Summary

Big data and artificial intelligence (AI) fueled technologies have evolved to leverage massive datasets and predictive analytics to suit different industries needs and interests. In this module we will examine how these technology trends have impacted the healthcare sector and patient care. 

Electronic Health Records (EHRs) are a necessary part of medical care in the 21st century. These sophisticated information system technologies spur massive production of discrete data, which then fuel AI implementations to support medical decision-making. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. All of these medical IS applications raise a host of social and political issues related to justice, equity, diversity, and inequality of the current U.S. medical and public health system that in turn impact technical aspects of designing AI based health data systems such as massive dataset quality control, algorithmic fairness and the potential for inherent biases, and medical system access and trust. There are also a variety of privacy concerns with AI-systems based on how the training and testing datasets are constructed and evaluated related to HIPAA data protections.