In its second year, DELTA provided funding for seven projects, which were selected from a highly competitive field of 43 proposals. Each of the funded projects will deploy innovative uses of digital technology to enhance the university’s teaching and learning enterprise for a wide variety of populations, including Johns Hopkins graduate students, undergraduates, faculty, and patients.
Principal investigators: Fasika A. Woreta and Ingrid E. Zimmer-Galler of the School of Medicine.
Although telemedicine is regarded as a tool to improve patient access to specialty healthcare, little has been done to evaluate its role in medical education. Considering that teleophthalmology can serve as an important educational tool and with expansion of telemedicine in the field of medicine, it is important for physicians and trainees to become familiar with this practice model. Early on in their medical education, it can be challenging for physicians-in-training to make clinical diagnoses both accurately and efficiently. These trainees are often responsible for much of the call and triaging at academic medical centers. At the Johns Hopkins Hospital, the overall length of stay in the Emergency Department (ED) for eye emergencies where the first-year ophthalmology resident takes call is over 9 hours. By introducing telemedicine into the education of residents in the ED, we hope to improve resident education, increase the accuracy of diagnoses in the ED by trainees, reduce ED lengths of stay, and increase patient satisfaction. A successful model of the integration of telemedicine in medical education could subsequently be applied throughout the Johns Hopkins Health System Corporation as well as in other teaching hospitals in the country. This project addresses several of the university’s 10X20 goals, with a particular emphasis on investing in programs that support our core academic mission of teaching and patient-centered care as well as faculty-led interdisciplinary collaboration and innovative cross-cutting initiatives.
Principal investigators: Mindi Levin of SOURCE, and Gundula Bosch and R. Tyler Derreth of the School of Public Health.
This project relies on the combined expertise of SOURCE as service-learning professionals, Center for Teaching and Learning as online instructional designers, and the R3 Graduate Science Initiative as experts in critically rigorous research methodology. We recognize the need to involve online students in civic engagement practices, and service-learning has proven highly effective. However, there remains no widely accepted or effective online service-learning pedagogy. The project will examine how to fully integrate service-learning pedagogy into an online course environment. The first goal of the project is to develop an innovative pedagogical framework that is theoretically sound, practically feasible, and leverages the advanced civic and critical thinking outcomes of service-learning with the broad access of online instructional design. Our second goal is to develop a pilot online service-learning course in the “Evidence- Based Teaching and Learning” series of the JHSPH R3 program. The course will have students work with Baltimore community-based organizations (CBOs) to develop evaluation plans and materials for the organizations’ identified program. The team will collect data from the course and students to evaluate the efficacy of the innovative pedagogical framework, and then we will make revisions to ensure we develop a sustainable and widely applicable method of online teaching that can be effective for JHU and higher education in future years.
Principal investigator: John Muschelli of the School of Public Health
The main medium of online learning is video lectures. However, for instructors creating video lectures takes time and requires video software skills and resources. Also, they are largely written for English-speaking audiences. We propose to set up a workflow that will allow instructors to take a set of slides, the words to be spoken on those slides, and automatically combine them into a video with the words spoken over top of the slides. We use open-source tools such as ffmpeg and R and paid services like the Amazon Polly text-to-speech API. Using other tools, such as the Google Translate API, and complementing it with human annotation, we will convert these videos into multiple different languages, each with language-specific subtitles. This allows for a set of videos to be created, each with a trackable version, while removing the need to re-record lectures. Our proposal includes: 1) an easy way for instructors to convert slides with notes to video lectures and update them, 2) converting the lectures into multiple languages, and 3) evaluating the student experience and efficacy of these lectures.
Principal investigators: Satyanarayana Vedula, Anand Malpani, Gregory Hager, and Mathias Unberath of the School of Engineering; and Brian Caffo of the School of Public Health
Significance: Machine learning and artificial intelligence (ML & AI) techniques are rapidly becoming a staple in healthcare research, but both healthcare and engineering workforces at Johns Hopkins lag behind in their capacity to advance innovation through interdisciplinary collaboration and to assimilate it into practice. Specifically, healthcare scientists need to understand machine learning algorithms, and engineers need to learn biases in healthcare data that affect performance and utility of algorithms. Addressing these learning needs is critical to foster interdisciplinary collaboration at Johns Hopkins and to enable the School of Medicine to shape the future of healthcare innovations through engineering (Goals 2 and 3 in JHU’s 10 by 20 priorities). Our goal is to develop an online course to equip clinicians and engineers with critical thinking skills to design, analyze, interpret, and report research on ML & AI in healthcare. The primary target population for this course, for purposes of this grant, is clinical care providers, faculties, and graduate trainees (residents, clinical & research post-doctoral fellows, doctoral students, and research staff) in the School of Medicine and the Whiting School of Engineering at Johns Hopkins. While we target a specific population for this grant, we have secured preliminary approval from the Engineering for Professionals Program at JHU for MOOC development of our course, which can enhance global visibility and impact of JHU as a leader in driving healthcare innovation using technology.
Innovation and Approach: To our knowledge, none of the courses or curricula at Johns Hopkins address the comprehensive learning needs to build capacity in ML & AI in healthcare. The proposed course integrates principles from engineering and statistical sciences with two complementary parts. The first part is focused upon essential concepts of state-of-the-art machine learning methods. The second part is focused upon core concepts of design, bias, evaluation, and reporting in studies on engineering in healthcare. This course brings together educators with engineering, statistical, epidemiological, and clinical experience.
Evaluation and Expected Outcome: In individual learners, we will evaluate skill acquisition using pre- and post-tests focused on the learning objectives of the course. To quantify impact on the University’s 10 by 20 priorities, we will survey Johns Hopkins personnel completing the course to enumerate downstream effects such as incident journal club sessions they lead within their divisions, new research studies, and collaborations focused upon ML & AI in healthcare.
Principal investigator: Daniel Rhee of the School of Medicine.
Minimally invasive surgery (MIS) is an essential aspect of surgical care and a core aspect of general surgery training. Graduating surgical trainees reach sophisticated levels of MIS performance in adult surgery; however, opportunities for developing MIS excellence in neonates and infants are limited. MIS training in neonates and infants present unique challenges due to the complex nature of the operations, patient size, confined working spaces, and delicate nature of their tissues. Developing proficiency in neonatal MIS requires repeated deliberate practice and there are no adequate resources available for MIS training in this population.
We propose to develop a model of a neonate through 3D printing technology and modify it to simulate the major index operations in neonatal MIS. We hypothesize that a model that emulates the anatomy and the technical steps of these operations can significantly improve surgical trainee education and proficiency in neonatal surgery and ultimately improve patient safety. This can be accomplished through the following specific aims: 1) To develop a neonatal MIS model trainer using 3D printing that best simulates the configuration, anatomy, and primary maneuvers of neonatal congenital surgery. 2) To develop a curriculum based on the 3D model for surgical trainees to develop proficiency in MIS in complex surgery in neonates and infants.
Through collaboration between the Department of Surgery and Art as Applied to Medicine, a prototype model has already been built and successfully tested, proving that simulation of neonatal MIS techniques is feasible. Expansion of the model prototype’s current design will focus on increasing the breadth of simulated operations, simplifying its set up, and increasing its durability for repeated use. Development of the curriculum will emphasize technical skills as well as critical operative decision making. Anticipated outcomes include improvement in resident proficiency in completing simulated exercises, improved resident involvement in patient operations, and improved patient safety outcomes. Evaluation and assessment will focus on how the 3D model emulates the correct surgery, curriculum-associated improvements in surgical trainee proficiency, and improvements in patient safety through simulation.
Principal investigators: Graham Mooney of the School of Medicine and Keilah A. Jacques of the School of Public Health.
Health disparities and health equity are not system level alone, they are the result of the ways oppression, power dynamics, and bias exist as embedded elements of teaching and learning for health professionals. Teaching practices have gone largely unchanged for more than a century. While there have been pedagogical counter-methods that introduce power and bias within curricula (Freire, 2005), these practices remain on the fringes of pedagogical work. Currently, there are few comprehensive pedagogical frameworks centering injustice orientation or designed with a bent towards anti-oppression framing (Agosto et.al., 2019). Similarly, there is an emerging need for comprehensive teaching methods for faculty that support the incorporation of multiple teaching modalities to model anti-oppressive approaches to teaching and learning (Clifford et.al., 2005; Mattsson, 2014; Metzl, & Petty, 2017). The purpose of utilizing a social justice and anti-oppressive pedagogical framework is to create efforts to address personal and structural forms of domination and subordination while guiding faculty in making clear connections for students among structural forms of oppression, learning environment, and the power and privilege they will navigate as practitioners. The value of developing this approach in a blended online and in-person learning environment is 1) to cater to the individual needs of the learner and unique learning styles; 2) to architect innovation that can support confident integration of topics in the classroom; and 3) to guide participants through deliberate steps to develop individual learning practices necessary to advance structural competence on issues of injustice. Gamified simulation increases information retention rates (Baines & Edwards, 2015), and can strengthen the relationship between instructional content and long-term learning outcomes (Soboleva et. al., 2018). Considering this, we propose to build a technology-oriented teaching framework that facilitates a social justice pedagogy approach through using the example of hyper-segregation in Baltimore City as a foundation to understand how systematic oppression impacts health equity.
Principal investigators: Jeanne Sheffield, Meghan McMahon, and Anja Frost of the School of Medicine
The Obstetric Triage Web Application will be an easily accessible web app aimed at patients, but also available for learners and providers to address common obstetric triage concerns and to provide first line recommendations and education. Obstetric triage is an emergency department on Labor and Delivery aimed specifically to address the needs of pregnant and postpartum patients. Common chief complaints include contractions, abdominal pain, decreased fetal movement, vaginal bleeding, and many others. According to the American College of Obstetrics and Gynecology (ACOG), the obstetric triage volume exceeds the overall birth volume of a hospital by 20-50%, and another large study showed only 36% of patients present to triage because they believe there is a true emergency. With these large volumes of non-emergent complaints, patients, providers, and the healthcare system suffer. Patients can experience long wait times, high triage bills, and dissatisfaction. Providers have increased patient demands and time away from more critical patient needs. Ultimately this results in increased health care visits and costs. Our obstetric online web app would address the most common obstetric triage scenarios to provide home remedies, expert recommendations, and resources and education to help alleviate this healthcare system burden. This will be a novel extension of digital learning for patients and providers in the obstetric population who frequently access applications and technology for health information, which has been an increasingly popular trend amongst all fields of medicine in the last decade. While multiple various obstetric applications exist, this would be the first to offer more individualized and stratified recommendations for complaints and concerns during and after pregnancy and offer a significant concurrent educational component.