Table 6.

Electronic Health Records

Best practices	Description
Use of the largest and best curated electronic EHR to develop AI/ML algorithms	EHR-based optimization of AI/ML algorithms for each application that takes into consideration the number and types of elements to maximize their generalizability.
The data represent the whole population for each application	Consideration to differences between centers in the accuracy and frequency of data collection, varying modalities, and clinical actions helps to avoid exacerbating disparities.
Development of predictive models and clinical decision support systems using EHR	Clinical conditions should be clearly defined for best use of EHR data.
Iterate future EHR structures on the basis of learning from current experience	The structure of current EHR borrows heavily from historical paper records. Future EHR may benefit from different data curation, structures, and analytic systems.
Gaps and challenges	Description
Ensure the accuracy and generalizability of predictive AI tools on the basis of the EHR.	EHR-based AI/ML algorithms can predict cardiovascular disease better than the American College of Cardiology/American Heart Association pooled cohort risk equation, yet more salient analyses may improve their rigor and robustness.
EHR-based AI/ML algorithms may complement randomized clinical trials	It is increasingly difficult and expensive to conduct randomized clinical trials. Robust “real-world trial emulation” may fill the gap between such trials.
Integration of EHR data from diverse electronic systems	EHR systems differ around the world. AI/ML algorithms developed in large national databases, or claims data, are expected to be applicable to diverse health care systems.
Integration of EHR data from different languages	Multilingual EHR may promote diversity, equity, and inclusion, enabling AI/ML algorithms trained with data from underrepresented races and ethnicities to be applied to these groups in the United States.
Ensure that EHR are available to all	Making EHR-based AI/ML algorithms cost feasible, including in remote and underresourced areas, helps avoid exacerbating inequities and perpetuating bias of these algorithms.

AI indicates artificial intelligence; EHRs, electronic health records; and ML, machine learning.