With a single healthcare system averaging 2.5 million EMR accesses a day, it’s safe to say that access control can be an overwhelming task for an organization to manage. Not to mention compliance regulations and the fact that healthcare data is often targeted by bad actors, both internal and external. Access to this highly sensitive information has to be controlled, but how to do that is more complicated than just a username and password.
Why is Controlling Access Important?
While access monitoring is a strong solution to help healthcare organizations understand who is accessing what and why, access still needs to be controlled as it occurs to prevent snooping, HIPAA violations, or even a data breach.
Small-scale medical privacy breaches are on the rise, and there’s not enough privacy monitors to monitor every possible suspicious access. In addition, healthcare organizations often have a large number of internal users accessing sensitive data, so insider threats are plenty.
How Does Artificial Intelligence Help Access Control Systems?
The risks could not be higher and the margin for error, unfortunately, is large, when it comes to EMR access control systems. The current methods of patient privacy monitoring and controlling access are, unfortunately, not enough.
Standard patient privacy monitoring software often utilizes a rules-based system, which results in a high rate of false positives and an inability to understand the context in which an access may have occurred. Not only does this kind of system create a burden on privacy officers as they sort through all the alerts, it misses many accesses that could be suspicious. In fact, many systems only audit 1% of accesses to EMR systems.
Instead of trying to find every access that could potentially be inappropriate, what an artificial intelligence, or machine learning, system does is verify appropriate access through context and pattern learning. This kind of technology better understands why an asset was accessed and can identify and remember patterns in access points to reduce false positives.
Say, for example, if a patient has cancer and the first time they see an oncologist, that oncologist accesses their private patient data. On a rules-based system, that might flag as inappropriate because why is an oncologist accessing data for a patient that, say, is in the ER? It would be flagged and ultimately deemed a false positive. But, with a machine learning system, the system would be able to detect that the patient has cancer, and that oncologists treat cancer, therefore this access is appropriate.
This technology adds access control by determining appropriate or inappropriate access in the moment, allowing for that access to be denied if it’s deemed inappropriate, unlike access monitoring which is mostly reviewed and analyzed retroactively. As this technology is automated, it can also scan more accesses because a new rule doesn’t need to be created for each access. You can jump from a 1% audit rate to a 99% audit rate.
How to Invest in Machine Learning Technology
This technology isn’t hypothetical. SecureLink’s Privacy Monitor utilizes machine learning to help organizations control and monitor access with efficiency and ease. In addition, our team of data scientists is continually figuring out new ways to utilize this technology within the software. One example is our drug detection solutions, so you can detect fraud and prescription abuse within your organization.
Learn more about Privacy Monitor.