Health Informatics Core

Explore how digital interventions are being employed to drive clinical decisions and offer value to healthcare organizations, their patients and their staffs. Digital health is a broad term that encompasses use of digital devices and platforms, including electronic health records (EHRs), patient-provider portals, mobile health (mHealth) applications and wearable biosensors to improve process and outcomes.

The course begins by focusing on the revolution in remote patient monitoring made possible by ubiquitous broadband networks and wide penetration of smartphones. (Over 80% of the U.S. population now owns a smartphone.)

In addition, it is now possible to supplement patient reported outcomes (PROs) with additional data from remote monitoring, such as from wearable biosensors. Specialized, medical-grade sensors are increasingly approved by the Food and Drug Administration and are useful to monitor physiologic data, from glucose levels to brain function to medication adherence.

The class will also cover the burgeoning ecosystem of mobile health apps, including patient-facing, provider-facing and patient-provider smartphone apps. We will review best practices for mHealth app development and review example of apps that worked—and didn't.

Students will learn how to develop, test and scale apps for patients and providers. We will also review issues surround data security, data storage and data sharing using mHealth applications, and discuss their role within the domain of consumer health informatics.

Students will also learn to apply design thinking methodology to solve vexing health challenges. Design thinking is a structured process to support innovation that is ideal for designing digital health solutions that are feasible and impactful because of the focus on the intended user population.

The class will next explore electronic health records (EHRs), including patient-provider portals. The class will review the different EHR architectures, benefits of cloud-based vs. fixed EHR systems, and ways to leverage the EHR to improve the value of care.

We will then examine technologies gaining traction in digital health, including telemedicine, virtual-reality interventions, and social media, among others. We consider these examples within a framework for making smarter decisions in the age of digital health—a model that brings together what the clinician knows, what the patient wants and what the technologies predict.

In all cases we will explore real-life case studies at Cedars-Sinai and beyond, learning from practitioners in the field using digital health in the clinical trenches.

The goals of this course are to provide an understanding of what AI is and the variety of ways AI is used in medicine. The course will cover the historical uses of AI, both inside and outside of healthcare, and the successes, failures and increased awareness of AI through the popularity of ChatGPT. This course will evaluate how AI is perceived and used by providers and different patient demographics, AI’s complex ethical and moral considerations, its legal implications, and the role of AI in a variety of healthcare settings such as in radiology, dermatology, mental health, and surgery. Student individual work will evaluate the current and future status of AI within a specific area of medicine.

An important but under-considered aspect of AI in medicine is trust, and to better understand it we will study how patients, providers, and other stakeholders perceive and understand AI. To further understand perceptions of AI, we will look at portrayals of AI in the media influencing both fear and excitement, and the financial side of AI from the perspectives of established AI companies and start-ups. Finally, we will consider the opportunities for AI to solve vexing challenges in healthcare, the innovation taking place, and what the future of AI might hold given the rapid evolution.