use of Machine learning in risk stratification of patients, predictive models, f

use of Machine learning in risk stratification of patients, predictive models, for providers, insurers, and for patients considerations in the implementation
Providing sufficiently labeled data: Do you have access to sufficiently labeled data?
Constantly changing health care data: Will your algorithm be affected by shifting health care data?
Deployment: Would current systems and APIs (application programming interfaces) need to be adjusted or updated?
Technical pitfalls: What potential pitfalls should be considered when designing and assessing your algorithms?
other considerations: resources, cost, phasing, technical issues, regulatory, legal, liability, ethical, education and training, healthcare and patient knowledge asymmetry
Comments from Customer
Discipline: AI in Healthcare