Active Learning: An Integrative Learning Approach for Adult Learners

In order to appeal to adult learners, the Analytics in Medicine course utilizes active learning methodology to foster self-directed learning, critical thinking, communication skills, and acquisition of knowledge. The modified flipped classroom model requires weekly assigned reading and a written reflection exercise completed several days before face-to-face class time. The reflections are used to better inform course instructors regarding areas needing further explanation in person. In the weekly 2-hour class, students explore topics in-depth by incorporating active learning methods. Examples include think-pair-share or small group activities requiring movement, discussion, and reflection focusing on question prompts or application of principles (e.g., carousel method). The instructor continuously circulates to ensure student engagement and grasp of the material, allowing for areas of clarification to be immediately identified. Instructors often involve peers to coach one another as a method of continued active learning without going into “lecture mode.”https://digitalscholarship.unlv.edu/btp_expo/1092/thumbnail.jp

The impact of interdisciplinary code simulation on perceptions of collaboration and team performance among internal medicine residents and nursing students

• Allows for inter-disciplinary training• Provides safe environment to practice patient care with immediate feedback-quality improvement• Results in better adherence to protocols• Well received by learners• In one study, almost half of IM residents surveyed felt ill- equipped to lead code teams even after ACLS training Crisis Resource Management (CRM) • Communication and cooperation• Leadership and management• Situational awareness• Decision-makin

Cancer Survivorship in Hematologic Malignancies: Lifestyle Changes After Diagnosis

© 2020 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. Background: Studies show that patients make lifestyle changes soon after certain solid tumor diagnoses, suggesting that this may be a teachable moment to motivate and promote healthy behaviors. There is a paucity of data regarding changes made after a diagnosis of a hematologic malignancy. Methods: A cross-sectional study of 116 patients at a community oncology center who completed anonymous questionnaires was performed. Questions addressed lifestyle choices made with respect to smoking, alcohol consumption, recreational drug use, diet, and exercise habits before and after diagnosis of a hematologic malignancy. Support systems utilized, including psychiatry services, were also assessed. Results: Patients exhibited significant reduction in smoking behavior (Χ2 = 31.0, p \u3c 0.001). 82.4% (n = 14) of one pack per day smokers quit between the time periods, with nearly all smokers showing a reduction after diagnosis. Alcohol use overall did not change significantly, however, 10.3% (n = 12) of patients reported quitting drinking completely between time periods. Changes in dietary intake and exercise were not statistically significant overall. Utilization of external support systems correlated with improved diet as well as decrease in total smoking years. Conclusions: This study demonstrates that patients exhibited significant lifestyle changes after being diagnosed with a hematologic malignancy. Clinicians should take advantage of this ‘teachable moment’ to educate patients about positive health behavior changes. Advances in cancer therapeutics have led to an increase in cancer survivors, this education is crucial in reducing the risk of developing chronic comorbidities as well as secondary malignancies

Using macros in microsoft excel to facilitate cleaning of research data

Background: Retrospective chart review studies may be delayed by inability to export clean clinical data from an electronic medical record (EMR) or data repository. Macros are pre-programmed procedures that can be used in Microsoft Excel to help streamline the process of cleaning clinical datasets. Objectives: To demonstrate how macros may be useful for researchers at community hospitals and smaller academic health centers that lack informatics support. Methods: Using an intrinsic function of our institution’s EMR, vital signs and lab results from 20 individual hospitalizations were exported to a spreadsheet. Two macros were developed to sort through these datasets and output them into a specified format. The speed of macro-assisted data cleaning was compared to manual transcription. Results: Time spent on data cleaning was significantly reduced when using macro-assisted sorting compared to the manual approach for both vital signs (46.5 seconds versus 12.3 minutes per record, a 94% reduction; P < 0.001) and labs (13.7 seconds versus 2.6 minutes per record, a 91% reduction; P < 0.001). Conclusions:Macros offer a flexible and efficient tool for cleaning large sets of clinical data, particularly when an institution lacks informatics support or EMR functionality to export clinical data in an analysis-ready format