HST.936 Health Disparities Research and Data Science for Disaster Resilience

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Course Information

Contact: hst936faculty@mit.edu

Course Directors:

Course Faculty:

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Meeting Times and Location:

Lectures: Fridays 9:30 AM - 11:30 AM ET
Lab: Fridays 11:30 AM - 12:30 PM ET
Room E25-117

Key Dates:

  • Start Date: February 4th, 2022
  • Spring Break: Week of March 25th, 2022
  • Final Presentations: May 6th, 2022

Background

HST.936 focuses on studying innovations in information systems to accelerate improvements of health outcomes. It is increasingly clear that progress in the field of biomedical research and global health technology requires a coordinated interdisciplinary approach. This can best be accomplished by convening multidisciplinary teams to develop innovations that are thoughtfully combined to yield solutions greater than the sum of their individual parts.

In this iteration of our course, we will explore health disparities research in the US and abroad, while exploring data science for disaster resilience. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how digital data sources can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient. This course is a collaborative offering from Sana, MIT Critical Data, Harvard School of Public Health, and Harvard Medical School, featuring speakers who are internationally recognized experts in the field.

Course Overview

This project-based course will allow students to gain real-world project experience by engaging in active healthcare technology projects at various stages of development. Our goals are to foster collaboration and facilitate the opportunity to showcase exciting innovative projects around the world. To this end, much of the in-class time will be dedicated to project workshops with our partners, while the bulk of the lecture material will be covered using an asynchronous learning model through our online course hosted by MIT’s Open Learning Library platform.

Learning Objectives

Understand health care gaps and inefficiencies in resource-poor settings Learn about strategies to improve and measure quality of care using information systems Apply machine learning and analytics techniques to actual health data projects to improve surveillance or processes and deliver solutions Develop project management and coordination skills on a multidisciplinary, cross-functional team Course Curriculum The bulk of the required didactic materials will be followed concurrently online via HST.936x on MIT’s Open Learning Library platform. In-class time will be devoted to topical seminars from field experts as well as hands-on guided workshops focused on practical aspects of various global health projects.

All students are required to complete the online course concurrently with the in-person course: https://openlearninglibrary.mit.edu/courses/course-v1:MITx+HST.936x+1T2019/aboutLinks to an external site.

Grading

Online Coursework: Online readings and section quizzes (from HST.936x) are worth a total of 20% of your grade. Three Problem Sets: Problem sets 1, 2, and 3 are each worth 10% of your grade. This means problem sets are worth a total of 30% of your grade. Course Final Project: The written proposal is worth 5% of your grade; the final project presentation is worth 20% of your grade; and the final project write up is worth 25%.

Project-Based Learning

Project-based activities will compose the bulk of the in-class time and are centered on developing designs or analytics for real-world implementation in global environments. Students will gain first-hand experience in identifying the global health challenges and how information systems can be used to overcome them. The course is led by multidisciplinary faculty with backgrounds in engineering, business, public health, and medicine, with real-world experience implementing health information systems around the world.

Each group will be paired with project mentors and/or members of the Sana Clinical Operations team. Teams will work together with global partners from healthcare organizations and universities in developing countries. The primary mentor for each of the projects will be an expert familiar with the clinical question and the local environment. Students will work to identify health-related problems that are the result of information gaps, and they will then design, pilot, evaluate, and scale an information system to address these problems.

At the end of the term, students will present their final projects and submit a completed deliverable. Deliverables will vary depending on the scope and stage of the projects and will range from market analyses to engineering prototypes to clinical evaluations. We expect some deliverables to progress to journal publication or competition. Most projects will continue to subsequent stages through the summer or the following semester, and students may be invited to continue on with their participation pending project lead approval.

Project Deliverables

  • Milestone 1: Written proposal (1 page)
  • Milestone 2: Mid-semester Presentation
  • Final Project: Presentation (May 6th – 10min, 5min Q&A) and Final Report (8+ pages – i.e. 3000+ words excluding figures)

Lab Hours

We have reserved lab time in the afternoons to provide you the opportunity to meet in your project groups or web conference with international partners. We will have a mix of course staff and mentors available to answer questions or give advice on your work. Professionalism and Expectations As a student and emerging leader in healthcare and technology, it is expected that you will arrive on time to class and contribute at a high level. Given that the “flipped classroom” model of this course, it is mandatory that students complete the online material in addition to class attendance.

In keeping with any academic work, references and data sources should be cited rigorously. Such data sources may include both literature and personal interviews. Since our mission is to make a global impact through sharing knowledge and best practices, an ancillary objective for the final project will be publication in a journal.

Projects and Authorship

A note on collaboration: Research is a collaborative activity and we encourage all students to collaborate and learn from each other. In general, when you put your name on something for research, you must: a) have materially contributed to the work, b) be able to defend the research, and c) acknowledge the contribution of others. Keep this in mind when working together and submitting material for evaluation.

A note on authorship: As noted, the expectation is that, by the end of the course, the final project will be sufficiently developed to submit to a peer-reviewed journal. The author order can be a somewhat controversial issue and is left to the project participants to decide. We would strongly encourage you to discuss what the order will be, or what philosophy you will use to decide the order while forming groups. In the case of a dispute during or after the course, the instructors will likely not be able to mediate in any meaningful way.

We would also recommend equal authorship (now more common), but the decision is left to each team.

For the clinicians: If you expect a certain level of authorship (first, last, etc.) you should mention this in your project pitch. Keep in mind that this is a two-way street involving both clinicians and data scientists. If a project fails to garner enough interest, it may not be able to be completed as part of the course.

A note on acknowledgement: Papers that result from work done during this course should recognize the contributions of the course in an acknowledgement or in other sections. The suggested language is: “This manuscript was composed by participants in the HST.936 course at the Massachusetts Institute of Technology, Spring 2022.”