation while simultaneously maximizing the possibility of soliciting the patients' narratives. Conclusions: Doctors adjusted the level of relevance of pre-existing information to problem solicitation through both verbal and nonverbal management of the MQ. It will be useful to instruct such context-dependent practices to improve communication skills in medical school curriculum.

Deep Neural Network-Based Method for Detecting Obstructive Meibomian Gland Dysfunction With in Vivo Laser Confocal Microscopy.

PURPOSE: To evaluate the ability of deep learning (DL) models to detect obstructive meibomian gland dysfunction (MGD) using in vivo laser confocal microscopy images. METHODS: For this study, we included 137 images from 137 individuals with obstructive MGD (mean age, 49.9 ± 17.7 years; 44 men and 93 women) and 84 images from 84 individuals with normal meibomian glands (mean age, 53.3 ± 19.6 years; 29 men and 55 women). We constructed and trained 9 different network structures and used single and ensemble DL models and calculated the area under the curve, sensitivity, and specificity to compare the diagnostic abilities of the DL. RESULTS: For the single DL model (the highest model; DenseNet-201), the area under the curve, sensitivity, and specificity for diagnosing obstructive MGD were 0.966%, 94.2%, and 82.1%, respectively, and for the ensemble DL model (the highest ensemble model; VGG16, DenseNet-169, DenseNet-201, and InceptionV3), 0.981%, 92.1%, and 98.8%, respectively. CONCLUSIONS: Our network combining DL and in vivo laser confocal microscopy learned to differentiate between images of healthy meibomian glands and images of obstructive MGD with a high level of accuracy that may allow for automatic obstructive MGD diagnoses in patients in the future.