In a recent webinar with Medcurity, Demi Borden shared insights on the role of AI in strengthening healthcare privacy practices. In today’s patient privacy landscape, where vast amounts of sensitive data flow through multiple systems, the risk of inappropriate access looms large. AI is emerging as a critical tool to manage these challenges, elevating privacy protocols and reducing manual burden.
In this post, we’ll explore the concept of inappropriate access, break it down into Demi’s “3 C’s” (Carelessness, Concern, and Curiosity), and examine how AI is transforming the way healthcare organizations protect patient privacy.
The 3 C’s of Inappropriate Access
1) Carelessness
Carelessness in healthcare settings often leads to accidental breaches of patient privacy. This includes mistakenly sending patient information to the wrong person, incorrect patient details being entered into portals, or adding results to the wrong account. While these errors are usually unintentional, they emphasize the need for ongoing education and reminders. Interventions, such as training and awareness programs, can help minimize these errors by teaching staff how to properly document and handle sensitive information.
2) Concern
This type of access arises when individuals, out of concern for their loved ones or themselves, access personal health information inappropriately. It might include employees looking up results for family members, checking appointments, or accessing their own records within an unauthorized system. Often perceived as harmless, these actions can violate policy and open doors to more serious breaches. To combat this, clear organizational policies and regular training sessions are essential to uphold ethical standards and reinforce proper channels for accessing information.
3) Curiosity
Curiosity-driven access leads to serious HIPAA violations and involves deliberate unauthorized access. Examples include peeking into the records of celebrities or high-profile cases, checking details of coworkers who are unwell, or even as bizarre as screening a potential dates’ health records. These actions are not only intrusive but also compromise patient trust and data security. Organizations must have strict policies and monitoring systems in place to detect and prevent such unauthorized access.
AI’s Role in Shaping Patient Privacy
AI is becoming a powerful tool in helping teams move from a reactive patient privacy program to a proactive one. Instead of spending hours digging through endless access logs, privacy teams can rely on AI to do the heavy lifting like spotting unusual patterns, pulling together summaries, and sending alerts when something looks off. Taking it one step further, patient privacy monitoring solutions that use machine learning can even adapt to each hospital’s unique workflows, learning what “normal” looks like so it’s better at recognizing when something isn’t right. And the more it’s used, the smarter it gets, filtering out false positives and zeroing in on the real risks that need attention.
Final Thoughts
As Demi Borden highlighted during the recent Medcurity webinar, safeguarding patient privacy is no longer just an IT responsibility, it’s a shared priority across the organization. Breaking down inappropriate access into the 3 C’s provides a practical framework to understand and address these risks. By creating awareness and equipping teams with the right tools, hospitals can strengthen their privacy programs and maintain patient trust.
That strength also comes from having the right tools in place. Our patient privacy monitoring solution, Haystack™ iS, uses machine learning to adapt to your hospital’s unique workflows and grow smarter over time. With built-in automation, it takes the busywork off your team’s plate so they can focus on the risks that matter most.
To connect with our patient privacy expert Demi Borden, or to learn how Haystack iS can support your privacy program, contact us below: