Celebrities, public figures, and their family members are especially vulnerable to inappropriate accesses to their medical records, given the public’s interest in their lives and well-being. After experiencing a health crisis or death, VIPs are often thrust into the spotlight where intense media scrutiny occurs. It is their health or insurance provider’s responsibility to ensure these patients’ private medical information is protected. It is often reported that members of the media will try to pay hospital employees to sneak into a VIP’s electronic medical records to obtain private information, giving reporters the inside scoop—a serious breach of the patient’s rights under the HIPAA Privacy Rule.
VIP patients face heightened risk
When it comes to celebrities or local public figures, even small-scale breaches (of just a single record) can cause tremendous harm. Historically, health systems have been fined hundreds of thousands of dollars for improper access to VIP records.
It’s human nature for all of us, including hospital employees, to be curious about all things celebrity-related. But VIPs have the same rights to patient privacy as the rest of us. Traditional compliance and privacy monitoring solutions manually stitch together information from disparate sources, decipher each alert individually, and then export the associated raw log data for manual analysis. Even with traditional patient privacy monitoring products in place, these simple rules engines detect very little, and what they do detect is often a false alarm. What’s needed is the power of clinical context-driven machine learning.
The need to acccount for healthcare's complexities
Why do you need an advanced analytics platform just to protect VIP patients’ data? Why not leverage existing rules-based systems to label VIPs as they enter the hospital or change the names of celebrities while they’re at your institution? Why not just set up a rule with a report-writing team to take care of this?
Traditional rules-based analysis is sufficient for simple scenarios, where every possibility is clearly understood, and it’s always black-and-white to determine whether an access is appropriate or inappropriate. However, as any clinical leader or compliance officer will tell you, that’s far from the case with privacy protection. It is impossible to anticipate every combination of events that can lead to a privacy violation. This problem is compounded with healthcare-based VIP breaches.
In healthcare, the exception is more common than the rule, and complexity increases more and more each day. A rules-based system is like an ever-expanding Rube Goldberg machine — it might just barely get the job done, but it’s costly, prone to failure, and highly inefficient. You have to deal with hundreds of false positives and the system requires lots of “care and feeding,” such as constantly building additional rules using managed services or staff time.
Rule-based systems have an additional obstacle; unless you tell them what to look for, they won’t know that something’s wrong. For example, you could write a rule that says “make sure that no one who works for a department outside of pediatrics looks at pediatric patients.” However, not only will this wrongly flag the appropriate accesses of OB-GYN providers following-up with their postnatal patients, it will also not catch the pediatric surgeon who has no business looking up a friend’s child.
In contrast, Protenus incorporates a deep understanding of the complex clinical environment. The Protenus patient privacy monitoring platform understands the difference between a cardiologist and a research nurse, a diabetes patient and a critically-ill admission, and a surgical ward and an outpatient clinic. It also continually updates its understanding of your unique organization. Protenus understands every single EHR user’s individual patterns of behavior. This important and always-improving context allows hospitals to find malicious user access patterns that might otherwise remain hidden. It also finds perfectly reasonable explanations that might have otherwise taken hours of investigation. Machine learning adapts to your hospital, your patients, your users, and your needs, continuously becoming more effective.
A critically-important element in the case of VIPs is that rule-based systems either have to be provided with a pre-made list of local celebrities, or hospital leadership has to be updated before the celebrity arrives or just as they arrive, in order to manually enter their VIP status. With Protenus’ advanced, machine-learning driven approach (which combines public information with private algorithms), celebrities are identified without intervention or planning. This protects VIPs even when hospitals don’t know their presence or wouldn’t normally think of the person as a “celebrity,” such as a private citizen who was involved in a news-reported accident or act of violence.
A paparazzi-proof approach to patient privacy
In any situation where patients are at an elevated risk, from VIPs to children to behavioral health patients who are more vulnerable to identity theft, Protenus can adjust monitoring thresholds to ensure these individuals are protected. Protenus has helped avert serious incidents relating to VIP access through fast, clear, and contextualized alerts that let compliance officers react and respond immediately.
Elevated risk detection
From the moment a VIP enters a hospital, Protenus’ proprietary algorithms have already determined that they are at risk for breach and require elevated monitoring levels.
News and event monitoring
Based on local news or current events, some people may be at greater risk than others. Instead of relying on manual lists of local celebrities, the platform taps into real-time media and social feeds to help define who is at risk at any given moment.
False positive filtration
When an inappropriate access is detected, Protenus ensures that it is, in fact, a violation. This is accomplished by examining the subtle network relationships between individuals viewing this patient, the clinical context surrounding the access, and the user’s unique historical workflow.
Rapid Visual Forensics
Interactive visualizations and natural language reporting help compliance officers quickly review of all accesses to a VIP’s medical record.