These applications also rely on sending a large amount of information to the cloud, which causes a new set of problems. One regards the sensitivity of the information. Sending and storing so much information in the cloud will entail security and privacy challenges. Application developers will have to consider whether the deluge of information they’re sending to the cloud contains personally identifiable information (PII) and whether storing it is in breach of privacy laws. They’ll also have to take the necessary measures to secure the information they store and prevent it from being stolen, or accessed and shared illegally.

The insertion of an algorithm’s predictions into the patient-physician relationship also introduces a third party, turning the relationship into one between the patient and the health care system. It also means significant changes in terms of a patient’s expectation of confidentiality. “Once machine-learning-based decision support is integrated into clinical care, withholding information from electronic records will become increasingly difficult, since patients whose data aren’t recorded can’t benefit from machine-learning analyses,” the authors wrote.

If only 2% – 5% of all faculty and their students (who are doing renewable assignments) were active creators and improvers of OER, that would likely be sufficient.