Results for MED 219

The practice of data science is inherently a collaborative endeavor, and this course aims to equip data scientists and aspiring data scientists with the necessary skills for effective teamwork in clinical and translational research. The principles discussed in the course apply not only to the medical field but also to the behavioral and social sciences. Throughout the course, participants will explore various aspects of team engagement that are critical for the entire translational research process from study design to data management to data analysis to dissemination of findings. Key questions addressed include how to identify the required expertise for a team, how to integrate and onboard data scientists into teams, how to engage collaborators effectively by successfully leveraging multiple disciplines to jointly solve problems, how to educate the team on the role of the data scientist, as well as the data scientist's role in data collection, cleaning, and management. The course also delves into issues that impact rigor and reproducibility such as authorship, reasonable timelines, interpreting empirical findings, and the importance of statistical analysis plans and study registration. Material is taught through lectures, simulated role-playing exercises, and real-time demonstrations to enhance learning and practical application. Data scientists are working more and more as part of scientific teams. In this course, participants who are (or who are training to be) data scientists will learn optimal team science tools for engaging clinical and translational investigators in the collaborative research process. These principles apply across the medical, behavioral, and social sciences.Topic areas include: optimal team make up from a data science perspective; how to engage collaborators on study design; how to educate collaborators on engaging data scientists; how to educate collaborators on rigor and reproducibility principles such as creating a statistical analysis plan, pre-registering studies, and deciding on authorship; elements that comprise the ideal statistical analysis plan; how to play an integral role during data collection and data extraction phases of the study; and optimal approaches for dissemination of findings to the team and to the research community that adhere to rigor and reproducibility principles and that ensure integration of the data scientist?s voice. In addition to lectures, materials will be taught using simulated role playing and real-time demonstrations of collaborations.

Analytic methods for determining optimal diagnostic and therapeutic decisions with applications to the care of individual patients and the design of policies applied to patient populations. Topics include: utility theory and probability modeling, empirical methods for disease prevalence estimation, probability models for periodic processes, binary decision-making techniques, Markov models of dynamic disease state problems, utility assessment techniques, parametric utility models, utility models for multidimensional outcomes, analysis of time-varying clinical outcomes, and the design of cost-constrained clinical policies. Extensive problem sets compliment the lectures. Prerequisites: introduction to calculus and basic statistics.

This course will provide students with an overview of current technologies related to the development of small molecules and neutralizing antibodies. Delivered via classroom instruction and a workshop, these modules will aim to increase the fundamental understanding of drug development terminology and processes, combined with real-world examples of discovery therapeutics and technologies developed for translation to clinic. Emphasis will be made on the development of small molecules that can cross the blood-brain barrier and show potential for translation in clinic. Focus will also be put on the screening of antibody targets followed by target validation, target profile development and preclinical evaluation. The workshop will focus on computational molecular docking which is an excellent tool that will help reduce the attrition rate during the drug development process and lead identification studies. Active engagement of the students during the workshop is expected based on the need to install specific software and completion of assigned tasks (computational docking). Students are expected to bring personal laptops to class on day 1 and 2 to perform docking exercises.

This class will be a guided tour into regeneration biology, with an emphasis on fundamental developmental processes. Instead of focusing on what we know, the goal of this course is for students to trace how we know, and how we should ask questions for the future. In my opinion, the most important scientific problems are often left unresolved not for lack of adequate information, but for lack of insights to specify the questions that require explanation. Therefore, in this class, we will work together to search for important questions in the area, by reconstructing historical and controversial ideas, dissecting classic literature, formulating our own questions, and debating to test our answers. This class is a tour, as there is no intention for it to be comprehensive; students will be treated as my future colleagues and provided by a taste of science ? you should progress in your own way, at your own pace that matches your ambition in learning. Therefore, I expect the class to be interactive and even provocative, and the students to be willing to read beyond the class as active reading is essential to succeed in this course.

New technologies designed to monitor and improve health outcomes are constantly emerging, but most fail in the clinic and in the marketplace because relatively few are supported by reliable, reproducible evidence that they produce a health benefit. This course covers the designs and methods that should be used to evaluate technologies to diagnose patients, predict prognosis or other health events, or screen for disease. These technologies can include devices, statistical prediction rules, biomarkers, gene panels, algorithms, imaging, or any information used to predict a future or a previously unknown health state. Specific topics to be covered include the phases of test development, how to frame a proper evaluation question, measures of test accuracy, Bayes theorem, internal and external validation, prediction evaluation criteria, decision analysis, net-utility, ROC curves, c-statistics, net reclassification index, decision curves and reporting standards. Examples of technology assessments and original methods papers are used. Knowledge of statistical software is not required, although facility with at least Excel for basic calculations is needed. Open to students with an understanding of introductory biostatistics, epidemiologic and clinical research study design. Undergraduates may enroll with consent of instructor.

Current Issues in Genetics is an in-house seminar series that meets each Academic Quarter for one hour per week (Friday, 4:00-5:00) and features talks by Genetics Department faculty, students, and postdoctoral fellows (with occasional visiting speakers). Thus, over the year, it provides a comprehensive overview of the work going on in the Department. First-year Ph.D. students in Genetics are required to enroll during all four Quarters, and students from other programs may be permitted to enroll with prior permission of the instructors.

Students completing this course will walk away with a profound understanding of how to foster sustainable community partnerships. Through a combination of classroom instruction and service learning, students will develop an understanding of the complex causes and consequences of housing inequity, community-based case management, and community-driven solutions to the housing crisis. The course will emphasize the importance of centering the lived experiences of people who are unhoused and strategies for connecting them with essential resources that improve their health/well-being. Ultimately, the insight and experience students gain from the course will empower them to be a catalyst for housing equity in any corner of the world. This is a Cardinal Course certified by the Haas Center for Public Service.

How might we design a product or service that helps each person to navigate the system to meet their health needs? This course aims to blend the best methods of co-design with key insights from digital product design and foundational AI to create novel solutions to population-health challenges, working alongside people from communities that have been historically underserved. Responsive to real-world challenges from a healthcare partner, students will work with patients with chronic illness (and their caregivers) to co-design solutions that reimagine the future of primary care. To understand these challenges, we will explore the intersections among epidemiology of chronic illness, fiscal and policy constraints, and social well-being. We will place a special emphasis on the practical challenge of delivering the right care, at the right place and time. Stakeholders will include clinicians, community experts, technologists and payers. Students will work in teams to design, prototype and test concepts that reflect the worlds in which their co-design partners live and work, all with an eye to helping our healthcare system work better for them.

Will teaching be an important part of your professional career? What knowledge and skills are necessary to become an outstanding medical educator? This seminar will use interactive and small group instruction to review core principles of medical education. Students will explore learning theory, bedside and clinical teaching techniques, feedback, curriculum design, assessment, education research methods, technology and career paths in medical education.

This seminar is intended for students who are interested in a career in health professions education. Completion of 'Introduction to Medical Education' (SOMGEN219, Winter Q) is recommended but not required. We will use didactic and small group instruction to examine several advanced topics in medical education: individualized learning, competency-based assessment, coaching in medical education, applied learning theory, disseminating educational scholarship, and the creation of digital learning resources. We hope that this course will inspire students to enter academic careers that include teaching as a central tenet of their life's work.