Online Program Overview
Artificial Intelligence (AI) and cognitive computing are projected to empower patients, transform the practice of medicine and save the health care industry over $150 billion by 2025.1
It is estimated that if implemented correctly, AI could improve health outcomes by up to 40 percent and reduce treatment costs up to 50 percent by improving diagnosis, increasing access to care and enabling precision medicine.2
There is no denying that AI is the future of health care, however AI technologies won’t implement themselves and require considerable translational expertise to deliver on their promise. Although health care professionals have firsthand experience with health and organizational issues, they typically do not have a detailed understanding of the AI technology needed to address them. Many health care executives know implementing AI will improve their organization and keep them competitive, but find the technology and scale of data intimidating. On the other hand, data scientists and technology professionals are not familiar with the intricacies in health care that will inform the development of AI in this field.
This program is everything you want to know about AI in health care, but are afraid to ask. For health care professionals, it will help you to think like a data scientist. For technology professionals, you will learn the nuances of health care that are central to effectively developing AI. This course will bridge the two parties, opening the communication and knowledge between health care leaders and data scientists.
You will learn from the leaders in health care AI, including prominent Harvard faculty and industry experts at many of the world’s top technology companies. Course faculty will use group discussions, active learning strategies, case studies, and master classes to explore such topics as AI creation, potential implementation challenges, business models for AI in health care, and the future of the field over the next 5 years. Additionally this course is designed to encourage networking among participants, fostering a long-term support system you can lean on after the program concludes.
During this program you will learn:
- What you need to know to understand how AI can support your organization’s strategy and serve your patients
- State of the art in AI from business leaders and practitioners
- Scope of applications of AI across all elements of health care: payer, provider, life sciences, consumer health, and genomics
- How to build a business case for AI in health care
- How to consider issues of equity to ensure that the promise of AI is realized for all patients
Solving Health Care Challenges with AI
There is a colossal amount of data being collected in the health care world – and much of it has not been fully utilized to best support care due to its complexity and scale. With the growing role of AI in health care organizations, it can be used to harness this data to help clinicians and leaders make expedient, informed, and personalized decisions when treating patients. The health care field is just starting to understand the depth and range of improvements that AI can make – and this is only the beginning of its incredible impact on improving the public’s health.
There are many ways AI is helping overcome long-standing health care challenges:
- Diagnosis: AI is able to process complex images, like CT scans, along with health records to make an accurate diagnosis in near real-time. A 2019 study found that AI correctly diagnosed diseases 87% of the time when reviewing medical imaging, compared to 86% by health care professionals.3 By combining the AI skill set with that of clinicians, the rate of misdiagnoses goes down, also helping reduce physician overload and in turn improving productivity.
- Precision Medicine: AI has played a substantial role in the emerging field of precision medicine, which defies the one-size-fits-all approach to health care. Precision medicine is heavily based in data, taking into consideration a patient’s behaviors, environment, genome, and medical history to develop a more personalized treatment plan. AI helps manage the massive data sets used to inform this approach, allowing clinicians to better understand the patient, provide more specialized care, and more efficiently target resources. This has ultimately been proven to better treat disease and improve patient care.
- Prediction Models: By using prediction models, clinicians can identify how a patient compares to others with a similar diagnosis, helping calculate potential outcomes. For example, it can help when determining if a patient is at higher risk of death, may need extra support to prevent complications, or can be released from the hospital shortly.
However, evidence also shows AI is also involved with important risks such as algorithmic bias. As individuals who develop AI carry implicit bias and health care systems exist in societies with prejudice, these biases end up being reflected in algorithms. It is crucial to think proactively about bias when developing and implementing AI by taking strategic actions to minimize the risk of algorithmic bias to ensure AI is helping – not further harming – the communities it serves.