What are Explanations?

Explanations align with an organization’s acceptable use policies, since they determine what is an inappropriate vs. an appropriate access. Explanations are validated against documented encounters in the EMR and are enforced within the system when approved. They can be modified to meet an organization’s unique workflow, and new explanations can be created manually. If the system can’t find an explanation for an access, it is labeled unexplained. Unexplained accesses are neutral and require additional input from users to clarify.

There are many reasons why a user may access a patient’s record. Explanations can include encounters, such as a patient having an appointment with a user. They can also include suspicious accesses, such as self-access:

Encounter – Interaction between a user and a patient (appointment, order, etc.). Encounters are documented in the EMR and provide a connection between the accessing user and the patient.

Note: Patient encounters are displayed on the View Full Access page under Patient Encounter Info. This section lists ALL patient encounters (occurring within the date range), along with the user that had the encounter with the patient. This information is documented in the EMR. When doing a manual review the audit team would log in to the EMR to find documented evidence (user was part of the patient’s treatment team). Within EBAS, this determination is automatically made.

Access – Individually logged instance, or audit trail, of a user’s access to a patient’s record (i.e. what a user clicked on).

Note: Access events are represented in Search results, as well as within the View Full Access page under Patient/User Access Details. Access events represent what a user “clicked” on, NOT that they had a clinical or operational encounter with the patient. This is the same information (audit trail) that an audit team sees if they run an audit on a patient or user.

Explanations should align with the organization’s acceptable use policy. It’s important to validate explanations the system found, to ensure they meet your organization’s guidelines.

 

Turning Explanations On and Off:

See a list of explanations the system found in your organization’s data by clicking the gears icon. With Maize-supervised machine-learning, you can turn explanations “on” or “off” to enforce future audits. You can also change which explanations appear as filters vs. flags. Active explanations will be applied to future audits. Inactive explanations will not. Check that active explanations are consistent with your organization’s policies.