Winter Quarter – Academic Year 2024/2025

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Students are required to apply for the course; applications are currently open at the link below. The application deadline is on November 30, end of day**.**

**https://stanforduniversity.qualtrics.com/jfe/form/SV_4UR5BP81e4vFvEi**

We will announce enrollment decisions & share enrollment codes on a rolling basis and, at the latest, by December 2, end of day. Early applications are encouraged, we might run out of enrollment codes before November 30. Enrollment is limited to current Stanford students.

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Overview

| **Instructors

Project Coaches** | Oliver Aalami, MD Carlos Guestrin, PhD Vishnu Ravi, MD Paul Schmiedmayer, PhD Aydin Zahedivash, MD Adrit Rao Nick Riedman … ⚠️ We are looking for project coaches with Swift & SwiftUI experience. Please reach out to [email protected] to learn more about the opportunity & compensation. | | --- | --- | | Contact | [email protected] | | Units | 3-4 units | | Day/Time | Tuesdays and Thursdays from 4:30 PM - 6:00 PM | | Location | TBD In person attendance is required! Zoom is offered as a backup for reasonable exceptions. Link to Zoom found in Canvas! | | Course Material | Canvas & GitHub | | Course Application | https://stanforduniversity.qualtrics.com/jfe/form/SV_4UR5BP81e4vFvEi |

As our world becomes more and more digitized, patients and their devices are generating streams of valuable data that can provide meaningful clinical insights. This digital health revolution provides great opportunities to design and validate new digital health concepts. Many groups within Stanford Medicine have promising ideas that are ripe for development, however, they lack the software engineering and healthcare compliance know-how to take them forward.

Building for Digital Health is a Biodesign course sponsored by the Stanford School of Medicine (SoM) and Stanford’s Computer Science (CS) department. Its goal is to provide CS students with the opportunity to apply their skills to real-world health technology development projects, while enabling SoM faculty to leverage these talented individuals to help advance their technology concepts toward patients. Both audiences will learn a repeatable approach for developing new digital health technologies and preparing to launch them in the market.

Over the course of ten weeks, students and faculty will work together to tackle a project and launch an app-enabled solution for research use. Every week, students will learn about app-development, sensor technologies, privacy, security, and more. In the final week of class, teams will present their final project (app) to a panel of digital health experts.

• Table of Content

Goals of the Course

• Gain exposure to unmet needs in the healthcare field and learn to design and build secure digital solutions to address them
• Get experience working with technical and multidisciplinary teams with the help of external mentors and industry experts who can help guide career paths in health tech innovation

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• Become familiar with the iOS platform, the Swift programming language, the use of Stanford Spezi and other open-source frameworks, and secure data collection practices

Topics Covered

Mobile Application Development (iOS, watchOS, visionOS)

Learn about what makes the mobile operating systems as powerful platforms to develop clinical apps. Overview of open source frameworks leverage Apple technologies to accelerate medical research.

Open Source + Using GitHub

Become familiar with using GitHub to manage software development projects. Learn how to contribute to open-source projects, collaborate on software, automatically test, and build high-quality systems.

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Stanford Spezi

Spezi's ecosystem of modules designed for digital health applications makes it easy to build your own app including questionnaires, data collection from wearable devices, and integration with electronic health record systems. Spezi apps use HL7 FHIR as their native data standard for interoperability. Spezi is free and open-source under the MIT license.

AI & Large Language Models

Machine learning, generative AI such as large language models, and time series data analysis are crucial for modern digital health applications. You will learn how to execute ML models locally on a device to ensure privacy and security and how to use patience-centric AI to support digital health innovations.

Using Apple Technologies and Frameworks