Otolaryngology for Internal Medicine: Increasing Exposure to Otolaryngology Using Computer Assisted Instruction

It has been well identified in previous literature that there is a lack of otolaryngology teaching for general practitioners. There is not currently a standardized curriculum for otolaryngology in undergraduate medical education or during residency for those pursuing a general field such as Emergency Medicine, Internal Medicine, Pediatrics or Family Medicine. While the need for more exposure to otolaryngology within these fields has been well documented, as 25% of primary care complaints are otolaryngology related, little has been done to identify the best method for educational intervention. Important topics for inclusion in such a curriculum have been identified and methods of teaching (online learning modules, case-based group learning sessions, physical exam skills, simulation activities, etc) have been proposed. This study will expand on prior research by surveying internal medicine residents and otolaryngology residency program directors for their opinions on how to incorporate a curriculum for otolaryngology. Residency program directors were chosen due to their experience with the nature, volume and content of referrals from primary care. Internal medicine residents were chosen due to their unique perspective on how to deliver a subspecialty curriculum within the scope of their current training. This data will further inform the ideal setting and format for an otolaryngology curriculum and identify how to incorporate it into primary care training. Improving education, and therefore confidence, in management of common otolaryngic conditions amongst general practitioners will ultimately enhance patient care

Moving towards accurate and early prediction of language delay with network science and machine learning approaches

Due to wide variability of typical language development, it has been historically difficult to distinguish typical and delayed trajectories of early language growth. Improving our understanding of factors that signal language disorder and delay has the potential to improve the lives of the millions with developmental language disorder (DLD). We develop predictive models of low language (LL) outcomes by analyzing parental report measures of early language skill using machine learning and network science approaches. We harmonized two longitudinal datasets including demographic and standardized measures of early language skills (the MacArthur-Bates Communicative Developmental Inventories; MBCDI) as well as a later measure of LL. MBCDI data was used to calculate several graph-theoretic measures of lexico-semantic structure in toddlers’ expressive vocabularies. We use machine-learning techniques to construct predictive models with these datasets to identify toddlers who will have later LL outcomes at preschool and school-age. This approach yielded robust and reliable predictions of later LL outcome with classification accuracies in single datasets exceeding 90%. Generalization performance between different datasets was modest due to differences in outcome ages and diagnostic measures. Grammatical and lexico-semantic measures ranked highly in predictive classification, highlighting promising avenues for early screening and delineating the roots of language disorders

The CDI in two longitudinal datasets: Methods and differences across decades

The MacArthur-Bates Communicative Development Inventories (CDI) are widely used, parent-report instruments of language acquisition. Here, we focus on the word-inventory sections of the instruments, and show two different approaches to modeling CDI data, based on real-world need. First, we show that Words & Gestures data collected out-of-range can be robustly adjusted to Words & Sentences Scores. Second, we demonstrate a novel application of Gompertz growth curves to longitudinal CDI data, especially when the same timepoints were not collected between individuals (i.e., an accelerated longitudinal design). Gompertz curves provide a “growth rate” parameter that can be used to summarize vocabulary development. We compare these parameters between healthy developing children in two longitudinal cohorts, as well as a cohort of children with developmental diagnoses, who we show to have lower growth rates. We hope these analyses and results inform future work

Using Computer-Assisted Instruction to Increase Otolaryngology Education During Medical School.

Introduction A quarter of all complaints seen in adult primary care and half of all complaints seen in pediatric primary care are otolaryngology related. Even though half of all medical students enter primary care fields, there is no standardized curriculum for otolaryngology during medical school. Due to increasing limitations on specialty teaching during general medical education, computer-assisted instruction has been suggested as a format for increasing exposure to otolaryngology. Methods We designed a computer-based learning module for teaching high-yield otolaryngology topics for third- and fourth-year medical students during their primary care clerkship at our institution from 2016–2018. We evaluated students’ prior otolaryngology knowledge with 11 case-based, multiple-choice questions and then evaluated the efficacy of the module by a similar posttest. Results Three-hundred and sixty-five students completed the module. The average pre- and posttest scores were 44% (SD = 21%) and 70% (SD = 17%), respectively, showing that the module resulted in significantly increased scores (p < .01). Discussion The improvement of test scores indicates that this module was an effective educational intervention at our institution for increasing exposure and improving otolaryngology knowledge in third- and fourth-year medical students. As medical schools shift toward adult learning principles such as independent and self-directed learning, computer-assisted instruction is an alternative to classroom-based didactics. Creating resources for independent study will allow more time for otolaryngology faculty and residents to teach clinical exam skills and interactive case-based discussions, which are less suitable to teach via computer-assisted instruction

Using Computer-Assisted Instruction to Increase Otolaryngology Education During Medical School

Introduction A quarter of all complaints seen in adult primary care and half of all complaints seen in pediatric primary care are otolaryngology related. Even though half of all medical students enter primary care fields, there is no standardized curriculum for otolaryngology during medical school. Due to increasing limitations on specialty teaching during general medical education, computer-assisted instruction has been suggested as a format for increasing exposure to otolaryngology. Methods We designed a computer-based learning module for teaching high-yield otolaryngology topics for third- and fourth-year medical students during their primary care clerkship at our institution from 2016–2018. We evaluated students’ prior otolaryngology knowledge with 11 case-based, multiple-choice questions and then evaluated the efficacy of the module by a similar posttest. Results Three-hundred and sixty-five students completed the module. The average pre- and posttest scores were 44% (SD = 21%) and 70% (SD = 17%), respectively, showing that the module resulted in significantly increased scores (p < .01). Discussion The improvement of test scores indicates that this module was an effective educational intervention at our institution for increasing exposure and improving otolaryngology knowledge in third- and fourth-year medical students. As medical schools shift toward adult learning principles such as independent and self-directed learning, computer-assisted instruction is an alternative to classroom-based didactics. Creating resources for independent study will allow more time for otolaryngology faculty and residents to teach clinical exam skills and interactive case-based discussions, which are less suitable to teach via computer-assisted instruction